ɊɈɋɋɂɃɋɄȺə ȺɄȺȾȿɆɂə ɇȺɍɄ ɋɂȻɂɊɋɄɈȿ ɈɌȾȿɅȿɇɂȿ ȼɕɑɂɋɅɂɌȿɅɖɇɕɃ ɐȿɇɌɊ (ɝ. Ʉɪɚɫɧɨɹɪɫɤ)
ɇȿɃɊɈɄɈɆɉɖɘɌȿɊ ɉɊɈȿɄɌ ɋɌȺɇȾȺɊɌȺ
Ɉɬɜɟɬɫɬɜɟɧɧɵɣ ɪɟɞɚɤɬɨɪ ɞɨɤɬɨɪ ɮɢɡɢɤɨ-ɦɚɬɟɦɚɬɢɱɟɫɤɢɯ ɧɚɭɤ ȼ.Ʌ.Ⱦɭɧɢɧ-Ȼɚɪɤɨɜɫɤɢɣ
ɇɨɜɨɫɢɛɢɪɫɤ «ɇɚɭɤɚ» ɋɢɛɢɪɫɤɚɹ ɢɡɞɚɬɟɥɶɫɤɚɹ ɮɢɪɦɚ ɊȺɇ 1998
ɇɟɣɪɨɤɨɦɩɶɸɬɟɪ. ɉɪɨɟɤɬ ɫɬɚɧɞɚɪɬɚ /
ȿ.Ɇ.Ɇɢɪɤɟɫ – ɇɨɜɨɫɢɛɢɪɫɤ: ɇɚɭɤɚ, ɋɢɛɢɪɫɤɚɹ ɢɡɞɚ-
ɬɟɥɶɫɤɚɹ ɮɢɪɦɚ ɊȺɇ, 1998. Ɇɧɨɝɨɥɟɬɧɢɟ ɭɫɢɥɢɹ ɦɧɨɝɢɯ ɢɫɫɥɟɞɨɜɚɬɟɥɶɫɤɢɯ ɝɪɭɩɩ ɩɪɢɜɟɥɢ ɤ ɬɨɦɭ, ɱɬɨ ɤ ɧɚɫɬɨɹɳɟɦɭ ɦɨɦɟɧɬɭ ɧɚɤɨɩɥɟɧɨ ɛɨɥɶɲɨɟ ɱɢɫɥɨ ɪɚɡɥɢɱɧɵɯ «ɩɪɚɜɢɥ ɨɛɭɱɟɧɢɹ» ɢ ɚɪɯɢɬɟɤɬɭɪ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ, ɫɩɨɫɨɛɨɜ ɨɰɟɧɢɜɚɬɶ ɢ ɢɧɬɟɪɩɪɟɬɢɪɨɜɚɬɶ ɢɯ ɪɚɛɨɬɭ, ɩɪɢɟɦɨɜ ɢɫɩɨɥɶɡɨɜɚɧɢɹ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ɞɥɹ ɪɟɲɟɧɢɹ ɩɪɢɤɥɚɞɧɵɯ ɡɚɞɚɱ. ȼ ɤɧɢɝɟ ɩɪɟɞɩɪɢɧɹɬɚ ɩɨɩɵɬɤɚ ɨɩɢɫɚɬɶ ɪɚɡɥɢɱɧɵɟ ɫɟɬɢ, ɚɥɝɨɪɢɬɦɵ ɨɛɭɱɟɧɢɹ ɢ ɞɪɭɝɢɟ ɤɨɦɩɨɧɟɧɬɵ ɢɞɟɚɥɶɧɨɝɨ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚ ɧɚ ɟɞɢɧɨɦ ɹɡɵɤɟ. Ɍɚɤɨɣ ɩɨɞɯɨɞ ɩɪɟɫɥɟɞɭɟɬ ɞɜɟ ɰɟɥɢ. ȼɨ-ɩɟɪɜɵɯ ɫɞɟɥɚɬɶ ɧɟɣɪɨɫɟɬɟɜɵɟ ɩɪɨɝɪɚɦɦɵ ɫɨɜɦɟɫɬɢɦɵɦɢ ɩɨ ɫɩɨɫɨɛɭ ɨɩɢɫɚɧɢɹ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ɢ ɫɨɩɭɬɫɬɜɭɸɳɢɯ ɤɨɦɩɨɧɟɧɬ, ɱɬɨ ɫɢɥɶɧɨ ɭɩɪɨɫɬɢɬ ɠɢɡɧɶ ɩɨɥɶɡɨɜɚɬɟɥɹɦ ɧɟɣɪɨɫɟɬɟɜɵɯ ɩɪɢɥɨɠɟɧɢɣ. ȼɨ-ɜɬɨɪɵɯ ɟɞɢɧɵɣ ɩɨɞɯɨɞ ɤ ɨɩɢɫɚɧɢɸ ɩɨɡɜɨɥɹɟɬ ɤɨɪɪɟɤɬɧɨ ɫɪɚɜɧɢɜɚɬɶ ɦɟɠɞɭ ɫɨɛɨɣ ɪɚɡɥɢɱɧɵɟ ɚɪɯɢɬɟɤɬɭɪɵ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ɢ ɚɥɝɨɪɢɬɦɨɜ ɨɛɭɱɟɧɢɹ. ȼɨɡɦɨɠɧɨɫɬɶ ɫɪɚɜɧɟɧɢɹ, ɜ ɫɜɨɸ ɨɱɟɪɟɞɶ, ɩɨɡɜɨɥɢɬ ɩɪɢɫɬɭɩɢɬɶ ɤ ɩɨɫɬɪɨɟɧɢɸ ɟɞɢɧɨɣ ɬɟɨɪɢɢ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ. Ⱦɥɹ ɫɩɟɰɢɚɥɢɫɬɨɜ ɩɨ ɧɟɣɪɨɢɧɮɨɪɦɚɬɢɤɟ, ɷɤɫɩɟɪɬɧɵɦ ɫɢɫɬɟɦɚɦ, ɪɚɡɪɚɛɨɬɱɢɤɨɜ ɩɪɨɝɪɚɦɦɧɨɝɨ ɨɛɟɫɩɟɱɟɧɢɹ, ɚ ɬɚɤɠɟ ɞɥɹ ɲɢɪɨɤɨɝɨ ɤɪɭɝɚ ɩɨɥɶɡɨɜɚɬɟɥɟɣ, ɢɧɬɟɪɟɫɭɸɳɢɯɫɹ ɧɟɣɪɨɧɧɵɦɢ ɫɟɬɹɦɢ.
ɍɬɜɟɪɠɞɟɧɨ ɤ ɩɟɱɚɬɢ ȼɵɱɢɫɥɢɬɟɥɶɧɵɦ ɰɟɧɬɪɨɦ ɋɈ ɊȺɇ (ɝ. Ʉɪɚɫɧɨɹɪɫɤ)
Ʉɧɢɝɚ ɢɡɞɚɧɚ ɩɪɢ ɮɢɧɚɧɫɨɜɨɣ ɩɨɞɞɟɪɠɤɟ ɋɢɛɢɪɫɤɨɝɨ ɨɬɞɟɥɟɧɢɹ ɊȺɇ, Ʉɪɚɫɧɨɹɪɫɤɨɝɨ ɤɪɚɟɜɨɝɨ ɮɨɧɞɚ ɧɚɭɤɢ ɢ ɁȺɈ «ɋɢɛɢɪɫɤɚɹ Ⱥɭɞɢɬɨɪɫɤɚɹ Ʉɨɦɩɚɧɢɹ»
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ȼɜɟɞɟɧɢɟ Ɇɧɨɝɨɥɟɬɧɢɟ ɭɫɢɥɢɹ ɦɧɨɝɢɯ ɢɫɫɥɟɞɨɜɚɬɟɥɶɫɤɢɯ ɝɪɭɩɩ ɩɪɢɜɟɥɢ ɤ ɬɨɦɭ, ɱɬɨ ɤ ɧɚɫɬɨɹɳɟɦɭ ɦɨɦɟɧɬɭ ɧɚɤɨɩɥɟɧɨ ɛɨɥɶɲɨɟ ɱɢɫɥɨ ɪɚɡɥɢɱɧɵɯ «ɩɪɚɜɢɥ ɨɛɭɱɟɧɢɹ» ɢ ɚɪɯɢɬɟɤɬɭɪ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ, ɫɩɨɫɨɛɨɜ ɨɰɟɧɢɜɚɬɶ ɢ ɢɧɬɟɪɩɪɟɬɢɪɨɜɚɬɶ ɢɯ ɪɚɛɨɬɭ, ɩɪɢɟɦɨɜ ɢɫɩɨɥɶɡɨɜɚɧɢɹ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ɞɥɹ ɪɟɲɟɧɢɹ ɩɪɢɤɥɚɞɧɵɯ ɡɚɞɚɱ. Ⱦɨ ɫɢɯ ɩɨɪ ɷɬɢ ɩɪɚɜɢɥɚ, ɚɪɯɢɬɟɤɬɭɪɵ, ɫɢɫɬɟɦɵ ɨɰɟɧɤɢ ɢ ɢɧɬɟɪɩɪɟɬɚɰɢɢ, ɩɪɢɟɦɵ ɢɫɩɨɥɶɡɨɜɚɧɢɹ ɢ ɞɪɭɝɢɟ ɢɧɬɟɥɥɟɤɬɭɚɥɶɧɵɟ ɧɚɯɨɞɤɢ ɫɭɳɟɫɬɜɭɸɬ ɜ ɜɢɞɟ «ɡɨɨɩɚɪɤɚ» ɫɟɬɟɣ. Ʉɚɠɞɚɹ ɫɟɬɶ ɢɡ ɡɨɨɩɚɪɤɚ ɢɦɟɟɬ ɫɜɨɸ ɚɪɯɢɬɟɤɬɭɪɭ, ɩɪɚɜɢɥɨ ɨɛɭɱɟɧɢɹ ɢ ɪɟɲɚɟɬ ɤɨɧɤɪɟɬɧɵɣ ɧɚɛɨɪ ɡɚɞɚɱ. Ɇɵ ɩɪɟɞɥɚɝɚɟɦ ɫɢɫɬɟɦɚɬɢɡɢɪɨɜɚɬɶ «ɡɨɨɩɚɪɤ». Ⱦɥɹ ɷɬɨɝɨ ɩɨɥɟɡɟɧ ɬɚɤɨɣ ɩɨɞɯɨɞ: ɤɚɠɞɚɹ ɧɟɣɪɨɧɧɚɹ ɫɟɬɶ ɢɡ ɡɨɨɩɚɪɤɚ ɞɨɥɠɧɚ ɛɵɬɶ ɩɪɟɞɫɬɚɜɥɟɧɚ ɤɚɤ ɪɟɚɥɢɡɨɜɚɧɧɚɹ ɧɚ ɢɞɟɚɥɶɧɨɦ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɟ, ɢɦɟɸɳɟɦ ɡɚɞɚɧɧɭɸ ɫɬɪɭɤɬɭɪɭ. ɇɟɫɨɦɧɟɧɧɨ, ɫɬɪɭɤɬɭɪɚ ɷɬɨɝɨ ɢɞɟɚɥɶɧɨɝɨ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚ ɫɨ ɜɪɟɦɟɧɟɦ ɛɭɞɟɬ ɷɜɨɥɸɰɢɨɧɢɪɨɜɚɬɶ. Ɉɞɧɚɤɨ ɩɪɟɢɦɭɳɟɫɬɜɚ ɞɚɠɟ ɨɬ ɩɟɪɜɵɯ ɲɚɝɨɜ ɫɬɚɧɞɚɪɬɢɡɚɰɢɢ ɧɟɫɨɦɧɟɧɧɵ. ȼ ɷɬɨɦ ɧɚɫ ɭɛɟɠɞɚɟɬ ɫɨɛɫɬɜɟɧɧɵɣ ɨɩɵɬ ɜɨɫɶɦɢɥɟɬɧɟɣ ɪɚɛɨɬɵ ɩɨ ɢɫɩɨɥɶɡɨɜɚɧɢɸ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ɜ ɪɚɡɥɢɱɧɵɯ ɡɚɞɚɱɚɯ: ɪɚɫɩɨɡɧɚɜɚɧɢɹ ɨɛɪɚɡɨɜ [64, 290, 285], ɦɟɞɢɰɢɧɫɤɨɣ ɞɢɚɝɧɨɫɬɢɤɢ [18, 49 – 52, 72, 90, 91, 160, 161, 165, 182 – 187, 190 – 208, 255, 295 – 298, 316, 317, 341 – 345, 351, 361], ɩɪɨɝɧɨɡɚ [299 – 301, 364] ɢ ɞɪ. Ƚɪɭɩɩɚ ɇɟɣɪɨɄɨɦɩ ɜ ɬɟɱɟɧɢɟ ɞɜɟɧɚɞɰɚɬɢ ɥɟɬ ɨɬɪɚɛɚɬɵɜɚɥɚ ɩɪɢɧɰɢɩɵ ɨɪɝɚɧɢɡɚɰɢɢ ɧɟɣɪɨɧɧɵɯ ɜɵɱɢɫɥɟɧɢɣ. Ɋɚɡɥɢɱɧɵɟ ɜɚɪɢɚɧɬɵ ɷɬɢɯ ɩɪɢɧɰɢɩɨɜ ɛɵɥɢ ɪɟɚɥɢɡɨɜɚɧɵ ɜ ɫɟɪɢɢ ɩɪɨɝɪɚɦɦɧɟɣɪɨɢɦɢɬɚɬɨɪɨɜ. ȼɨɡɦɨɠɧɨɫɬɶ ɮɨɪɦɢɪɨɜɚɧɢɹ ɛɨɥɶɲɢɧɫɬɜɚ ɚɪɯɢɬɟɤɬɭɪ, ɚɥɝɨɪɢɬɦɨɜ ɢ ɫɩɨɫɨɛɨɜ ɢɫɩɨɥɶɡɨɜɚɧɢɹ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ɧɚ ɨɫɧɨɜɟ ɧɟɛɨɥɶɲɨɝɨ ɱɢɫɥɚ ɫɬɚɧɞɚɪɬɧɵɯ ɛɥɨɤɨɜ ɫɭɳɟɫɬɜɟɧɧɨ ɨɛɥɟɝɱɚɟɬ ɫɨɡɞɚɧɢɟ ɩɪɨɝɪɚɦɦɧɨɝɨ ɨɛɟɫɩɟɱɟɧɢɹ. ȼ ɞɚɧɧɨɣ ɪɚɛɨɬɟ ɨɩɢɫɚɧɚ ɮɭɧɤɰɢɨɧɚɥɶɧɚɹ ɫɬɪɭɤɬɭɪɚ ɢɞɟɚɥɶɧɨɝɨ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚ ɞɥɹ ɪɟɚɥɢɡɚɰɢɢ ɛɨɥɶɲɢɧɫɬɜɚ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ɨɞɧɨɝɨ ɢɡ ɤɪɭɩɧɵɯ ɨɬɞɟɥɨɜ «ɡɨɨɩɚɪɤɚ». Ɋɟɱɶ ɢɞɟɬ ɨ ɫɟɬɹɯ, ɫɜɹɡɚɧɧɵɯ ɫ ɦɟɬɨɞɨɦ ɨɛɪɚɬɧɨɝɨ ɪɚɫɩɪɨɫɬɪɚɧɟɧɢɹ ɨɲɢɛɤɢ - ɷɬɨ ɦɨɳɧɚɹ ɢ ɲɢɪɨɤɨ ɩɪɢɦɟɧɹɟɦɚɹ ɬɟɯɧɨɥɨɝɢɹ ɨɛɭɱɟɧɢɹ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ. Ʉ ɫɨɠɚɥɟɧɢɸ, ɨɧɚ ɩɨɥɭɱɢɥɚ ɪɚɫɩɪɨɫɬɪɚɧɟɧɢɟ ɜ ɜɢɞɟ ɚɥɝɨɪɢɬɦɚ, ɚ ɧɟ ɜ ɜɢɞɟ ɫɩɨɫɨɛɚ ɩɨɫɬɪɨɟɧɢɹ ɚɥɝɨɪɢɬɦɨɜ. Ȼɨɥɟɟ ɨɛɳɚɹ ɬɟɨɪɢɹ ɨɛɭɱɟɧɢɹ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ - ɩɪɢɧɰɢɩ ɞɜɨɣɫɬɜɟɧɧɨɫɬɢ [64, 250, 290, 283] - ɦɚɥɨ ɢɡɜɟɫɬɧɚ. ɇɚ ɞɚɧɧɵɣ ɦɨɦɟɧɬ ɜ ɥɢɬɟɪɚɬɭɪɟ ɜɫɬɪɟɱɚɟɬɫɹ ɨɩɢɫɚɧɢɟ ɛɨɥɟɟ ɱɟɦ ɞɜɭɯ ɞɟɫɹɬɤɨɜ ɪɚɡɥɢɱɧɵɯ ɚɥɝɨɪɢɬɦɨɜ ɨɛɭɱɟɧɢɹ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ɩɨ ɦɟɬɨɞɭ ɨɛɪɚɬɧɨɝɨ ɪɚɫɩɪɨɫɬɪɚɧɟɧɢɹ ɨɲɢɛɤɢ. ɉɪɟɞɥɚɝɚɟɦɵɣ ɜ ɷɬɨɣ ɪɚɛɨɬɟ ɩɪɨɟɤɬ ɫɬɚɧɞɚɪɬɚ ɨɪɢɟɧɬɢɪɨɜɚɧ ɜ ɩɟɪɜɭɸ ɨɱɟɪɟɞɶ ɧɚ ɫɟɬɢ, ɨɛɭɱɚɟɦɵɟ ɩɨ ɦɟɬɨɞɭ ɨɛɪɚɬɧɨɝɨ ɪɚɫɩɪɨɫɬɪɚɧɟɧɢɹ ɨɲɢɛɤɢ, ɧɨ ɜ ɩɪɢɜɟɞɟɧɧɵɯ ɩɪɢɦɟɪɚɯ ɩɨɤɚɡɚɧɚ ɩɪɢɦɟɧɢɦɨɫɬɶ ɷɬɨɝɨ ɫɬɚɧɞɚɪɬɚ ɢ ɞɥɹ ɞɪɭɝɢɯ ɬɢɩɨɜ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ – ɫɟɬɟɣ ɚɫɫɨɰɢɚɬɢɜɧɨɣ ɩɚɦɹɬɢ (ɏɨɩɮɢɥɞ) ɢ ɫɟɬɟɣ, ɨɛɭɱɚɸɳɢɯɫɹ ɛɟɡ ɭɱɢɬɟɥɹ (Ʉɨɯɨɧɟɧ). ɉɨɫɥɟ ɬɳɚɬɟɥɶɧɨɝɨ ɚɧɚɥɢɡɚ ɨɩɢɫɚɧɢɹ ɜɫɟɯ ɞɨɫɬɭɩɧɵɯ ɢɡ ɥɢɬɟɪɚɬɭɪɵ ɚɥɝɨɪɢɬɦɨɜ ɨɛɭɱɟɧɢɹ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ, ɨɩɢɪɚɹɫɶ ɧɚ ɩɪɢɧɰɢɩ ɞɜɨɣɫɬɜɟɧɧɨɫɬɢ ɜ ɨɛɭɱɟɧɢɢ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ɢ ɧɚ ɫɜɨɣ ɞɜɟɧɚɞɰɚɬɢɥɟɬɧɢɣ ɨɩɵɬ, ɧɚɦ ɭɞɚɥɨɫɶ ɫɮɨɪɦɭɥɢɪɨɜɚɬɶ ɩɪɢɧɰɢɩɵ ɫɬɪɭɤɬɭɪɧɨ-ɮɭɧɤɰɢɨɧɚɥɶɧɨɣ ɨɪɝɚɧɢɡɚɰɢɢ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɨɜ. ȼ ɞɚɧɧɨɣ ɪɚɛɨɬɟ ɩɪɟɞɥɚɝɚɟɬɫɹ ɞɜɚ ɭɪɨɜɧɹ ɫɬɚɧɞɚɪɬɢɡɚɰɢɢ. ɉɟɪɜɵɣ ɭɪɨɜɟɧɶ ɫɨɫɬɨɢɬ ɜ ɫɨɡɞɚɧɢɢ ɟɞɢɧɨɝɨ ɹɡɵɤɚ ɨɩɢɫɚɧɢɹ ɮɭɧɤɰɢɨɧɚɥɶɧɵɯ ɤɨɦɩɨɧɟɧɬ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚ. ɉɪɢ ɷɬɨɦ ɧɟ ɜɚɠɧɨ ɤɟɦ ɢ ɞɥɹ ɤɚɤɢɯ ɤɨɦɩɶɸɬɟɪɨɜ ɛɵɥ ɪɚɡɪɚɛɨɬɚɧ ɩɪɨɝɪɚɦɦɧɵɣ ɢɦɢɬɚɬɨɪ. ȼɨɡɦɨɠɧɨɫɬɶ ɢɦɟɬɶ ɜɧɟɲɧɟɟ, ɩɨ ɨɬɧɨɲɟɧɢɸ ɤ ɩɪɨɝɪɚɦɦɧɨɦɭ ɢɦɢɬɚɬɨɪɭ, ɨɩɢɫɚɧɢɟ ɜɫɟɯ ɨɫɧɨɜɧɵɯ ɤɨɦɩɨɧɟɧɬ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚ ɩɪɢɡɜɚɧɚ ɨɛɥɟɝɱɢɬɶ ɪɚɡɪɚɛɨɬɤɭ ɢ ɪɚɫɩɪɨɫɬɪɚɧɟɧɢɟ ɚɪɯɢɬɟɤɬɭɪ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ, ɩɪɚɜɢɥ ɢɧɬɟɪɩɪɟɬɚɰɢɢ ɨɬɜɟɬɨɜ ɢ ɢɯ ɨɰɟɧɤɢ, ɚɥɝɨɪɢɬɦɨɜ ɨɛɭɱɟɧɢɹ, ɦɟɬɨɞɨɜ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɹ (ɫɤɟɥɟɬɨɧɢɡɚɰɢɢ) ɢ ɬ.ɞ. ɉɪɢ ɷɬɨɦ ɪɟɡɭɥɶɬɚɬ ɫɬɚɧɨɜɢɬɫɹ ɧɟ ɡɚɜɢɫɹɳɢɦ ɨɬ ɩɪɨɝɪɚɦɦɵ, ɩɪɢ ɩɨɦɨɳɢ ɤɨɬɨɪɨɣ ɨɧ ɛɵɥ ɩɨɥɭɱɟɧ, ɢ ɜɨɫɩɪɨɢɡɜɨɞɢɦɵɦ ɞɪɭɝɢɦɢ ɢɫɫɥɟɞɨɜɚɬɟɥɹɦɢ. ȼɬɨɪɨɣ ɭɪɨɜɟɧɶ ɩɪɟɞɥɚɝɚɟɦɨɝɨ ɩɪɨɟɤɬɚ ɫɬɚɧɞɚɪɬɚ ɩɪɟɞɭɫɦɚɬɪɢɜɚɟɬ ɜɨɡɦɨɠɧɨɫɬɶ ɜɡɚɢɦɨɡɚɦɟɧɵ ɪɚɡɥɢɱɧɵɯ ɤɨɦɩɨɧɟɧɬ ɜ ɩɪɟɞɟɥɚɯ ɨɞɧɨɣ ɩɪɨɝɪɚɦɦɵ. ɉɪɟɞɩɨɥɚɝɚɟɬɫɹ, ɱɬɨ ɜɨɡɦɨɠɧɨ ɢɫɩɨɥɶɡɨɜɚɧɢɟ ɤɨɦɩɨɧɟɧɬ ɨɞɧɨɝɨ ɪɚɡɪɚɛɨɬɱɢɤɚ ɩɪɨɝɪɚɦɦ ɫɨɜɦɟɫɬɧɨ ɫ ɤɨɦɩɨɧɟɧɬɚɦɢ, ɪɚɡɪɚɛɨɬɚɧɧɵɦɢ ɞɪɭɝɢɦɢ ɪɚɡɪɚɛɨɬɱɢɤɚɦɢ. ɗɬɨɬ ɫɬɚɧɞɚɪɬ ɩɨ ɫɜɨɟɦɭ ɩɪɢɦɟɧɟɧɢɸ ɫɭɳɟɫɬɜɟɧɧɨ ɭɠɟ ɩɟɪɜɨɝɨ, ɩɨɫɤɨɥɶɤɭ ɜɨɡɦɨɠɧɨɫɬɢ ɩɟɪɟɧɨɫɚ ɪɚɡɪɚɛɨɬɨɤ ɦɟɠɞɭ ɪɚɡɥɢɱɧɵɦɢ ɜɵɱɢɫɥɢɬɟɥɶɧɵɦɢ ɩɥɚɬɮɨɪɦɚɦɢ ɫɢɥɶɧɨ ɨɝɪɚɧɢɱɟɧɵ. ɇɟɫɤɨɥɶɤɨ ɫɥɨɜ ɨ ɫɬɪɭɤɬɭɪɟ ɤɧɢɝɢ. ȼ ɩɟɪɜɨɣ ɝɥɚɜɟ ɜɵɞɟɥɹɸɬɫɹ ɨɫɧɨɜɧɵɟ ɤɨɦɩɨɧɟɧɬɵ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚ ɩɨ ɫɥɟɞɭɸɳɢɦ ɩɪɢɡɧɚɤɚɦ. 1. Ɉɬɧɨɫɢɬɟɥɶɧɚɹ ɮɭɧɤɰɢɨɧɚɥɶɧɚɹ ɨɛɨɫɨɛɥɟɧɧɨɫɬɶ: ɤɚɠɞɵɣ ɤɨɦɩɨɧɟɧɬ ɢɦɟɟɬ ɱɟɬɤɢɣ ɧɚɛɨɪ ɮɭɧɤɰɢɣ. ȿɝɨ ɜɡɚɢɦɨɞɟɣɫɬɜɢɟ ɫ ɞɪɭɝɢɦɢ ɤɨɦɩɨɧɟɧɬɚɦɢ ɦɨɠɟɬ ɛɵɬɶ ɨɩɢɫɚɧɨ ɜ ɜɢɞɟ ɧɟɛɨɥɶɲɨɝɨ ɱɢɫɥɚ ɡɚɩɪɨɫɨɜ. 2. ȼɨɡɦɨɠɧɨɫɬɶ ɪɟɚɥɢɡɚɰɢɢ ɛɨɥɶɲɢɧɫɬɜɚ ɢɫɩɨɥɶɡɭɟɦɵɯ ɚɥɝɨɪɢɬɦɨɜ. 3. ȼɨɡɦɨɠɧɨɫɬɶ ɜɡɚɢɦɨɡɚɦɟɧɵ ɪɚɡɥɢɱɧɵɯ ɪɟɚɥɢɡɚɰɢɣ ɥɸɛɨɝɨ ɤɨɦɩɨɧɟɧɬɚ ɛɟɡ ɢɡɦɟɧɟɧɢɹ ɞɪɭɝɢɯ ɤɨɦɩɨɧɟɧɬɨɜ.
CHAP0.DOC
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ȼɨ ɜɬɨɪɨɣ ɝɥɚɜɟ ɨɩɢɫɚɧɵ ɫɬɚɧɞɚɪɬɵ ɬɢɩɨɜ ɞɚɧɧɵɯ ɢ ɨɛɳɢɣ ɛɚɡɢɫ ɹɡɵɤɚ ɨɩɢɫɚɧɢɹ ɪɚɡɥɢɱɧɵɯ ɤɨɦɩɨɧɟɧɬɨɜ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚ. ȼ ɧɟɣ ɬɚɤɠɟ ɩɪɢɜɟɞɟɧɨ ɨɩɢɫɚɧɢɟ ɡɚɩɪɨɫɨɜ, ɢɫɩɨɥɧɹɟɦɵɯ ɜɫɟɦɢ ɢɥɢ ɛɨɥɶɲɢɧɫɬɜɨɦ ɤɨɦɩɨɧɟɧɬ. Ʉɪɨɦɟ ɬɨɝɨ, ɜ ɷɬɨɣ ɝɥɚɜɟ ɩɪɢɜɟɞɟɧɵ ɫɩɨɫɨɛɵ ɪɚɛɨɬɵ ɫ ɧɟɫɬɚɧɞɚɪɬɧɵɦɢ ɬɢɩɚɦɢ ɞɚɧɧɵɯ, ɬɚɤɢɦɢ ɤɚɤ «ɰɜɟɬ» ɩɪɢɦɟɪɚ ɜ ɨɛɭɱɚɸɳɟɣ ɜɵɛɨɪɤɟ ɢ ɞɪ. Ʉɚɠɞɚɹ ɢɡ ɨɫɬɚɥɶɧɵɯ ɝɥɚɜ ɩɨɫɜɹɳɟɧɚ ɨɩɢɫɚɧɢɸ ɨɞɧɨɝɨ ɢɥɢ ɧɟɫɤɨɥɶɤɢɯ ɬɟɫɧɨ ɫɜɹɡɚɧɧɵɯ ɦɟɠɞɭ ɫɨɛɨɣ ɤɨɦɩɨɧɟɧɬɨɜ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚ. Ƚɥɚɜɵ ɮɚɤɬɢɱɟɫɤɢ ɫɚɦɨɫɬɨɹɬɟɥɶɧɵ. ȿɫɥɢ ɜɨɡɧɢɤɚɟɬ ɧɟɨɛɯɨɞɢɦɨɫɬɶ ɩɪɢɜɥɟɱɟɧɢɹ ɦɚɬɟɪɢɚɥɚ ɞɪɭɝɢɯ ɝɥɚɜ, ɬɨ ɞɚɟɬɫɹ ɬɨɱɧɚɹ ɫɫɵɥɤɚ ɧɚ ɪɚɡɞɟɥ, ɜ ɤɨɬɨɪɨɦ ɩɪɢɜɨɞɢɬɫɹ ɧɭɠɧɵɣ ɦɚɬɟɪɢɚɥ. Ʉɚɠɞɚɹ ɢɡ ɷɬɢɯ ɝɥɚɜ, ɫɨɫɬɨɢɬ ɢɡ ɬɪɟɯ ɱɚɫɬɟɣ. ȼ ɩɟɪɜɨɣ ɱɚɫɬɢ ɩɪɢɜɨɞɢɬɫɹ ɨɛɫɭɠɞɟɧɢɟ ɪɚɫɫɦɚɬɪɢɜɚɟɦɨɝɨ ɤɨɦɩɨɧɟɧɬɚ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚ, ɩɪɢɜɨɞɹɬɫɹ ɩɪɢɦɟɪɵ. ȼɨ ɜɬɨɪɨɣ ɱɚɫɬɢ ɝɥɚɜɵ ɨɩɢɫɵɜɚɟɬɫɹ ɩɪɟɞɥɚɝɚɟɦɵɣ ɫɬɚɧɞɚɪɬ ɹɡɵɤɚ ɨɩɢɫɚɧɢɹ ɤɨɦɩɨɧɟɧɬɵ, ɚ ɜ ɬɪɟɬɶɟɣ – ɨɩɢɫɚɧɢɟ ɡɚɩɪɨɫɨɜ, ɢɫɩɨɥɧɹɟɦɵɯ ɷɬɢɦ ɤɨɦɩɨɧɟɧɬɨɦ. Ȼɥɚɝɨɞɚɪɧɨɫɬɢ. ɂɞɟɹ ɧɚɩɢɫɚɧɢɹ ɷɬɨɣ ɤɧɢɝɢ ɪɨɞɢɥɚɫɶ ɧɚ ɨɫɧɨɜɟ ɞɜɟɧɚɞɰɚɬɢɥɟɬɧɟɣ ɪɚɛɨɬɵ Ʉɪɚɫɧɨɹɪɫɤɨɣ ɝɪɭɩɩɵ ɇɟɣɪɨɄɨɦɩ. Ɍɚɤ ɜɵɞɟɥɟɧɢɟ ɮɭɧɤɰɢɨɧɚɥɶɧɵɯ ɤɨɦɩɨɧɟɧɬ ɹɜɢɥɨɫɶ ɪɟɡɭɥɶɬɚɬɨɦ ɪɚɡɪɚɛɨɬɤɢ ɪɹɞɚ ɧɟɣɪɨɫɟɬɟɜɵɯ ɩɪɨɝɪɚɦɦ Ƚɢɥɟɜɵɦ ɋ.ȿ., Ʉɨɱɟɧɨɜɵɦ Ⱦ.Ⱥ., Ɋɨɫɫɢɟɜɵɦ Ⱦ.Ⱥ, ɢ ɚɜɬɨɪɨɦ. Ⱥɜɬɨɪ ɛɥɚɝɨɞɚɪɟɧ Ƚɢɥɟɜɭ ɋ.ȿ, Ⱦɨɪɪɟɪɭ Ɇ.Ƚ., Ʉɨɱɟɧɨɜɭ Ⱦ.Ⱥ., ɇɨɜɨɯɨɞɶɤɨ Ⱥ.ɘ., Ɋɨɫɫɢɟɜɭ Ⱦ.Ⱥ., ɋɢɪɨɬɢɧɢɧɨɣ ɇ.ɘ., ɐɚɪɟɝɨɪɨɞɰɟɜɭ ȼ.Ƚ. ɢ ɑɟɪɬɵɤɨɜɭ ɉ.ȼ. ɡɚ ɧɟɨɞɧɨɤɪɚɬɧɵɟ ɢ ɨɱɟɧɶ ɩɨɥɟɡɧɵɟ ɨɛɫɭɠɞɟɧɢɹ ɩɪɟɞɥɚɝɚɟɦɵɯ ɜ ɤɧɢɝɟ ɫɬɚɧɞɚɪɬɨɜ. Ⱥɜɬɨɪ ɛɥɚɝɨɞɚɪɟɧ ɞɢɪɟɤɬɨɪɭ ɮɢɪɦɵ «ȺɁȺ» ɂ.Ƚ.ɋɭɥɶɤɢɫɭ, ɞɢɪɟɤɬɨɪɭ Ʉɪɚɫɧɨɹɪɫɤɨɝɨ ɜɵɫɲɟɝɨ ɤɨɥɥɟɞɠɚ ɢɧɮɨɪɦɚɬɢɤɢ Ƚ.Ɇ.ɐɢɛɭɥɶɫɤɨɦɭ ɢ ɞɢɪɟɤɬɨɪɭ ɂȼɆ ɋɈ ɊȺɇ ȼ.ȼ.ɒɚɣɞɭɪɨɜɭ ɡɚ ɧɟɨɰɟɧɢɦɭɸ ɨɪɝɚɧɢɡɚɰɢɨɧɧɭɸ ɩɨɞɞɟɪɠɤɭ. Ɉɫɨɛɭɸ ɛɥɚɝɨɞɚɪɧɨɫɬɶ ɚɜɬɨɪ ɜɵɪɚɠɚɟɬ ɫɜɨɟɦɭ ɭɱɢɬɟɥɸ, ɪɭɤɨɜɨɞɢɬɟɥɸ ɝɪɭɩɩɵ ɇɟɣɪɨɄɨɦɩ Ⱥ.ɇ.Ƚɨɪɛɚɧɸ. Ɋɚɛɨɬɚ ɧɚɞ ɤɧɢɝɨɣ ɛɵɥɚ ɩɨɞɞɟɪɠɚɧɚ Ʉɪɚɫɧɨɹɪɫɤɢɦ ɤɪɚɟɜɵɦ ɮɨɧɞɨɦ ɧɚɭɤɢ (ɝɪɚɧɬ ???)
CHAP0.DOC
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1. Ɏɭɧɤɰɢɨɧɚɥɶɧɵɟ ɤɨɦɩɨɧɟɧɬɵ ɗɬɚ ɝɥɚɜɚ ɩɨɫɜɹɳɟɧɚ ɜɵɞɟɥɟɧɢɸ ɮɭɧɤɰɢɨɧɚɥɶɧɵɯ ɤɨɦɩɨɧɟɧɬɨɜ, ɫɨɫɬɚɜɥɹɸɳɢɯ ɭɧɢɜɟɪɫɚɥɶɧɵɣ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪ. Ɉɫɧɨɜɧɵɟ ɤɨɦɩɨɧɟɧɬɵ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚ ɜɵɞɟɥɹɸɬɫɹ ɩɨ ɫɥɟɞɭɸɳɢɦ ɩɪɢɡɧɚɤɚɦ: 1. Ɉɬɧɨɫɢɬɟɥɶɧɚɹ ɮɭɧɤɰɢɨɧɚɥɶɧɚɹ ɨɛɨɫɨɛɥɟɧɧɨɫɬɶ: ɤɚɠɞɵɣ ɤɨɦɩɨɧɟɧɬ ɢɦɟɟɬ ɱɟɬɤɢɣ ɧɚɛɨɪ ɮɭɧɤɰɢɣ. ȿɝɨ ɜɡɚɢɦɨɞɟɣɫɬɜɢɟ ɫ ɞɪɭɝɢɦɢ ɤɨɦɩɨɧɟɧɬɚɦɢ ɦɨɠɟɬ ɛɵɬɶ ɨɩɢɫɚɧɨ ɜ ɜɢɞɟ ɧɟɛɨɥɶɲɨɝɨ ɱɢɫɥɚ ɡɚɩɪɨɫɨɜ. 2. ȼɨɡɦɨɠɧɨɫɬɶ ɪɟɚɥɢɡɚɰɢɢ ɛɨɥɶɲɢɧɫɬɜɚ ɢɫɩɨɥɶɡɭɟɦɵɯ ɚɥɝɨɪɢɬɦɨɜ. 3. ȼɨɡɦɨɠɧɨɫɬɶ ɜɡɚɢɦɨɡɚɦɟɧɵ ɪɚɡɥɢɱɧɵɯ ɪɟɚɥɢɡɚɰɢɣ ɥɸɛɨɝɨ ɤɨɦɩɨɧɟɧɬɚ ɛɟɡ ɢɡɦɟɧɟɧɢɹ ɞɪɭɝɢɯ ɤɨɦɩɨɧɟɧɬɨɜ. Ɉɞɧɚɤɨ, ɩɪɟɠɞɟ ɱɟɦ ɩɪɢɫɬɭɩɚɬɶ ɤ ɜɵɞɟɥɟɧɢɸ ɤɨɦɩɨɧɟɧɬ, ɨɩɢɲɟɦ ɪɚɫɫɦɚɬɪɢɜɚɟɦɵɣ ɧɚɛɨɪ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ɢ ɩɪɨɰɟɫɫ ɢɯ ɨɛɭɱɟɧɢɹ.
1.1 Ʉɪɚɬɤɢɣ ɨɛɡɨɪ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ Ɇɨɠɧɨ ɩɨ ɪɚɡɧɨɦɭ ɨɩɢɫɵɜɚɬɶ «ɡɨɨɩɚɪɤ» ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ. ɉɪɢɜɟɞɟɦ ɤɥɚɫɫɢɮɢɤɚɰɢɸ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ɩɨ ɪɟɲɚɟɦɵɦ ɢɦɢ ɡɚɞɚɱɚɦ. 1. Ʉɥɚɫɫɢɮɢɤɚɰɢɹ ɛɟɡ ɭɱɢɬɟɥɹ ɢɥɢ ɩɨɢɫɤ ɡɚɤɨɧɨɦɟɪɧɨɫɬɟɣ ɜ ɞɚɧɧɵɯ. ɇɚɢɛɨɥɟɟ ɢɡɜɟɫɬɧɵɦ ɩɪɟɞɫɬɚɜɢɬɟɥɟɦ ɷɬɨɝɨ ɤɥɚɫɫɚ ɫɟɬɟɣ ɹɜɥɹɟɬɫɹ ɫɟɬɶ Ʉɨɯɨɧɟɧɚ, ɪɟɚɥɢɡɭɸɳɚɹ ɩɪɨɫɬɟɣɲɢɣ ɜɚɪɢɚɧɬ ɪɟɲɟɧɢɹ ɷɬɨɣ ɡɚɞɚɱɢ. ɇɚɢɛɨɥɟɟ ɨɛɳɢɣ ɜɚɪɢɚɧɬ ɪɟɲɟɧɢɹ ɷɬɨɣ ɡɚɞɚɱɢ ɢɡɜɟɫɬɟɧ ɤɚɤ ɦɟɬɨɞ ɞɢɧɚɦɢɱɟɫɤɢɯ ɹɞɟɪ [223, 261]. 2. Ⱥɫɫɨɰɢɚɬɢɜɧɚɹ ɩɚɦɹɬɶ. ɇɚɢɛɨɥɟɟ ɢɡɜɟɫɬɧɵɣ ɩɪɟɞɫɬɚɜɢɬɟɥɶ – ɫɟɬɢ ɏɨɩɮɢɥɞɚ. ɗɬɚ ɡɚɞɚɱɚ ɬɚɤɠɟ ɩɨɡɜɨɥɹɟɬ ɫɬɪɨɢɬɶ ɨɛɨɛɳɟɧɢɹ. ɇɚɢɛɨɥɟɟ ɨɛɳɢɣ ɜɚɪɢɚɧɬ ɨɩɢɫɚɧ ɜ [77 – 79]. 3. Ⱥɩɩɪɨɤɫɢɦɚɰɢɹ ɮɭɧɤɰɢɣ, ɡɚɞɚɧɧɵɯ ɜ ɤɨɧɟɱɧɨɦ ɱɢɫɥɟ ɬɨɱɟɤ. Ʉ ɫɟɬɹɦ, ɪɟɲɚɸɳɢɦ ɷɬɭ ɡɚɞɚɱɭ, ɨɬɧɨɫɹɬɫɹ ɩɟɪɫɟɩɬɪɨɧɵ, ɫɟɬɢ ɨɛɪɚɬɧɨɝɨ ɪɚɫɩɪɨɫɬɪɚɧɟɧɢɹ. ȼ ɰɟɧɬɪɟ ɧɚɲɟɝɨ ɜɧɢɦɚɧɢɹ ɛɭɞɭɬ ɫɟɬɢ, ɩɪɟɞɧɚɡɧɚɱɟɧɧɵɟ ɞɥɹ ɪɟɲɟɧɢɹ ɬɪɟɬɶɟɣ ɡɚɞɚɱɢ, ɨɞɧɚɤɨ ɩɪɟɞɥɚɝɚɟɦɵɣ ɜɚɪɢɚɧɬ ɫɬɚɧɞɚɪɬɚ ɩɨɡɜɨɥɹɟɬ ɨɩɢɫɚɬɶ ɥɸɛɭɸ ɫɟɬɶ. Ʉɨɧɟɱɧɨ, ɧɟɜɨɡɦɨɠɧɨ ɢɫɩɨɥɶɡɨɜɚɬɶ ɭɱɢɬɟɥɶ, ɩɪɟɞɧɚɡɧɚɱɟɧɧɵɣ ɞɥɹ ɩɨɫɬɪɨɟɧɢɹ ɚɫɫɨɰɢɚɬɢɜɧɨɣ ɩɚɦɹɬɢ, ɞɥɹ ɪɟɲɟɧɢɹ ɡɚɞɚɱɢ ɤɥɚɫɫɢɮɢɤɚɰɢɢ ɛɟɡ ɭɱɢɬɟɥɹ ɢ ɧɚɨɛɨɪɨɬ. ɋɪɟɞɢ ɫɟɬɟɣ, ɚɩɩɪɨɤɫɢɦɢɪɭɸɳɢɯ ɮɭɧɤɰɢɢ, ɧɟɨɛɯɨɞɢɦɨ ɜɵɞɟɥɢɬɶ ɟɳɟ ɞɜɚ ɬɢɩɚ ɫɟɬɟɣ – ɫ ɞɢɮɮɟɪɟɧɰɢɪɭɟɦɨɣ ɢ ɩɨɪɨɝɨɜɨɣ ɯɚɪɚɤɬɟɪɢɫɬɢɱɟɫɤɨɣ ɮɭɧɤɰɢɟɣ. Ⱦɢɮɮɟɪɟɧɰɢɪɭɟɦɨɣ ɛɭɞɟɦ ɧɚɡɵɜɚɬɶ ɫɟɬɶ, ɤɚɠɞɵɣ ɷɥɟɦɟɧɬ ɤɨɬɨɪɨɣ ɪɟɚɥɢɡɭɟɬ ɞɢɮɮɟɪɟɧɰɢɪɭɟɦɭɸ ɮɭɧɤɰɢɸ (ɬɨɱɧɟɟ, ɧɟɩɪɟɪɵɜɧɨ ɞɢɮɮɟɪɟɧɰɢɪɭɟɦɭɸ). ȼɨɨɛɳɟ ɝɨɜɨɪɹ, ɚɥɶɬɟɪɧɚɬɢɜɨɣ ɞɢɮɮɟɪɟɧɰɢɪɭɟɦɨɣ ɫɟɬɢ ɹɜɥɹɟɬɫɹ ɧɟɞɢɮɮɟɪɟɧɰɢɪɭɟɦɚɹ, ɚ ɧɟ ɩɨɪɨɝɨɜɚɹ, ɧɨ ɧɚ ɩɪɚɤɬɢɤɟ, ɤɚɤ ɩɪɚɜɢɥɨ, ɜɫɟ ɧɟɞɢɮɮɟɪɟɧɰɢɪɭɟɦɵɟ ɫɟɬɢ ɹɜɥɹɸɬɫɹ ɩɨɪɨɝɨɜɵɦɢ. Ɉɬɦɟɬɢɦ, ɱɬɨ ɞɥɹ ɬɨɝɨ, ɱɬɨɛɵ ɫɟɬɶ ɛɵɥɚ ɩɨɪɨɝɨɜɨɣ, ɞɨɫɬɚɬɨɱɧɨ ɜɫɬɚɜɢɬɶ ɜ ɧɟɟ ɨɞɢɧ ɩɨɪɨɝɨɜɵɣ ɷɥɟɦɟɧɬ. Ɉɫɧɨɜɧɨɟ ɪɚɡɥɢɱɢɟ ɦɟɠɞɭ ɞɢɮɮɟɪɟɧɰɢɪɭɟɦɵɦɢ ɢ ɩɨɪɨɝɨɜɵɦɢ ɫɟɬɹɦɢ ɫɨɫɬɨɢɬ ɜ ɫɩɨɫɨɛɟ ɨɛɭɱɟɧɢɹ. Ⱦɥɹ ɞɢɮɮɟɪɟɧɰɢɪɭɟɦɵɯ ɫɟɬɟɣ ɟɫɬɶ ɤɨɧɫɬɪɭɤɬɢɜɧɚɹ ɩɪɨɰɟɞɭɪɚ ɨɛɭɱɟɧɢɹ, ɝɚɪɚɧɬɢɪɭɸɳɚɹ ɪɟɡɭɥɶɬɚɬ, ɟɫɥɢ ɨɧ ɞɨɫɬɢɠɢɦ – ɦɟɬɨɞ ɞɜɨɣɫɬɜɟɧɧɨɝɨ ɨɛɭɱɟɧɢɹ (ɨɛɪɚɬɧɨɝɨ ɪɚɫɩɪɨɫɬɪɚɧɟɧɢɹ ɨɲɢɛɤɢ). Ⱦɥɹ ɨɛɭɱɟɧɢɹ ɩɨɪɨɝɨɜɵɯ ɫɟɬɟɣ ɢɫɩɨɥɶɡɭɸɬ ɩɪɚɜɢɥɨ ɏɟɛɛɚ ɢɥɢ ɟɝɨ ɦɨɞɢɮɢɤɚɰɢɢ. Ɉɞɧɚɤɨ, ɞɥɹ ɦɧɨɝɨɫɥɨɣɧɵɯ ɫɟɬɟɣ ɫ ɩɨɪɨɝɨɜɵɦɢ ɷɥɟɦɟɧɬɚɦɢ ɩɪɚɜɢɥɨ ɏɟɛɛɚ ɧɟ ɝɚɪɚɧɬɢɪɭɟɬ ɨɛɭɱɟɧɢɹ. (ȼ ɫɥɭɱɚɟ ɨɞɧɨɫɥɨɣɧɵɯ ɫɟɬɟɣ – ɩɟɪɫɟɩɬɪɨɧɨɜ, ɞɨɤɚɡɚɧɚ ɬɟɨɪɟɦɚ ɨ ɞɨɫɬɢɠɟɧɢɢ ɪɟɡɭɥɶɬɚɬɚ ɜ ɫɥɭɱɚɟ ɟɝɨ ɩɪɢɧɰɢɩɢɚɥɶɧɨɣ ɞɨɫɬɢɠɢɦɨɫɬɢ). ɋ ɞɪɭɝɨɣ ɫɬɨɪɨɧɵ, ɜ ɪɚɛɨɬɟ [145] ɞɨɤɚɡɚɧɨ, ɱɬɨ ɦɧɨɝɨɫɥɨɣɧɵɟ ɫɟɬɢ ɫ ɩɨɪɨɝɨɜɵɦɢ ɧɟɣɪɨɧɚɦɢ ɦɨɠɧɨ ɡɚɦɟɧɢɬɶ ɷɤɜɢɜɚɥɟɧɬɧɵɦɢ ɨɞɧɨɫɥɨɣɧɵɦɢ.
1.2 ȼɵɞɟɥɟɧɢɟ ɤɨɦɩɨɧɟɧɬ ɉɟɪɜɵɦ ɨɫɧɨɜɧɵɦ ɤɨɦɩɨɧɟɧɬɨɦ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚ ɹɜɥɹɟɬɫɹ ɧɟɣɪɨɧɧɚɹ ɫɟɬɶ. Ɉɬɧɨɫɢɬɟɥɶɧɨ ɚɪɯɢɬɟɤɬɭɪɵ ɫɟɬɢ ɩɪɢɧɰɢɩ ɞɜɨɣɫɬɜɟɧɧɨɫɬɢ ɩɪɟɞɩɨɥɚɝɚɟɬ ɬɨɥɶɤɨ ɨɞɧɨ – ɜɫɟ ɷɥɟɦɟɧɬɵ ɫɟɬɢ ɪɟɚɥɢɡɭɸɬ ɩɪɢ 1 ɩɪɹɦɨɦ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɢ ɯɚɪɚɤɬɟɪɢɫɬɢɱɟɫɤɢɟ ɮɭɧɤɰɢɢ ɢɡ ɤɥɚɫɫɚ C E (ɧɟɩɪɟɪɵɜɧɨ ɞɢɮɮɟɪɟɧɰɢ-
( )
ɪɭɟɦɵɟ ɧɚ ɨɛɥɚɫɬɢ ɨɩɪɟɞɟɥɟɧɢɹ E , ɤɨɬɨɪɨɣ, ɤɚɤ ɩɪɚɜɢɥɨ, ɹɜɥɹɟɬɫɹ ɜɫɹ ɱɢɫɥɨɜɚɹ ɨɫɶ). Ⱦɥɹ ɨɛɭɱɟɧɢɹ ɧɟɣɪɨɧɧɨɣ ɫɟɬɢ ɧɟɨɛɯɨɞɢɦɨ ɧɚɥɢɱɢɟ ɡɚɞɚɱɧɢɤɚ. Ɉɞɧɚɤɨ ɱɚɳɟ ɜɫɟɝɨ, ɨɛɭɱɟɧɢɟ ɩɪɨɢɡɜɨɞɢɬɫɹ ɧɟ ɩɨ ɜɫɟɦɭ ɡɚɞɚɱɧɢɤɭ, ɚ ɩɨ ɧɟɤɨɬɨɪɨɣ ɟɝɨ ɱɚɫɬɢ. Ɍɭ ɱɚɫɬɶ ɡɚɞɚɱɧɢɤɚ, ɩɨ ɤɨɬɨɪɨɣ ɜ ɞɚɧɧɵɣ ɦɨɦɟɧɬ ɩɪɨɢɡɜɨɞɢɬɫɹ ɨɛɭɱɟɧɢɟ, ɛɭɞɟɦ ɧɚɡɵɜɚɬɶ ɨɛɭɱɚɸɳɟɣ ɜɵɛɨɪɤɨɣ. Ⱦɥɹ ɦɧɨɝɢɯ ɡɚɞɚɱ ɨɛɭɱɚɸɳɚɹ ɜɵɛɨɪɤɚ ɢɦɟɟɬ ɛɨɥɶɲɢɟ ɪɚɡɦɟɪɵ (ɨɬ ɧɟɫɤɨɥɶɤɢɯ ɫɨɬ ɞɨ ɧɟɫɤɨɥɶɤɢɯ ɞɟɫɹɬɤɨɜ ɬɵɫɹɱ ɩɪɢɦɟɪɨɜ). ɉɪɢ ɨɛɭɱɟɧɢɢ ɫ ɢɫɩɨɥɶɡɨɜɚɧɢɟɦ ɫɤɨɪɨɫɬɧɵɯ ɦɟɬɨɞɨɜ ɨɛɭɱɟɧɢɹ (ɢɯ ɫɤɨɪɨɫɬɶ ɧɚ ɬɪɢ-ɱɟɬɵɪɟ ɩɨɪɹɞɤɚ ɩɪɟɜɵɲɚɟɬ ɫɤɨɪɨɫɬɶ ɨɛɭɱɟɧɢɹ ɩɨ ɤɥɚɫɫɢɱɟɫɤɨɦɭ ɦɟɬɨɞɭ ɨɛɪɚɬɧɨɝɨ ɪɚɫɩɪɨɫɬɪɚɧɟɧɢɹ ɨɲɢɛɤɢ) ɩɪɢɯɨɞɢɬɫɹ ɛɵɫɬɪɨ ɫɦɟɧɹɬɶ ɩɪɢɦɟɪɵ. Ɍɚɤɢɦ ɨɛɪɚɡɨɦ, ɫɤɨɪɨɫɬɶ ɨɛɪɚɛɨɬɤɢ ɨɛɭɱɚɸɳɟɣ ɜɵɛɨɪɤɢ ɦɨɠɟɬ ɫɭɳɟɫɬɜɟɧɧɨ ɜɥɢɹɬɶ ɧɚ ɫɤɨɪɨɫɬɶ ɨɛɭɱɟɧɢɹ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚ. Ʉ ɫɨɠɚɥɟɧɢɸ, ɛɨɥɶɲɢɧɫɬɜɨ ɪɚɡɪɚɛɨɬɱɢɤɨɜ ɚɩɩɚɪɚɬɧɵɯ ɫɪɟɞɫɬɜ
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ɧɟ ɩɪɟɞɭɫɦɚɬɪɢɜɚɟɬ ɫɪɟɞɫɬɜ ɞɥɹ ɛɵɫɬɪɨɣ ɫɦɟɧɵ ɩɪɢɦɟɪɨɜ. Ɍɚɤɢɦ ɨɛɪɚɡɨɦ, ɡɚɞɚɱɧɢɤ ɜɵɞɟɥɟɧ ɜ ɨɬɞɟɥɶɧɵɣ ɤɨɦɩɨɧɟɧɬ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚ. ɉɪɢ ɪɚɛɨɬɟ ɫ ɨɛɭɱɚɸɳɟɣ ɜɵɛɨɪɤɨɣ ɭɞɨɛɧɨ ɢɫɩɨɥɶɡɨɜɚɬɶ ɩɪɢɜɵɱɧɵɣ ɞɥɹ ɩɨɥɶɡɨɜɚɬɟɥɹ ɮɨɪɦɚɬ ɞɚɧɧɵɯ. Ɉɞɧɚɤɨ, ɷɬɨɬ ɮɨɪɦɚɬ ɱɚɳɟ ɜɫɟɝɨ ɧɟɩɪɢɝɨɞɟɧ ɞɥɹ ɢɫɩɨɥɶɡɨɜɚɧɢɹ ɧɟɣɪɨɫɟɬɶɸ. Ɍɚɤɢɦ ɨɛɪɚɡɨɦ, ɦɟɠɞɭ ɨɛɭɱɚɸɳɟɣ ɜɵɛɨɪɤɨɣ ɢ ɧɟɣɪɨɫɟɬɶɸ ɜɨɡɧɢɤɚɟɬ ɞɨɩɨɥɧɢɬɟɥɶɧɵɣ ɤɨɦɩɨɧɟɧɬ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚ – ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ . ɂɡ ɥɢɬɟɪɚɬɭɪɧɵɯ ɢɫɬɨɱɧɢɤɨɜ ɫɥɟɞɭɟɬ, ɱɬɨ ɪɚɡɪɚɛɨɬɤɚ ɷɮɮɟɤɬɢɜɧɵɯ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɨɜ ɞɥɹ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɨɜ ɹɜɥɹɟɬɫɹ ɧɨɜɨɣ, ɩɨɱɬɢ ɫɨɜɫɟɦ ɧɟ ɢɫɫɥɟɞɨɜɚɧɧɨɣ ɨɛɥɚɫɬɶɸ. Ȼɨɥɶɲɢɧɫɬɜɨ ɪɚɡɪɚɛɨɬɱɢɤɨɜ ɩɪɨɝɪɚɦɦɧɨɝɨ ɨɛɟɫɩɟɱɟɧɢɹ ɞɥɹ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɨɜ ɫɤɥɨɧɧɨ ɜɨɡɥɚɝɚɬɶ ɮɭɧɤɰɢɢ ɩɪɟɞɨɛɪɚɛɨɬɤɢ ɜɯɨɞɧɵɯ ɞɚɧɧɵɯ ɧɚ ɨɛɭɱɚɸɳɭɸ ɜɵɛɨɪɤɭ ɢɥɢ ɜɨɨɛɳɟ ɩɟɪɟɤɥɚɞɵɜɚɸɬ ɟɟ ɧɚ ɩɨɥɶɡɨɜɚɬɟɥɹ. ɗɬɨ ɪɟɲɟɧɢɟ ɬɟɯɧɨɥɨɝɢɱɟɫɤɢ ɧɟɜɟɪɧɨ. Ⱦɟɥɨ ɜ ɬɨɦ, ɱɬɨ ɩɪɢ ɩɨɫɬɚɧɨɜɤɟ ɡɚɞɚɱɢ ɞɥɹ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚ ɬɪɭɞɧɨ ɫɪɚɡɭ ɭɝɚɞɚɬɶ ɩɪɚɜɢɥɶɧɵɣ ɫɩɨɫɨɛ ɩɪɟɞɨɛɪɚɛɨɬɤɢ. Ⱦɥɹ ɟɝɨ ɩɨɞɛɨɪɚ ɩɪɨɜɨɞɢɬɫɹ ɫɟɪɢɹ ɷɤɫɩɟɪɢɦɟɧɬɨɜ. ȼ ɤɚɠɞɨɦ ɢɡ ɷɤɫɩɟɪɢɦɟɧɬɨɜ ɢɫɩɨɥɶɡɭɟɬɫɹ ɨɞɧɚ ɢ ɬɚ ɠɟ ɨɛɭɱɚɸɳɚɹ ɜɵɛɨɪɤɚ ɢ ɪɚɡɧɵɟ ɫɩɨɫɨɛɵ ɩɪɟɞɨɛɪɚɛɨɬɤɢ ɜɯɨɞɧɵɯ ɞɚɧɧɵɯ ɫɟɬɢ. Ɍɚɤɢɦ ɨɛɪɚɡɨɦ, ɜɵɞɟɥɟɧ ɬɪɟɬɢɣ ɜɚɠɧɵɣ ɤɨɦɩɨɧɟɧɬ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚ – ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ ɜɯɨɞɧɵɯ ɞɚɧɧɵɯ. Ɂɚɦɟɬɢɦ, ɱɬɨ ɟɫɥɢ ɩɪɢɜɵɱɧɵɣ ɞɥɹ ɱɟɥɨɜɟɤɚ ɫɩɨɫɨɛ ɩɪɟɞɫɬɚɜɥɟɧɢɹ ɜɯɨɞɧɵɯ ɞɚɧɧɵɯ ɧɟɩɪɢɝɨɞɟɧ ɞɥɹ ɧɟɣɪɨɧɧɨɣ ɫɟɬɢ, ɬɨ ɢ ɮɨɪɦɚɬ ɨɬɜɟɬɨɜ ɧɟɣɪɨɧɧɨɣ ɫɟɬɢ ɱɚɫɬɨ ɦɚɥɨɩɪɢɝɨɞɟɧ ɞɥɹ ɱɟɥɨɜɟɤɚ. ɇɟɨɛɯɨɞɢɦɨ ɢɧɬɟɪɩɪɟɬɢɪɨɜɚɬɶ ɨɬɜɟɬɵ ɧɟɣɪɨɧɧɨɣ ɫɟɬɢ. ɂɧɬɟɪɩɪɟɬɚɰɢɹ ɡɚɜɢɫɢɬ ɨɬ ɜɢɞɚ ɨɬɜɟɬɚ. Ɍɚɤ, ɟɫɥɢ ɨɬɜɟɬɨɦ ɧɟɣɪɨɧɧɨɣ ɫɟɬɢ ɹɜɥɹɟɬɫɹ ɞɟɣɫɬɜɢɬɟɥɶɧɨɟ ɱɢɫɥɨ, ɬɨ ɟɝɨ, ɤɚɤ ɩɪɚɜɢɥɨ, ɩɪɢɯɨɞɢɬɫɹ ɦɚɫɲɬɚɛɢɪɨɜɚɬɶ ɢ ɫɞɜɢɝɚɬɶ ɞɥɹ ɩɨɩɚɞɚɧɢɹ ɜ ɧɭɠɧɵɣ ɞɢɚɩɚɡɨɧ ɨɬɜɟɬɨɜ. ȿɫɥɢ ɫɟɬɶ ɢɫɩɨɥɶɡɭɟɬɫɹ ɤɚɤ ɤɥɚɫɫɢɮɢɤɚɬɨɪ, ɬɨ ɜɵɛɨɪ ɢɧɬɟɪɩɪɟɬɚɬɨɪɨɜ ɟɳɟ ɲɢɪɟ. Ȼɨɥɶɲɨɟ ɪɚɡɧɨɨɛɪɚɡɢɟ ɢɧɬɟɪɩɪɟɬɚɬɨɪɨɜ ɩɪɢ ɧɟɜɨɡɦɨɠɧɨɫɬɢ ɪɟɲɢɬɶ ɪɚɡ ɢ ɧɚɜɫɟɝɞɚ ɜɨɩɪɨɫ ɨ ɩɪɟɢɦɭɳɟɫɬɜɚɯ ɨɞɧɨɝɨ ɢɡ ɧɢɯ ɧɚɞ ɞɪɭɝɢɦɢ ɩɪɢɜɨɞɢɬ ɤ ɧɟɨɛɯɨɞɢɦɨɫɬɢ ɜɵɞɟɥɟɧɢɹ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ ɨɬɜɟɬɚ ɧɟɣɪɨɧɧɨɣ ɫɟɬɢ ɜ ɨɬɞɟɥɶɧɵɣ ɤɨɦɩɨɧɟɧɬ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚ. ɋ ɢɧɬɟɪɩɪɟɬɚɬɨɪɨɦ ɨɬɜɟɬɚ ɬɟɫɧɨ ɫɜɹɡɚɧ ɟɳɟ ɨɞɢɧ ɨɛɹɡɚɬɟɥɶɧɵɣ ɤɨɦɩɨɧɟɧɬ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚ – ɨɰɟɧɤɚ. ɇɟɜɧɢɦɚɧɢɟ ɤ ɷɬɨɦɭ ɤɨɦɩɨɧɟɧɬɭ ɜɵɡɜɚɧɨ ɩɪɚɤɬɢɤɨɣ ɪɚɫɫɦɚɬɪɢɜɚɬɶ ɦɟɬɨɞ ɨɛɪɚɬɧɨɝɨ ɪɚɫɩɪɨɫɬɪɚɧɟɧɢɹ ɨɲɢɛɤɢ ɜ ɜɢɞɟ ɚɥɝɨɪɢɬɦɚ. Ⱦɨɦɢɧɢɪɨɜɚɧɢɟ ɬɚɤɨɣ ɬɨɱɤɢ ɡɪɟɧɢɹ ɩɪɢɜɟɥɨ ɤ ɬɨɦɭ, ɱɬɨ, ɫɭɞɹ ɩɨ ɩɭɛɥɢɤɚɰɢɹɦ, ɛɨɥɶɲɢɧɫɬɜɨ ɢɫɫɥɟɞɨɜɚɬɟɥɟɣ ɞɚɠɟ ɧɟ ɩɨɞɨɡɪɟɜɚɟɬ ɨ ɬɨɦ, ɱɬɨ «ɭɤɥɨɧɟɧɢɟ ɨɬ ɩɪɚɜɢɥɶɧɨɝɨ ɨɬɜɟɬɚ», ɩɨɞɚɜɚɟɦɨɟ ɧɚ ɜɯɨɞ ɫɟɬɢ ɩɪɢ ɨɛɪɚɬɧɨɦ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɢ, ɟɫɬɶ ɧɢ ɱɬɨ ɢɧɨɟ, ɤɚɤ ɩɪɨɢɡɜɨɞɧɚɹ ɮɭɧɤɰɢɢ ɨɰɟɧɤɢ ɩɨ ɜɵɯɨɞɧɨɦɭ ɫɢɝɧɚɥɭ ɫɟɬɢ (ɟɫɥɢ ɮɭɧɤɰɢɹ ɨɰɟɧɤɢ ɹɜɥɹɟɬɫɹ ɫɭɦɦɨɣ ɤɜɚɞɪɚɬɨɜ ɭɤɥɨɧɟɧɢɣ). ȼɨɡɦɨɠɧɨ (ɢ ɢɧɨɝɞɚ ɨɱɟɧɶ ɩɨɥɟɡɧɨ) ɤɨɧɫɬɪɭɢɪɨɜɚɬɶ ɞɪɭɝɢɟ ɨɰɟɧɤɢ (ɫɦ. ɝɥɚɜɭ «Ɉɰɟɧɤɚ ɢ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɨɬɜɟɬɚ»). ɇɚɲɟɣ ɝɪɭɩɩɨɣ ɜ ɯɨɞɟ ɱɢɫɥɟɧɧɵɯ ɷɤɫɩɟɪɢɦɟɧɬɨɜ ɛɵɥɨ ɜɵɹɫɧɟɧɨ, ɱɬɨ ɞɥɹ ɨɛɭɱɟɧɢɹ ɫɟɬɟɣɤɥɚɫɫɢɮɢɤɚɬɨɪɨɜ ɮɭɧɤɰɢɹ ɨɰɟɧɤɢ ɜɢɞɚ ɫɭɦɦɵ ɤɜɚɞɪɚɬɨɜ, ɩɨɠɚɥɭɣ, ɧɚɢɛɨɥɟɟ ɩɥɨɯɚ. ɂɫɩɨɥɶɡɨɜɚɧɢɟ ɚɥɶɬɟɪɧɚɬɢɜɧɵɯ ɮɭɧɤɰɢɣ ɨɰɟɧɤɢ ɩɨɡɜɨɥɹɟɬ ɜ ɧɟɫɤɨɥɶɤɨ ɪɚɡ ɭɫɤɨɪɢɬɶ ɨɛɭɱɟɧɢɟ ɧɟɣɪɨɧɧɨɣ ɫɟɬɢ. ɒɟɫɬɵɦ ɧɟɨɛɯɨɞɢɦɵɦ ɤɨɦɩɨɧɟɧɬɨɦ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚ ɹɜɥɹɟɬɫɹ ɭɱɢɬɟɥɶ. ɗɬɨɬ ɤɨɦɩɨɧɟɧɬ ɦɨɠɟɬ ɦɟɬɶ ɦɧɨɠɟɫɬɜɨ ɪɟɚɥɢɡɚɰɢɣ. Ɉɛɡɨɪ ɧɚɢɛɨɥɟɟ ɱɚɫɬɨ ɭɩɨɬɪɟɛɥɹɟɦɵɯ ɢ ɧɚɢɛɨɥɟɟ ɷɮɮɟɤɬɢɜɧɵɯ ɭɱɢɬɟɥɟɣ ɩɪɢɜɨɞɢɬɫɹ ɜ ɝɥɚɜɟ «ɍɱɢɬɟɥɶ». ɉɪɢɧɰɢɩ ɨɬɧɨɫɢɬɟɥɶɧɨɣ ɮɭɧɤɰɢɨɧɚɥɶɧɨɣ ɨɛɨɫɨɛɥɟɧɧɨɫɬɢ ɬɪɟɛɭɟɬ ɜɵɞɟɥɟɧɢɹ ɟɳɟ ɨɞɧɨɝɨ ɤɨɦɩɨɧɟɧɬɚ, ɧɚɡɜɚɧɧɨɝɨ ɢɫɩɨɥɧɢɬɟɥɟɦ ɡɚɩɪɨɫɨɜ ɭɱɢɬɟɥɹ ɢɥɢ ɩɪɨɫɬɨ ɢɫɩɨɥɧɢɬɟɥɟɦ. ɇɚɡɧɚɱɟɧɢɟ ɷɬɨɝɨ ɤɨɦɩɨɧɟɧɬɚ ɧɟ ɬɚɤ ɨɱɟɜɢɞɧɨ, ɤɚɤ ɜɫɟɯ ɩɪɟɞɵɞɭɳɢɯ. Ɂɚɦɟɬɢɦ, ɱɬɨ ɞɥɹ ɜɫɟɯ ɭɱɢɬɟɥɟɣ, ɨɛɭɱɚɸɳɢɯ ɫɟɬɢ ɩɨ ɦɟɬɨɞɭ ɨɛɪɚɬɧɨɝɨ ɪɚɫɩɪɨɫɬɪɚɧɟɧɢɹ ɨɲɢɛɤɢ, ɢ ɩɪɢ ɬɟɫɬɢɪɨɜɚɧɢɢ ɫɟɬɢ ɯɚɪɚɤɬɟɪɟɧ ɫɥɟɞɭɸɳɢɣ ɧɚɛɨɪ ɨɩɟɪɚɰɢɣ ɫ ɤɚɠɞɵɦ ɩɪɢɦɟɪɨɦ ɨɛɭɱɚɸɳɟɣ ɜɵɛɨɪɤɢ: 1. Ɍɟɫɬɢɪɨɜɚɧɢɟ ɪɟɲɟɧɢɹ ɩɪɢɦɟɪɚ 1.1. ȼɡɹɬɶ ɩɪɢɦɟɪ ɭ ɡɚɞɚɱɧɢɤɚ. 1.2. ɉɪɟɞɴɹɜɢɬɶ ɟɝɨ ɫɟɬɢ ɞɥɹ ɪɟɲɟɧɢɹ. 1.3. ɉɪɟɞɴɹɜɢɬɶ ɪɟɡɭɥɶɬɚɬ ɢɧɬɟɪɩɪɟɬɚɬɨɪɭ ɨɬɜɟɬɚ. 2. Ɉɰɟɧɢɜɚɧɢɟ ɪɟɲɟɧɢɹ ɩɪɢɦɟɪɚ 2.1. ȼɡɹɬɶ ɩɪɢɦɟɪ ɭ ɡɚɞɚɱɧɢɤɚ. 2.2. ɉɪɟɞɴɹɜɢɬɶ ɟɝɨ ɫɟɬɢ ɞɥɹ ɪɟɲɟɧɢɹ. 2.3. ɉɪɟɞɴɹɜɢɬɶ ɪɟɡɭɥɶɬɚɬ ɨɰɟɧɤɟ. 3. Ɉɰɟɧɢɜɚɧɢɟ ɪɟɲɟɧɢɹ ɩɪɢɦɟɪɚ ɫ ɜɵɱɢɫɥɟɧɢɟɦ ɝɪɚɞɢɟɧɬɚ. 3.1. ȼɡɹɬɶ ɩɪɢɦɟɪ ɭ ɡɚɞɚɱɧɢɤɚ. 3.2. ɉɪɟɞɴɹɜɢɬɶ ɟɝɨ ɫɟɬɢ ɞɥɹ ɪɟɲɟɧɢɹ. 3.3. ɉɪɟɞɴɹɜɢɬɶ ɪɟɡɭɥɶɬɚɬ ɨɰɟɧɤɟ ɫ ɜɵɱɢɫɥɟɧɢɟɦ ɩɪɨɢɡɜɨɞɧɵɯ. 3.4. ɉɪɟɞɴɹɜɢɬɶ ɪɟɡɭɥɶɬɚɬ ɪɚɛɨɬɵ ɨɰɟɧɤɢ ɫɟɬɢ ɞɥɹ ɜɵɱɢɫɥɟɧɢɹ ɝɪɚɞɢɟɧɬɚ. 4. Ɉɰɟɧɢɜɚɧɢɟ ɢ ɬɟɫɬɢɪɨɜɚɧɢɟ ɪɟɲɟɧɢɹ ɩɪɢɦɟɪɚ. 4.1. ȼɡɹɬɶ ɩɪɢɦɟɪ ɭ ɡɚɞɚɱɧɢɤɚ. 4.2. ɉɪɟɞɴɹɜɢɬɶ ɟɝɨ ɫɟɬɢ ɞɥɹ ɪɟɲɟɧɢɹ. 4.3. ɉɪɟɞɴɹɜɢɬɶ ɪɟɡɭɥɶɬɚɬ ɨɰɟɧɤɟ. 4.4. ɉɪɟɞɴɹɜɢɬɶ ɪɟɡɭɥɶɬɚɬ ɢɧɬɟɪɩɪɟɬɚɬɨɪɭ ɨɬɜɟɬɚ.
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Ɂɚɦɟɬɢɦ, ɱɬɨ ɜɫɟ ɱɟɬɵɪɟ ɜɚɪɢɚɧɬɚ ɪɚɛɨɬɵ ɫ ɫɟɬɶɸ, ɡɚɞɚɱɧɢɤɨɦ, ɢɧɬɟɪɩɪɟɬɚɬɨɪɨɦ ɨɬɜɟɬɚ ɢ ɨɰɟɧɤɨɣ ɥɟɝɤɨ ɨɛɴɟɞɢɧɢɬɶ ɜ ɨɞɢɧ ɡɚɩɪɨɫ, ɩɚɪɚɦɟɬɪɵ ɤɨɬɨɪɨɝɨ ɩɨɡɜɨɥɹɸɬ ɭɤɚɡɚɬɶ ɩɨɫɥɟɞɨɜɚɬɟɥɶɧɨɫɬɶ ɞɟɣɫɬɜɢɣ. Ɍɚɤɢɦ ɨɛɪɚɡɨɦ, ɢɫɩɨɥɧɢɬɟɥɶ ɢɫɩɨɥɧɹɟɬ ɜɫɟɝɨ ɨɞɢɧ ɡɚɩɪɨɫ – ɨɛɪɚɛɨɬɚɬɶ ɩɪɢɦɟɪ. Ɉɞɧɚɤɨ ɜɵɞɟɥɟɧɢɟ ɷɬɨɝɨ ɤɨɦɩɨɧɟɧɬɚ ɩɨɡɜɨɥɹɟɬ ɢɫɤɥɸɱɢɬɶ ɧɟɨɛɯɨɞɢɦɨɫɬɶ ɜ ɩɪɹɦɵɯ ɫɜɹɡɹɯ ɬɚɤɢɯ ɤɨɦɩɨɧɟɧɬɨɜ, ɤɚɤ ɤɨɧɬɪɚɫɬɟɪ ɢ ɭɱɢɬɟɥɶ, ɫ ɤɨɦɩɨɧɟɧɬɚɦɢ ɨɰɟɧɤɚ ɢ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɨɬɜɟɬɚ, ɚ ɢɯ ɜɡɚɢɦɨɞɟɣɫɬɜɢɟ ɫ ɤɨɦɩɨɧɟɧɬɨɦ ɫɟɬɶ ɫɜɟɫɬɢ ɢɫɤɥɸɱɢɬɟɥɶɧɨ ɤ ɡɚɩɪɨɫɚɦ ɫɜɹɡɚɧɧɵɦ ɫ ɦɨɞɢɮɢɤɚɰɢɟɣ ɨɛɭɱɚɟɦɵɯ ɩɚɪɚɦɟɬɪɨɜ ɫɟɬɢ. ɉɨɫɥɟɞɧɢɦ ɤɨɦɩɨɧɟɧɬɨɦ, ɤɨɬɨɪɨɝɨ ɧɟɨɛɯɨɞɢɦɨ ɜɵɞɟɥɢɬɶ, ɹɜɥɹɟɬɫɹ ɤɨɧɬɪɚɫɬɟɪ ɧɟɣɪɨɧɧɨɣ ɫɟɬɢ. ɗɬɨɬ ɤɨɦɩɨɧɟɧɬ ɹɜɥɹɟɬɫɹ ɧɚɞɫɬɪɨɣɤɨɣ ɧɚɞ ɭɱɢɬɟɥɟɦ. ȿɝɨ ɧɚɡɧɚɱɟɧɢɟ – ɫɜɨɞɢɬɶ ɱɢɫɥɨ ɫɜɹɡɟɣ ɫɟɬɢ ɞɨ ɦɢɧɢɦɚɥɶɧɨ ɧɟɨɛɯɨɞɢɦɨɝɨ ɢɥɢ ɞɨ «ɪɚɡɭɦɧɨɝɨ» ɦɢɧɢɦɭɦɚ (ɫɬɟɩɟɧɶ ɪɚɡɭɦɧɨɫɬɢ ɦɢɧɢɦɭɦɚ ɨɩɪɟɞɟɥɹɟɬɫɹ ɩɨɥɶɡɨɜɚɬɟɥɟɦ). Ʉɪɨɦɟ ɬɨɝɨ, ɤɨɧɬɪɚɫɬɟɪ, ɤɚɤ ɩɪɚɜɢɥɨ, ɩɨɡɜɨɥɹɟɬ ɫɜɟɫɬɢ ɦɧɨɠɟɫɬɜɨ ɜɟɥɢɱɢɧ ɜɟɫɨɜ ɫɜɹɡɟɣ ɤ 2-4, ɪɟɠɟ ɤ 8 ɜɵɞɟɥɟɧɧɵɦ ɩɨɥɶɡɨɜɚɬɟɥɟɦ ɡɧɚɱɟɧɢɹɦ. ɇɚɢɛɨɥɟɟ ɜɚɠɧɵɦ ɫɥɟɞɫɬɜɢɟɦ ɩɪɢɦɟɧɟɧɢɹ ɩɪɨɰɟɞɭɪɵ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɹ ɹɜɥɹɟɬɫɹ ɩɨɥɭɱɟɧɢɟ ɥɨɝɢɱɟɫɤɢ ɩɪɨɡɪɚɱɧɵɯ ɫɟɬɟɣ – ɫɟɬɟɣ, ɪɚɛɨɬɭ ɤɨɬɨɪɵɯ ɥɟɝɤɨ ɨɩɢɫɚɬɶ ɢ ɩɨɧɹɬɶ ɧɚ ɹɡɵɤɟ ɥɨɝɢɤɢ [75, 82]. Ⱦɥɹ ɤɨɨɪɞɢɧɚɰɢɢ ɪɚɛɨɬɵ ɜɫɟɯ ɤɨɦɩɨɧɟɧɬ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚ ɜɜɨɞɢɬɫɹ ɦɚɤɪɨɤɨɦɩɨɧɟɧɬɚ ɇɟɣɪɨɤɨɦɩɶɸɬɟɪ . Ɉɫɧɨɜɧɚɹ ɡɚɞɚɱɚ ɷɬɨɝɨ ɤɨɦɩɨɧɟɧɬɚ – ɨɪɝɚɧɢɡɚɰɢɹ ɢɧɬɟɪɮɟɣɫɚ ɫ ɩɨɥɶɡɨɜɚɬɟɥɟɦ ɢ ɤɨɨɪɞɢɧɚɰɢɹ ɞɟɣɫɬɜɢɣ ɜɫɟɯ ɨɫɬɚɥɶɧɵɯ ɤɨɦɩɨɧɟɧɬɨɜ.
1.3 Ɂɚɩɪɨɫɵ ɤɨɦɩɨɧɟɧɬɨɜ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚ ȼ ɷɬɨɦ ɪɚɡɞɟɥɟ ɩɪɢɜɨɞɢɬɫɹ ɨɫɧɨɜɧɨɣ ɫɩɢɫɨɤ ɡɚɩɪɨɫɨɜ, ɤɨɬɨɪɵɟ ɨɛɟɫɩɟɱɢɜɚɸɬ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɟ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚ. Ɂɚ ɪɟɞɤɢɦ ɢɫɤɥɸɱɟɧɢɟɦ ɩɪɢɜɨɞɹɬɫɹ ɬɨɥɶɤɨ ɡɚɩɪɨɫɵ, ɤɨɬɨɪɵɟ ɝɟɧɟɪɢɪɭɸɬɫɹ ɤɨɦɩɨɧɟɧɬɚɦɢ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚ (ɧɟɤɨɬɨɪɵɟ ɢɡ ɷɬɢɯ ɡɚɩɪɨɫɨɜ ɦɨɝɭɬ ɩɨɫɬɭɩɚɬɶ ɜ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪ ɨɬ ɩɨɥɶɡɨɜɚɬɟɥɹ). ɍɱɢɬɟɥɶ Ʉɨɧɬɪɚɫɬɟɪ Ɂɞɟɫɶ ɪɚɫɫɦɚɬɪɢɜɚɟɬɫɹ ɬɨɥɶɤɨ ɮɨɪɦɚ ɡɚɩɪɨɫɚ ɢ ɟɝɨ ɫɦɵɫɥ. ɉɨɥɧɵɣ ɫɩɢɫɨɤ ɡɚɩɪɨɫɨɜ ɤɚɠɞɨɝɨ ɤɨɦɩɨɧɟɧɬɚ, ɞɟɬɚɥɢ ɢɯ ɢɫɩɨɥɧɟɧɢɹ ɢ ɮɨɪɦɚɬɵ ɞɚɧɧɵɯ ɪɚɫɋɟɬɶ Ɂɚɞɚɱɧɢɤ ɂɫɩɨɥɧɢɬɟɥɶ ɫɦɚɬɪɢɜɚɸɬɫɹ ɜ ɫɨɨɬɜɟɬɫɬɜɭɸɳɢɯ ɝɥɚɜɚɯ, ɜ ɪɚɡɞɟɥɚɯ «ɋɬɚɧɞɚɪɬ ɜɬɨɪɨɝɨ ɭɪɨɜɧɹ ɤɨɦɩɨɧɟɧɬɚ ... «. ɇɚ ɪɢɫ. 1. ɩɪɢɜɟɞɟɧɚ ɫɯɟɦɚ ɡɚɂɧɬɟɪɩɪɟɬɚɬɨɪ ɩɪɨɫɨɜ ɜ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɟ. ɉɪɢ ɩɨɈɰɟɧɤɚ ɉɪɟɞɨɛɪɚɛɨɬɱɢɤ ɨɬɜɟɬɚ ɫɬɪɨɟɧɢɢ ɫɯɟɦɵ ɩɪɟɞɩɨɥɚɝɚɟɬɫɹ, ɱɬɨ ɧɚ ɤɚɠɞɵɣ ɡɚɩɪɨɫ ɩɪɢɯɨɞɢɬ ɨɬɜɟɬ. ȼɢɞ ɨɬɜɟɬɚ ɨɩɢɫɚɧ ɩɪɢ ɨɩɢɫɚɧɢɢ ɡɚɩɪɨɫɨɜ. ɋɬɪɟɥɤɢ, ɢɡɨɛɪɚɠɚɸɳɢɟ ɡɚɩɪɨɫɵ, ɢɞɭɬ ɨɬ ɨɛɴɟɤɬɚ, ɢɧɢɰɢɢɪɭɸɳɟɝɨ ɡɚɩɪɨɫ, ɤ ɨɛɴɟɤɬɭ ɟɝɨ ɢɫɩɨɥɧɹɸɳɟɦɭ.
Ɋɢɫ 1. ɋɯɟɦɚ ɡɚɩɪɨɫɨɜ ɜ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɟ
1.3.1 Ɂɚɩɪɨɫɵ ɤ ɡɚɞɚɱɧɢɤɭ Ɂɚɩɪɨɫɵ ɤ ɡɚɞɚɱɧɢɤɭ ɩɨɡɜɨɥɹɸɬ ɩɨɫɥɟɞɨɜɚɬɟɥɶɧɨ ɩɟɪɟɛɢɪɚɬɶ ɜɫɟ ɩɪɢɦɟɪɵ ɨɛɭɱɚɸɳɟɣ ɜɵɛɨɪɤɢ, ɨɛɪɚɳɚɬɶɫɹ ɧɟɩɨɫɪɟɞɫɬɜɟɧɧɨ ɤ ɥɸɛɨɦɭ ɩɪɢɦɟɪɭ ɡɚɞɚɱɧɢɤɚ ɢ ɢɡɦɟɧɹɬɶ ɨɛɭɱɚɸɳɭɸ ɜɵɛɨɪɤɭ. Ɉɛɭɱɚɸɳɚɹ ɜɵɛɨɪɤɚ ɜɵɞɟɥɹɟɬɫɹ ɩɭɬɟɦ «ɪɚɫɤɪɚɲɢɜɚɧɢɹ» ɩɪɢɦɟɪɨɜ ɡɚɞɚɱɧɢɤɚ ɜ ɪɚɡɥɢɱɧɵɟ «ɰɜɟɬɚ». ɉɨɧɹɬɢɟ ɰɜɟɬɚ ɢ ɫɩɨɫɨɛ ɪɚɛɨɬɵ ɫ ɰɜɟɬɚɦɢ ɨɩɢɫɚɧɵ ɜ ɪɚɡɞɟɥɟ «ɉɟɪɟɦɟɧɧɵɟ ɬɢɩɚ ɰɜɟɬ ɢ ɨɩɟɪɚɰɢɢ ɫ ɰɜɟɬɚɦɢ». Ɂɚɩɪɨɫɵ ɩɨɫɥɟɞɨɜɚɬɟɥɶɧɨɝɨ ɩɟɪɟɛɨɪɚ ɨɛɭɱɚɸɳɟɣ ɜɵɛɨɪɤɢ: «ɂɧɢɰɢɢɪɨɜɚɬɶ ɜɵɞɚɱɭ ɩɪɢɦɟɪɨɜ ɰɜɟɬɚ Ʉ». ɉɨ ɷɬɨɦɭ ɡɚɩɪɨɫɭ ɩɪɨɢɫɯɨɞɢɬ ɢɧɢɰɢɚɰɢɹ ɜɵɞɚɱɢ ɩɪɢɦɟɪɨɜ Ʉ-ɝɨ ɰɜɟɬɚ. «Ⱦɚɬɶ ɨɱɟɪɟɞɧɨɣ ɩɪɢɦɟɪ». ɉɨ ɷɬɨɦɭ ɡɚɩɪɨɫɭ ɡɚɞɚɱɧɢɤ ɜɨɡɜɪɚɳɚɟɬ ɩɪɟɞɨɛɪɚɛɨɬɚɧɧɵɟ ɞɚɧɧɵɟ ɨɱɟɪɟɞɧɨɝɨ ɩɪɢɦɟɪɚ ɢ, ɩɪɢ ɧɟɨɛɯɨɞɢɦɨɫɬɢ, ɩɪɚɜɢɥɶɧɵɟ ɨɬɜɟɬɵ, ɭɪɨɜɟɧɶ ɞɨɫɬɨɜɟɪɧɨɫɬɢ ɢ ɞɪɭɝɢɟ ɞɚɧɧɵɟ ɷɬɨɝɨ ɩɪɢɦɟɪɚ. «ɋɥɟɞɭɸɳɢɣ ɩɪɢɦɟɪ». ɉɨ ɷɬɨɦɭ ɡɚɩɪɨɫɭ ɡɚɞɚɱɧɢɤ ɩɟɪɟɯɨɞɢɬ ɤ ɫɥɟɞɭɸɳɟɦɭ ɩɪɢɦɟɪɭ ɨɛɭɱɚɸɳɟɣ ɜɵɛɨɪɤɢ. ȿɫɥɢ ɬɚɤɨɝɨ ɩɪɢɦɟɪɚ ɧɟɬ, ɬɨ ɜɨɡɜɪɚɳɚɟɬɫɹ ɩɪɢɡɧɚɤ ɨɬɫɭɬɫɬɜɢɹ ɨɱɟɪɟɞɧɨɝɨ ɩɪɢɦɟɪɚ. Ⱦɥɹ ɧɟɩɨɫɪɟɞɫɬɜɟɧɧɨɝɨ ɞɨɫɬɭɩɚ ɤ ɩɪɢɦɟɪɚɦ ɡɚɞɚɱɧɢɤɚ ɫɥɭɠɢɬ ɡɚɩɪɨɫ «Ⱦɚɬɶ ɩɪɢɦɟɪ ɧɨɦɟɪ N». Ⱦɟɣɫɬɜɢɹ ɡɚɞɚɱɧɢɤɚ ɜ ɷɬɨɦ ɫɥɭɱɚɟ ɚɧɚɥɨɝɢɱɧɵ ɜɵɩɨɥɧɟɧɢɸ ɡɚɩɪɨɫɚ «Ⱦɚɬɶ ɨɱɟɪɟɞɧɨɣ ɩɪɢɦɟɪ». Ⱦɥɹ ɢɡɦɟɧɟɧɢɹ ɨɛɭɱɚɸɳɟɣ ɜɵɛɨɪɤɢ ɫɥɭɠɢɬ ɡɚɩɪɨɫ «Ɉɤɪɚɫɢɬɶ ɩɪɢɦɟɪɵ ɜ ɰɜɟɬ Ʉ». ɗɬɨɬ ɡɚɩɪɨɫ ɢɫɩɨɥɶɡɭɟɬɫɹ ɪɟɞɤɨ, ɩɨɫɤɨɥɶɤɭ ɢɡɦɟɧɟɧɢɟ ɨɛɭɱɚɸɳɟɣ ɜɵɛɨɪɤɢ, ɤɚɤ ɩɪɚɜɢɥɨ, ɨɫɭɳɟɫɬɜɥɹɟɬɫɹ ɩɨɥɶɡɨɜɚɬɟɥɟɦ ɩɪɢ ɪɟɞɚɤɬɢɪɨɜɚɧɢɢ ɡɚɞɚɱɧɢɤɚ.
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1.3.2 Ɂɚɩɪɨɫ ɤ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɭ ɉɪɟɞɨɛɪɚɛɨɬɱɢɤ ɫɚɦ ɧɢɤɚɤɢɯ ɡɚɩɪɨɫɨɜ ɧɟ ɝɟɧɟɪɢɪɭɟɬ. ȿɞɢɧɫɬɜɟɧɧɵɣ ɡɚɩɪɨɫ ɤ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɭ – «ɉɪɟɞɨɛɪɚɛɨɬɚɬɶ ɩɪɢɦɟɪ» ɦɨɠɟɬ ɛɵɬɶ ɜɵɞɚɧ ɬɨɥɶɤɨ ɡɚɞɚɱɧɢɤɨɦ.
1.3.3 Ɂɚɩɪɨɫ ɤ ɢɫɩɨɥɧɢɬɟɥɸ «Ɉɛɪɚɛɨɬɚɬɶ ɨɱɟɪɟɞɧɨɣ ɩɪɢɦɟɪ». ȼɢɞ ɨɬɜɟɬɚ ɡɚɜɢɫɢɬ ɨɬ ɩɚɪɚɦɟɬɪɨɜ ɡɚɩɪɨɫɚ.
1.3.4 Ɂɚɩɪɨɫɵ ɤ ɭɱɢɬɟɥɸ «ɇɚɱɚɬɶ ɨɛɭɱɟɧɢɟ ɫɟɬɢ». ɉɨ ɷɬɨɦɭ ɡɚɩɪɨɫɭ ɭɱɢɬɟɥɶ ɧɚɱɢɧɚɟɬ ɩɪɨɰɟɫɫ ɨɛɭɱɟɧɢɹ ɫɟɬɢ. «ɉɪɟɪɜɚɬɶ ɨɛɭɱɟɧɢɟ ɫɟɬɢ». ɗɬɨɬ ɡɚɩɪɨɫ ɩɪɢɜɨɞɢɬ ɤ ɩɪɟɤɪɚɳɟɧɢɸ ɩɪɨɰɟɫɫɚ ɨɛɭɱɟɧɢɹ ɫɟɬɢ. ɗɬɨɬ ɡɚɩɪɨɫ ɬɪɟɛɭɟɬɫɹ ɜ ɫɥɭɱɚɟ ɧɟɨɛɯɨɞɢɦɨɫɬɢ ɨɫɬɚɧɨɜɢɬɶ ɨɛɭɱɟɧɢɟ ɫɟɬɢ ɞɨ ɬɨɝɨ, ɤɚɤ ɛɭɞɟɬ ɭɞɨɜɥɟɬɜɨɪɟɧ ɤɪɢɬɟɪɢɣ ɨɫɬɚɧɨɜɤɢ ɨɛɭɱɟɧɢɹ, ɩɪɟɞɭɫɦɨɬɪɟɧɧɵɣ ɜ ɭɱɢɬɟɥɟ. «ɉɪɨɜɟɫɬɢ N ɲɚɝɨɜ ɨɛɭɱɟɧɢɹ» – ɤɚɤ ɩɪɚɜɢɥɨ, ɜɵɞɚɟɬɫɹ ɤɨɧɬɪɚɫɬɟɪɨɦ, ɧɟɨɛɯɨɞɢɦ ɞɥɹ ɧɚɤɨɩɥɟɧɢɹ ɩɨɤɚɡɚɬɟɥɟɣ ɱɭɜɫɬɜɢɬɟɥɶɧɨɫɬɢ.
1.3.5 Ɂɚɩɪɨɫ ɤ ɤɨɧɬɪɚɫɬɟɪɭ «Ɉɬɤɨɧɬɪɚɫɬɢɪɨɜɚɬɶ ɫɟɬɶ». Ɉɬɜɟɬɨɦ ɹɜɥɹɟɬɫɹ ɤɨɞ ɡɚɜɟɪɲɟɧɢɹ ɨɩɟɪɚɰɢɢ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɹ.
1.3.6 Ɂɚɩɪɨɫ ɤ ɨɰɟɧɤɟ Ɉɰɟɧɤɚ ɧɟ ɝɟɧɟɪɢɪɭɟɬ ɧɢɤɚɤɢɯ ɡɚɩɪɨɫɨɜ. Ɉɧɚ ɜɵɩɨɥɧɹɟɬ ɬɨɥɶɤɨ ɨɞɢɧ ɡɚɩɪɨɫ – «Ɉɰɟɧɢɬɶ ɩɪɢɦɟɪ». Ɋɟɡɭɥɶɬɚɬɨɦ ɜɵɩɨɥɧɟɧɢɹ ɡɚɩɪɨɫɚ ɹɜɥɹɟɬɫɹ ɨɰɟɧɤɚ ɩɪɢɦɟɪɚ ɢ, ɩɪɢ ɧɟɨɛɯɨɞɢɦɨɫɬɢ, ɜɟɤɬɨɪ ɩɪɨɢɡɜɨɞɧɵɯ ɨɰɟɧɤɢ ɩɨ ɜɵɯɨɞɧɵɦ ɫɢɝɧɚɥɚɦ ɫɟɬɢ.
1.3.7 Ɂɚɩɪɨɫ ɤ ɢɧɬɟɪɩɪɟɬɚɬɨɪɭ ɨɬɜɟɬɚ ɂɧɬɟɪɩɪɟɬɚɬɨɪ ɨɬɜɟɬɚ ɧɟ ɝɟɧɟɪɢɪɭɟɬ ɧɢɤɚɤɢɯ ɡɚɩɪɨɫɨɜ. Ɉɧ ɜɵɩɨɥɧɹɟɬ ɬɨɥɶɤɨ ɨɞɢɧ ɡɚɩɪɨɫ – «ɂɧɬɟɪɩɪɟɬɢɪɨɜɚɬɶ ɨɬɜɟɬ». Ɉɬɜɟɬɨɦ ɹɜɥɹɟɬɫɹ ɪɟɡɭɥɶɬɚɬ ɢɧɬɟɪɩɪɟɬɚɰɢɢ.
1.3.8 Ɂɚɩɪɨɫɵ ɤ ɫɟɬɢ ɋɟɬɶ ɧɟ ɝɟɧɟɪɢɪɭɟɬ ɧɢɤɚɤɢɯ ɡɚɩɪɨɫɨɜ. ɇɚɛɨɪ ɢɫɩɨɥɧɹɟɦɵɯ ɫɟɬɶɸ ɡɚɩɪɨɫɨɜ ɦɨɠɧɨ ɪɚɡɛɢɬɶ ɧɚ ɬɪɢ ɝɪɭɩɩɵ. Ɂɚɩɪɨɫ, ɨɛɟɫɩɟɱɢɜɚɸɳɢɣ ɬɟɫɬɢɪɨɜɚɧɢɟ. «ɉɪɨɜɟɫɬɢ ɩɪɹɦɨɟ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɟ». ɇɚ ɜɯɨɞ ɫɟɬɢ ɩɨɞɚɸɬɫɹ ɞɚɧɧɵɟ ɩɪɢɦɟɪɚ. ɇɚ ɜɵɯɨɞɟ ɫɟɬɢ ɜɵɱɢɫɥɹɟɬɫɹ ɨɬɜɟɬ ɫɟɬɢ, ɩɨɞɥɟɠɚɳɢɣ ɨɰɟɧɢɜɚɧɢɸ ɢɥɢ ɢɧɬɟɪɩɪɟɬɚɰɢɢ. Ɂɚɩɪɨɫɵ, ɨɛɟɫɩɟɱɢɜɚɸɳɢɟ ɨɛɭɱɟɧɢɟ ɫɟɬɢ. «Ɉɛɧɭɥɢɬɶ ɝɪɚɞɢɟɧɬ». ɉɪɢ ɢɫɩɨɥɧɟɧɢɢ ɷɬɨɝɨ ɡɚɩɪɨɫɚ ɝɪɚɞɢɟɧɬ ɨɰɟɧɤɢ ɩɨ ɨɛɭɱɚɟɦɵɦ ɩɚɪɚɦɟɬɪɚɦ ɫɟɬɢ ɤɥɚɞɟɬɫɹ ɪɚɜɧɵɦ ɧɭɥɸ. ɗɬɨɬ ɡɚɩɪɨɫ ɧɟɨɛɯɨɞɢɦ, ɩɨɫɤɨɥɶɤɭ ɩɪɢ ɜɵɱɢɫɥɟɧɢɢ ɝɪɚɞɢɟɧɬɚ ɩɨ ɨɱɟɪɟɞɧɨɦɭ ɩɪɢɦɟɪɭ ɫɟɬɶ ɞɨɛɚɜɥɹɟɬ ɟɝɨ ɤ ɪɚɧɟɟ ɜɵɱɢɫɥɟɧɧɨɦɭ ɝɪɚɞɢɟɧɬɭ ɩɨ ɫɭɦɦɟ ɞɪɭɝɢɯ ɩɪɢɦɟɪɨɜ. «ȼɵɱɢɫɥɢɬɶ ɝɪɚɞɢɟɧɬ ɩɨ ɩɪɢɦɟɪɭ». ɉɪɨɜɨɞɢɬɫɹ ɨɛɪɚɬɧɨɟ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɟ ɫɟɬɢ. ȼɵɱɢɫɥɟɧɧɵɣ ɝɪɚɞɢɟɧɬ ɞɨɛɚɜɥɹɟɬɫɹ ɤ ɪɚɧɟɟ ɜɵɱɢɫɥɟɧɧɨɦɭ ɝɪɚɞɢɟɧɬɭ ɩɨ ɫɭɦɦɟ ɞɪɭɝɢɯ ɩɪɢɦɟɪɨɜ. «ɂɡɦɟɧɢɬɶ ɤɚɪɬɭ ɫ ɲɚɝɚɦɢ ɇ1 ɢ H2». Ƚɟɧɟɪɢɪɭɟɬɫɹ ɭɱɢɬɟɥɟɦ ɜɨ ɜɪɟɦɹ ɨɛɭɱɟɧɢɹ. Ɂɚɩɪɨɫ, ɨɛɟɫɩɟɱɢɜɚɸɳɢɟ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɟ. «ɂɡɦɟɧɢɬɶ ɤɚɪɬɭ ɩɨ ɨɛɪɚɡɰɭ». Ƚɟɧɟɪɢɪɭɟɬɫɹ ɤɨɧɬɪɚɫɬɟɪɨɦ ɩɪɢ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɢ ɫɟɬɢ. Ɍɚɤɢɦ ɨɛɪɚɡɨɦ, ɜɵɞɟɥɟɧɨ ɫɟɦɶ ɨɫɧɨɜɧɵɯ ɤɨɦɩɨɧɟɧɬ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚ, ɨɩɪɟɞɟɥɟɧɵ ɢɯ ɮɭɧɤɰɢɢ ɢ ɨɫɧɨɜɧɵɟ ɢɫɩɨɥɧɹɟɦɵɟ ɢɦɢ ɡɚɩɪɨɫɵ.
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2. Ɉɛɳɢɣ ɫɬɚɧɞɚɪɬ ɗɬɚ ɝɥɚɜɚ ɩɨɫɜɹɳɟɧɚ ɨɩɢɫɚɧɢɸ ɷɥɟɦɟɧɬɨɜ ɫɬɚɧɞɚɪɬɚ, ɨɛɳɢɯ ɞɥɹ ɜɫɟɯ ɤɨɦɩɨɧɟɧɬɨɜ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚ.
2.1 ɋɬɚɧɞɚɪɬ ɬɢɩɨɜ ɞɚɧɧɵɯ ɉɪɢ ɨɩɢɫɚɧɢɢ ɡɚɩɪɨɫɨɜ, ɫɬɪɭɤɬɭɪ ɞɚɧɧɵɯ, ɫɬɚɧɞɚɪɬɨɜ ɤɨɦɩɨɧɟɧɬɨɜ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚ ɧɟɨɛɯɨɞɢɦɨ ɢɫɩɨɥɶɡɨɜɚɬɶ ɧɚɛɨɪ ɩɟɪɜɢɱɧɵɯ ɬɢɩɨɜ ɞɚɧɧɵɯ. ɉɨɫɤɨɥɶɤɭ ɜ ɪɚɡɧɵɯ ɹɡɵɤɚɯ ɩɪɨɝɪɚɦɦɢɪɨɜɚɧɢɹ ɬɢɩɵ ɞɚɧɧɵɯ ɧɚɡɵɜɚɸɬɫɹ ɩɨ-ɪɚɡɧɨɦɭ, ɜɜɟɞɟɦ ɟɞɢɧɵɣ ɧɚɛɨɪ ɨɛɨɡɧɚɱɟɧɢɣ ɞɥɹ ɧɢɯ. Ɍɚɛɥɢɰɚ 1. Ɍɢɩɵ ɞɚɧɧɵɯ ɞɥɹ ɜɫɟɯ ɤɨɦɩɨɧɟɧɬɨɜ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚ ɌɢɩȾɥɢɧɚɁɧɚɱɟɧɢɹɈɩɢɫɚɧɢɟ Real 4 ɛɚɣɬɚɨɬ 1.5 e- 45 Ⱦɟɣɫɬɜɢɬɟɥɶɧɨɟ ɱɢɫɥɨ. ȼɟɥɢɱɢɧɚ ɢɡ ɭɤɚɡɚɧɧɨɝɨ ɞɢɚɩɚɡɨɞɨ 3.4 e 38 ɧɚ. Ɂɧɚɤ ɩɪɨɢɡɜɨɥɶɧɵɣ. ȼ ɞɚɥɶɧɟɣɲɟɦ ɧɚɡɵɜɚɟɬɫɹ «ɞɟɣɫɬɜɢɬɟɥɶɧɨɟ». Integer 2 ɛɚɣɬɚɨɬ -32768 ɐɟɥɨɟ ɱɢɫɥɨ ɢɡ ɭɤɚɡɚɧɧɨɝɨ ɞɢɚɩɚɡɨɧɚ. ȼ ɞɚɥɶɧɟɣɲɟɦ ɧɚɞɨ 32767 ɡɵɜɚɟɬɫɹ «ɰɟɥɨɟ». Long 4 ɛɚɣɬɚɨɬ -2147483648 ɐɟɥɨɟ ɱɢɫɥɨ ɢɡ ɭɤɚɡɚɧɧɨɝɨ ɞɢɚɩɚɡɨɧɚ. ȼ ɞɚɥɶɧɟɣɲɟɦ ɧɚɞɨ 2147483647 ɡɵɜɚɟɬɫɹ «ɞɥɢɧɧɨɟ ɰɟɥɨɟ». RealArray 4*N ɛɚɣɬɆɚɫɫɢɜ ɞɟɣɫɬɜɢɬɟɥɶɧɵɯ ɱɢɫɟɥ. PRealArray 4 ɛɚɣɬɚɂɫɩɨɥɶɡɭɟɬɫɹ ɞɥɹ ɩɟɪɟɞɚɱɢ ɦɚɫɫɢɜɨɜ ɦɟɠɞɭ ɤɨɦɩɨɧɟɧɬɚɦɢ. ɂɦɟɟɬ ɡɧɚɱɟɧɢɟ ɚɞɪɟɫɚ ɦɚɫɫɢɜɚ ɞɟɣɫɬɜɢɬɟɥɶɧɵɯ ɱɢɫɟɥ. IntegerArray 2*N ɛɚɣɬɆɚɫɫɢɜ ɰɟɥɵɯ ɱɢɫɟɥ. PIntegerArray 4 ɛɚɣɬɚɂɫɩɨɥɶɡɭɟɬɫɹ ɞɥɹ ɩɟɪɟɞɚɱɢ ɦɚɫɫɢɜɨɜ ɦɟɠɞɭ ɤɨɦɩɨɧɟɧɬɚɦɢ. ɂɦɟɟɬ ɡɧɚɱɟɧɢɟ ɚɞɪɟɫɚ ɦɚɫɫɢɜɚ ɰɟɥɵɯ ɱɢɫɟɥ. LongArray 4*N ɛɚɣɬɆɚɫɫɢɜ ɞɥɢɧɧɵɯ ɰɟɥɵɯ ɱɢɫɟɥ. PLongArray 4 ɛɚɣɬɚɂɫɩɨɥɶɡɭɟɬɫɹ ɞɥɹ ɩɟɪɟɞɚɱɢ ɦɚɫɫɢɜɨɜ ɦɟɠɞɭ ɤɨɦɩɨɧɟɧɬɚɦɢ. ɂɦɟɟɬ ɡɧɚɱɟɧɢɟ ɚɞɪɟɫɚ ɦɚɫɫɢɜɚ ɞɥɢɧɧɵɯ ɰɟɥɵɯ ɱɢɫɟɥ. Logic 1 ɛɚɣɬ True, False Ʌɨɝɢɱɟɫɤɚɹ ɜɟɥɢɱɢɧɚ. Ⱦɚɥɟɟ ɧɚɡɵɜɚɟɬɫɹ «ɥɨɝɢɱɟɫɤɚɹ». LogicArray N ɛɚɣɬɆɚɫɫɢɜ ɥɨɝɢɱɟɫɤɢɯ ɩɟɪɟɦɟɧɧɵɯ. PLogicArray 4 ɛɚɣɬɚɂɫɩɨɥɶɡɭɟɬɫɹ ɞɥɹ ɩɟɪɟɞɚɱɢ ɦɚɫɫɢɜɨɜ ɦɟɠɞɭ ɤɨɦɩɨɧɟɧɬɚɦɢ. ɂɦɟɟɬ ɡɧɚɱɟɧɢɟ ɚɞɪɟɫɚ ɦɚɫɫɢɜɚ ɥɨɝɢɱɟɫɤɢɯ ɩɟɪɟɦɟɧɧɵɯ. Color 2 ɛɚɣɬɚɂɫɩɨɥɶɡɭɟɬɫɹ ɞɥɹ ɡɚɞɚɧɢɹ ɰɜɟɬɨɜ. əɜɥɹɟɬɫɹ ɫɨɜɨɤɭɩɧɨɫɬɶɸ ɢɡ 16 ɷɥɟɦɟɧɬɚɪɧɵɯ (ɛɢɬɨɜɵɯ) ɮɥɚɝɨɜ. ɋɦ. ɪɚɡɞɟɥ «ɐɜɟɬ ɢ ɨɩɟɪɚɰɢɢ ɫ ɰɜɟɬɚɦɢ». FuncType 4 ɛɚɣɬɚȺɞɪɟɫ ɮɭɧɤɰɢɢ. ɂɫɩɨɥɶɡɭɟɬɫɹ ɩɪɢ ɧɟɨɛɯɨɞɢɦɨɫɬɢ ɩɟɪɟɞɚɬɶ ɮɭɧɤɰɢɸ ɜ ɤɚɱɟɫɬɜɟ ɚɪɝɭɦɟɧɬɚ. String 256 ɛɚɣɬɋɬɪɨɤɚ ɫɢɦɜɨɥɨɜ. PString 4 ɛɚɣɬɚȺɞɪɟɫ ɫɬɪɨɤɢ ɫɢɦɜɨɥɨɜ. ɋɥɭɠɢɬ ɞɥɹ ɩɟɪɟɞɚɱɢ ɫɬɪɨɤ ɜ ɡɚɩɪɨɫɚɯ Visual 4 ɛɚɣɬɚɈɬɨɛɪɚɠɚɟɦɵɣ ɷɥɟɦɟɧɬ. ɋɥɭɠɢɬ ɞɥɹ ɚɞɪɟɫɚɰɢɢ ɨɬɨɛɪɚɠɚɟɦɵɯ ɷɥɟɦɟɧɬɨɜ ɜ ɢɧɬɟɪɮɟɣɫɧɵɯ ɮɭɧɤɰɢɹɯ. Ɍɢɩ ɡɧɚɱɟɧɢɣ ɡɚɜɢɫɢɬ ɨɬ ɪɟɚɥɢɡɚɰɢɢ ɛɢɛɥɢɨɬɟɤɢ ɢɧɬɟɪɮɟɣɫɧɵɯ ɮɭɧɤɰɢɣ ɢ ɧɟ ɦɨɠɟɬ ɢɡɦɟɧɹɬɶɫɹ ɩɨɥɶɡɨɜɚɬɟɥɟɦ ɢɧɚɱɟ, ɱɟɦ ɱɟɪɟɡ ɜɵɡɨɜ ɢɧɬɟɪɮɟɣɫɧɨɣ ɮɭɧɤɰɢɢ. Pointer 4 ɛɚɣɬɚɇɟ ɬɢɩɢɡɨɜɚɧɧɵɣ ɭɤɚɡɚɬɟɥɶ (ɚɞɪɟɫ). ɗɬɨɬ ɬɢɩ ɫɨɜɦɟɫɬɢɦ ɫ ɥɸɛɵɦ ɬɢɩɢɡɨɜɚɧɧɵɦ ɭɤɚɡɚɬɟɥɹɦ. ɑɢɫɥɨɜɵɟ ɬɢɩɵ ɞɚɧɧɵɯ Integer, Long ɢ Real ɩɪɟɞɧɚɡɧɚɱɟɧɵ ɞɥɹ ɯɪɚɧɟɧɢɹ ɪɚɡɥɢɱɧɵɯ ɱɢɫɟɥ. ɉɟɪɟɦɟɧɧɵɟ ɱɢɫɥɨɜɵɯ ɬɢɩɨɜ ɞɨɩɭɫɤɚɸɬɫɹ ɜ ɹɡɵɤɚɯ ɨɩɢɫɚɧɢɹ ɜɫɟɯ ɤɨɦɩɨɧɟɧɬɨɜ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚ. ɉɪɢ ɧɟɨɛɯɨɞɢɦɨɫɬɢ ɡɚɩɢɫɚɬɶ ɜ ɨɞɢɧ ɦɚɫɫɢɜ ɱɢɫɥɨɜɵɟ ɩɟɪɟɦɟɧɧɵɟ ɪɚɡɥɢɱɧɨɝɨ ɬɢɩɚ ɫɥɟɞɭɟɬ ɢɫɩɨɥɶɡɨɜɚɬɶ ɮɭɧɤɰɢɢ ɩɪɢɜɟɞɟɧɢɹ ɬɢɩɨɜ, ɨɩɢɫɚɧɧɵɟ ɜ ɪɚɡɞɟɥɟ «ɉɪɢɜɟɞɟɧɢɟ ɬɢɩɨɜ» ɋɬɪɨɤɚ. ɋɢɦɜɨɥɶɧɵɣ ɬɢɩ ɞɚɧɧɵɯ ɩɪɟɞɧɚɡɧɚɱɟɧ ɞɥɹ ɯɪɚɧɟɧɢɹ ɤɨɦɦɟɧɬɚɪɢɟɜ, ɧɚɡɜɚɧɢɣ ɩɨɥɟɣ, ɢɦɟɧ ɫɟɬɟɣ, ɨɰɟɧɨɤ ɢ ɞɪɭɝɨɣ ɬɟɤɫɬɨɜɨɣ ɢɧɮɨɪɦɚɰɢɢ. ȼɫɟ ɫɬɪɨɤɨɜɵɟ ɩɟɪɟɦɟɧɧɵɟ ɡɚɧɢɦɚɸɬ 256 ɛɚɣɬ ɢ ɦɨɝɭɬ ɜɤɥɸɱɚɬɶ ɜ ɫɟɛɹ ɞɨ 255 ɫɢɦɜɨɥɨɜ. ɉɟɪɜɵɣ ɛɚɣɬ ɫɬɪɨɤɢ ɫɨɞɟɪɠɢɬ ɞɥɢɧɭ ɫɬɪɨɤɢ. ȼ ɩɟɪɟɦɟɧɧɵɯ ɬɢɩɚ
CHAP2.DOC
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ɫɬɪɨɤɚ ɜɨɡɦɨɠɟɧ ɞɨɫɬɭɩ ɤ ɥɸɛɨɦɭ ɫɢɦɜɨɥɭ ɤɚɤ ɤ ɷɥɟɦɟɧɬɭ ɦɚɫɫɢɜɚ. ɉɪɢ ɷɬɨɦ ɞɥɢɧɚ ɢɦɟɟɬ ɢɧɞɟɤɫ ɧɨɥɶ, ɩɟɪɜɵɣ ɫɢɦɜɨɥ – 1 ɢ ɬ.ɞ. ɍɤɚɡɚɬɟɥɶ ɧɚ ɫɬɪɨɤɭ. ɉɪɢ ɩɟɪɟɞɚɱɟ ɞɚɧɧɵɯ ɦɟɠɞɭ ɤɨɦɩɨɧɟɧɬɚɦɢ ɫɟɬɢ ɢ ɩɪɨɰɟɞɭɪɚɦɢ ɜ ɩɪɟɞɟɥɚɯ ɨɞɧɨɝɨ ɤɨɦɩɨɧɟɧɬɚ ɭɞɨɛɧɨ ɜɦɟɫɬɨ ɫɬɪɨɤɢ ɩɟɪɟɞɚɜɚɬɶ ɭɤɚɡɚɬɟɥɶ ɧɚ ɫɬɪɨɤɭ, ɩɨɫɤɨɥɶɤɭ ɭɤɚɡɚɬɟɥɶ ɡɚɧɢɦɚɟɬ ɜɫɟɝɨ ɱɟɬɵɪɟ ɛɚɣɬɚ. Ⱦɥɹ ɷɬɨɣ ɰɟɥɢ ɫɥɭɠɢɬ ɬɢɩ ɭɤɚɡɚɬɟɥɶ ɧɚ ɫɬɪɨɤɭ. Ʌɨɝɢɱɟɫɤɢɣ ɬɢɩ ɢɫɩɨɥɶɡɭɟɬɫɹ ɞɥɹ ɯɪɚɧɟɧɢɹ ɥɨɝɢɱɟɫɤɢɯ ɡɧɚɱɟɧɢɣ. Ɂɧɚɱɟɧɢɟ ɢɫɬɢɧɚ ɡɚɞɚɟɬɫɹ ɩɪɟɞɨɩɪɟɞɟɥɟɧɧɨɣ ɤɨɧɫɬɚɧɬɨɣ True, ɡɧɚɱɟɧɢɟ ɥɨɠɶ – False. Ɇɚɫɫɢɜɵ. ȼ ɞɚɧɧɨɦ ɫɬɚɧɞɚɪɬɟ ɩɪɟɞɭɫɦɨɬɪɟɧɵ ɦɚɫɫɢɜɵ ɱɟɬɵɪɟɯ ɬɢɩɨɜ – ɥɨɝɢɱɟɫɤɢɯ, ɰɟɥɨɱɢɫɥɟɧɧɵɯ, ɞɥɢɧɧɵɯ ɰɟɥɵɯ ɢ ɞɟɣɫɬɜɢɬɟɥɶɧɵɯ ɩɟɪɟɦɟɧɧɵɯ. Ⱦɥɢɧɵ ɦɚɫɫɢɜɨɜ ɨɩɪɟɞɟɥɹɸɬɫɹ ɩɪɢ ɨɩɢɫɚɧɢɢ, ɧɨ ɜɫɟ ɦɚɫɫɢɜɵ ɩɟɪɟɦɟɧɧɵɯ ɨɞɧɨɝɨ ɬɢɩɚ ɨɬɧɨɫɹɬɫɹ ɤ ɨɞɧɨɦɭ ɬɢɩɭ, ɜ ɨɬɥɢɱɢɟ ɨɬ ɹɡɵɤɨɜ ɬɢɩɚ ɉɚɫɤɚɥɶ. ɂɫɩɨɥɶɡɨɜɚɧɢɟ ɮɭɧɤɰɢɣ ɩɪɢɜɟɞɟɧɢɹ ɢ ɩɪɟɨɛɪɚɡɨɜɚɧɢɹ ɬɢɩɨɜ ɩɨɡɜɨɥɹɸɬ ɩɨɥɭɱɚɬɶ ɢɡ ɷɬɢɯ ɦɚɫɫɢɜɨɜ ɫɬɪɭɤɬɭɪɵ ɩɪɨɢɡɜɨɥɶɧɨɣ ɫɥɨɠɧɨɫɬɢ. ɗɥɟɦɟɧɬɵ ɦɚɫɫɢɜɨɜ ɜɫɟɝɞɚ ɧɭɦɟɪɭɸɬɫɹ ɫ ɟɞɢɧɢɰɵ. ȼɧɟ ɡɚɜɢɫɢɦɨɫɬɢ ɨɬ ɬɢɩɚ ɦɚɫɫɢɜɚ ɧɭɥɟɜɨɣ ɷɥɟɦɟɧɬ ɦɚɫɫɢɜɚ ɢɦɟɟɬ ɬɢɩ Long ɢ ɫɨɞɟɪɠɢɬ ɞɥɢɧɭ ɦɚɫɫɢɜɚ ɜ ɷɥɟɦɟɧɬɚɯ. ɇɚ ɪɢɫ. 1 ɩɪɢɜɟɞɟɧɚ ɫɯɟɦɚ ɪɚɫɩɪɟɞɟɥɟɧɢɹ ɩɚɦɹɬɢ ɜɫɟɯ ɬɢɩɨɜ ɦɚɫɫɢɜɨɜ, ɤɚɠɞɵɣ ɢɡ ɤɨɬɨɪɵɯ ɫɨɞɟɪɠɢɬ ɲɟɫɬɶ ɷɥɟɦɟɧɬɨɜ.
ɚ) RealArray ɋɨɞɟɪɠɢɦɨɟ ɇɨɦɟɪɚ ɛɚɣɬ
03 01
00 02
00 03
00 04
01 05
Ⱥɞɪɟɫ ɦɚɫɫɢɜɚȺɞ
01 06
01 07
01 08
02 09
02 10
02 11
02 12
03 13
03 14
03 15
03 16
02 10
02 11
02 12
03 13
03 14
03 15
03 16
ɪɟɫ ɩɟɪɜɨɝɨ ɷɥɟɦɟɧɬɚ
ɛ) LongArray ɋɨɞɟɪɠɢɦɨɟ ɇɨɦɟɪɚ ɛɚɣɬ
03 01
00 02
00 03
00 04
Ⱥɞɪɟɫ ɦɚɫɫɢɜɚȺɞ
01 05
01 06
01 07
01 08
02 09
ɪɟɫ ɩɟɪɜɨɝɨ ɷɥɟɦɟɧɬɚ
ɜ) IntegerArray ɋɨɞɟɪɠɢɦɨɟ ɇɨɦɟɪɚ ɛɚɣɬ
03 01
00 02
00 03
00 04
Ⱥɞɪɟɫ ɦɚɫɫɢɜɚ ɝ) LogicArray ɋɨɞɟɪɠɢɦɨɟ ɇɨɦɟɪɚ ɛɚɣɬ
03 01
00 02
00 03
Ⱥɞɪɟɫ ɦɚɫɫɢɜɚ
00 04
01 01 02 02 03 03 05 06 07 08 09 10 Ⱥɞɪɟɫ ɩɟɪɜɨɝɨ ɷɥɟɦɟɧɬɚ Ɋɢɫ. 1. ɉɪɢɦɟɪ ɪɚɫɩɪɟɞɟɥɟɧɢɹ ɩɚɦɹɬɢ ɞɥɹ ɱɟɬɵɪɟɯ ɜɢɞɨɜ ɦɚɫɫɢɜɨɜ ɢɡ ɬɪɟɯ ɷɥɟɦɟɧɬɨɜ. 01 02 03 ɚ) Ɇɚɫɫɢɜ ɞɟɣɫɬɜɢɬɟɥɶɧɵɯ ɱɢɫɟɥ, ɡɚɧɢɦɚɟɬ 16 ɛɚɣɬ 05 06 07 ɛ) Ɇɚɫɫɢɜ ɞɥɢɧɧɵɯ ɰɟɥɵɯ ɱɢɫɟɥ, ɡɚɧɢɦɚɟɬ 16 ɛɚɣɬ Ⱥɞɪɟɫ ɩɟɪɜɨɝɨ ɜ) Ɇɚɫɫɢɜ ɰɟɥɵɯ ɱɢɫɟɥ, ɡɚɧɢɦɚɟɬ 10 ɛɚɣɬ ɷɥɟɦɟɧɬɚ ɝ) Ɇɚɫɫɢɜ ɥɨɝɢɱɟɫɤɢɯ ɜɟɥɢɱɢɧ, ɡɚɧɢɦɚɟɬ 7 ɛɚɣɬ
ȼɫɟ ɦɚɫɫɢɜɵ, ɤɚɤ ɩɪɚɜɢɥɨ, ɢɫɩɨɥɶɡɭɟɬɫɹ ɬɨɥɶɤɨ ɜ ɩɪɟɞɟɥɚɯ ɨɞɧɨɝɨ ɤɨɦɩɨɧɟɧɬɚ. ɉɪɢ ɩɟɪɟɞɚɱɟ ɦɚɫɫɢɜɨɜ ɦɟɠɞɭ ɤɨɦɩɨɧɟɧɬɚɦɢ ɢɥɢ ɦɟɠɞɭ ɩɪɨɰɟɞɭɪɚɦɢ ɜ ɩɪɟɞɟɥɚɯ ɨɞɧɨɝɨ ɤɨɦɩɨɧɟɧɬɚ ɢɫɩɨɥɶɡɭɸɬɫɹ ɭɤɚɡɚɬɟɥɢ ɧɚ ɦɚɫɫɢɜɵ. Ⱥɞɪɟɫ ɮɭɧɤɰɢɢ. ɗɬɨɬ ɬɢɩ ɢɫɩɨɥɶɡɭɟɬɫɹ ɞɥɹ ɩɟɪɟɞɚɱɢ ɮɭɧɤɰɢɢ ɜ ɤɚɱɟɫɬɜɟ ɚɪɝɭɦɟɧɬɚ. ɉɟɪɟɦɟɧɧɚɹ ɬɢɩɚ FuncType ɡɚɧɢɦɚɟɬ ɱɟɬɵɪɟ ɛɚɣɬɚ ɢ ɹɜɥɹɟɬɫɹ ɚɞɪɟɫɨɦ ɮɭɧɤɰɢɢ. ȼ ɡɚɜɢɫɢɦɨɫɬɢ ɨɬ ɪɟɚɥɢɡɚɰɢɢ ɩɨ ɷɬɨɦɭ ɚɞɪɟɫɭ ɦɨɠɟɬ ɥɟɠɚɬɶ ɥɢɛɨ ɧɚɱɚɥɨ ɦɚɲɢɧɧɨɝɨ ɤɨɞɚ ɮɭɧɤɰɢɢ, ɥɢɛɨ ɧɚɱɚɥɨ ɬɟɤɫɬɚ ɮɭɧɤɰɢɢ. ȼ ɫɥɭɱɚɟ ɩɟɪɟɞɚɱɢ ɬɟɤɫɬɚ ɮɭɧɤɰɢɢ ɩɟɪɜɵɟ ɜɨɫɟɦɶ ɛɚɣɬ ɩɨ ɩɟɪɟɞɚɧɧɨɦɭ ɚɞɪɟɫɭ ɫɨɞɟɪɠɚɬ ɫɥɨɜɨ «Function». Ɉɬɨɛɪɚɠɚɟɦɵɣ ɷɥɟɦɟɧɬ. ɉɟɪɟɦɟɧɧɵɟ ɬɢɩɚ Visual (ɨɬɨɛɪɚɠɚɟɦɵɣ ɷɥɟɦɟɧɬ) ɋɥɭɠɚɬ ɞɥɹ ɚɞɪɟɫɚɰɢɢ ɨɬɨɛɪɚɠɚɟɦɵɯ ɷɥɟɦɟɧɬɨɜ ɜ ɢɧɬɟɪɮɟɣɫɧɵɯ ɮɭɧɤɰɢɹɯ. Ɍɢɩ ɡɧɚɱɟɧɢɣ ɡɚɜɢɫɢɬ ɨɬ ɪɟɚɥɢɡɚɰɢɢ ɛɢɛɥɢɨɬɟɤɢ ɢɧɬɟɪɮɟɣɫɧɵɯ ɮɭɧɤɰɢɣ ɢ ɧɟ ɦɨɠɟɬ ɢɡɦɟɧɹɬɶɫɹ ɩɨɥɶɡɨɜɚɬɟɥɟɦ ɢɧɚɱɟ, ɱɟɦ ɱɟɪɟɡ ɜɵɡɨɜ ɢɧɬɟɪɮɟɣɫɧɨɣ ɮɭɧɤɰɢɢ. Ɉɫɨɛɨ ɫɥɟɞɭɟɬ ɨɬɦɟɬɢɬɶ, ɱɬɨ ɛɢɛɥɢɨɬɟɤɚ ɢɧɬɟɪɮɟɣɫɧɵɯ ɮɭɧɤɰɢɣ ɧɟ ɹɜɥɹɟɬɫɹ ɱɚɫɬɶɸ ɧɢ ɨɞɧɨɝɨ ɢɡ ɤɨɦɩɨɧɟɧɬɨɜ.
2.2 ɉɟɪɟɦɟɧɧɵɟ ɬɢɩɚ ɰɜɟɬ ɢ ɨɩɟɪɚɰɢɢ ɫ ɰɜɟɬɚɦɢ ɂɫɩɨɥɶɡɨɜɚɧɢɟ ɰɜɟɬɨɜ ɩɨɡɜɨɥɹɟɬ ɝɢɛɤɨ ɪɚɡɛɢɜɚɬɶ ɦɧɨɠɟɫɬɜɚ ɧɚ ɩɨɞɦɧɨɠɟɫɬɜɚ. ȼ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɟ ɜɨɡɧɢɤɚɟɬ ɧɟɨɛɯɨɞɢɦɨɫɬɶ ɜ ɪɚɡɛɢɟɧɢɢ ɧɚ ɩɨɞɦɧɨɠɟɫɬɜɚ (ɪɚɫɤɪɚɲɢɜɚɧɢɢ) ɡɚɞɚɱɧɢɤɚ. ȼ ɷɬɨɦ ɪɚɡɞɟɥɟ ɨɩɢɫɵɜɚɟɬɫɹ ɫɬɚɧɞɚɪɬ ɪɚɛɨɬɵ ɫ ɩɟɪɟɦɟɧɧɵɦɢ ɬɢɩɚ ɰɜɟɬ.
CHAP2.DOC
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2.2.1 Ɂɧɚɱɟɧɢɟ ɩɟɪɟɦɟɧɧɨɣ ɬɢɩɚ ɰɜɟɬ (Color) ɉɟɪɟɦɟɧɧɚɹ ɬɢɩɚ ɰɜɟɬ ɩɪɟɞɫɬɚɜɥɹɟɬ ɫɨɛɨɣ ɞɜɭɯɛɚɣɬɨɜɨɟ ɛɟɡɡɧɚɤɨɜɨɟ ɰɟɥɨɟ. Ɉɞɧɚɤɨ ɨɫɧɨɜɧɨɟ ɢɫɩɨɥɶɡɨɜɚɧɢɟ ɩɪɟɞɩɨɥɚɝɚɟɬ ɪɚɛɨɬɭ ɧɟ ɤɚɤ ɫ ɰɟɥɵɦ ɱɢɫɥɨɦ, ɚ ɤɚɤ ɫ ɫɨɜɨɤɭɩɧɨɫɬɶɸ ɨɞɧɨɛɢɬɧɵɯ ɮɥɚɝɨɜ. ɉɪɢ ɡɚɩɢɫɢ ɧɚ ɞɢɫɤ ɢɫɩɨɥɶɡɭɟɬɫɹ ɫɢɦɜɨɥɶɧɨɟ ɩɪɟɞɫɬɚɜɥɟɧɢɟ ɞɜɨɢɱɧɨɣ ɡɚɩɢɫɢ ɱɢɫɥɚ ɫ ɜɟɞɭɳɢɦɢ ɧɭɥɹɦɢ ɢ ɪɚɡɛɢɟɧɢɟɦ ɧɚ ɱɟɬɜɟɪɤɢ ɫɢɦɜɨɥɨɦ «.» (ɬɨɱɤɚ), ɩɪɟɞɜɚɪɹɟɦɚɹ ɡɚɝɥɚɜɧɨɣ ɛɭɤɜɨɣ «B» ɥɚɬɢɧɫɤɨɝɨ ɚɥɮɚɜɢɬɚ, ɢɥɢ ɫɢɦɜɨɥɶɧɨɟ ɩɪɟɞɫɬɚɜɥɟɌɚɛɥɢɰɚ 2 ɧɢɟ ɲɟɫɬɧɚɞɰɚɬɟɪɢɱɧɨɣ ɇɭɦɟɪɚɰɢɹ ɮɥɚɝɨɜ (ɛɢɬ) ɩɟɪɟɦɟɧɧɨɣ ɬɢɩɚ Color ɡɚɩɢɫɢ ɱɢɫɥɚ ɫ ɜɟɞɭɳɢɦɢ ɇɨɦɟɪɒɟɫɬɧɚɞɰɚɬɢ Ⱦɟɫɹɬɢɱɧɚɹ Ⱦɜɨɢɱɧɚɹ ɡɚɩɢɫɶ ɧɭɥɹɦɢ, ɩɪɟɞɜɚɪɹɟɦɚɹ ɡɚɪɢɱɧɚɹ ɡɚɩɢɫɶ ɡɚɩɢɫɶ ɝɥɚɜɧɨɣ ɛɭɤɜɨɣ «H» ɥɚɬɢɧ0 H0001 1 B.0000.0000.0000.0001 ɫɤɨɝɨ ɚɥɮɚɜɢɬɚ. ȼ ɬɚɛɥɢɰɟ 2 ɩɪɢɜɟɞɟɧɚ ɧɭɦɟɪɚɰɢɹ ɮɥɚɝɨɜ 1 H0002 2 B.0000.0000.0000.0010 (ɛɢɬ) ɩɟɪɟɦɟɧɧɨɣ ɬɢɩɚ Color, 2 H0004 4 B.0000.0000.0000.0100 ɢɯ ɲɟɫɬɧɚɞɰɚɬɟɪɢɱɧɨɟ, ɞɟ3 H0008 8 B.0000.0000.0000.1000 ɫɹɬɢɱɧɨɟ ɢ ɞɜɨɢɱɧɨɟ ɡɧɚɱɟ4 H0010 16 B.0000.0000.0001.0000 ɧɢɟ. ɉɪɢ ɢɫɩɨɥɶɡɨɜɚɧɢɢ ɜ 5 H0020 32 B.0000.0000.0010.0000 ɭɱɢɬɟɥɟ ɢɥɢ ɞɪɭɝɢɯ ɤɨɦɩɨ6 H0040 64 B.0000.0000.0100.0000 ɧɟɧɬɚɯ ɦɨɠɟɬ ɜɨɡɧɢɤɧɭɬɶ 7 H0080 128 B.0000.0000.1000.0000 ɧɟɨɛɯɨɞɢɦɨɫɬɶ ɜ ɩɪɢɫɜɨɟɧɢɢ 8 H0100 256 B.0000.0001.0000.0000 ɧɟɤɨɬɨɪɵɦ ɢɡ ɮɥɚɝɨɜ ɢɥɢ ɢɯ 9 H0200 512 B.0000.0010.0000.0000 ɤɨɦɛɢɧɚɰɢɣ ɢɦɟɧ. ɇɚ ɬɚɤɨɟ 10 H0400 1024 B.0000.0100.0000.0000 ɢɦɟɧɨɜɚɧɢɟ ɧɟ ɧɚɤɥɚɞɵɜɚɟɬ11 H0800 2048 B.0000.1000.0000.0000 ɫɹ ɧɢɤɚɤɢɯ ɨɝɪɚɧɢɱɟɧɢɣ, 12 H1000 4096 B.0001.0000.0000.0000 ɯɨɬɹ ɜɨɡɦɨɠɧɨ ɛɭɞɟɬ ɜɵɪɚ13 H2000 8192 B.0010.0000.0000.0000 ɛɨɬɚɧ ɫɬɚɧɞɚɪɬ ɢ ɧɚ ɧɚɡɜɚɧɢɹ 14 H4000 16384 B.0100.0000.0000.0000 ɱɚɫɬɨ ɢɫɩɨɥɶɡɭɟɦɵɯ ɰɜɟɬɨɜ 15 H8000 32768 B.1000.0000.0000.0000 (ɦɚɫɨɤ, ɫɨɜɨɤɭɩɧɨɫɬɟɣ ɮɥɚɝɨɜ).
2.2.2 Ɉɩɟɪɚɰɢɢ ɫ ɩɟɪɟɦɟɧɧɵɦɢ ɬɢɩɚ ɰɜɟɬ (Color) ȼ ɬɚɛɥ. 3 ɩɪɢɜɟɞɟɧɵ ɨɩɟɪɚɰɢɢ ɫ ɩɟɪɟɦɟɧɧɵɦɢ ɬɢɩɚ Color. ɉɟɪɜɵɟ ɩɹɬɶ ɨɩɟɪɚɰɢɣ ɦɨɝɭɬ ɢɫɩɨɥɶɡɨɜɚɬɶɫɹ ɬɨɥɶɤɨ ɞɥɹ ɫɪɚɜɧɟɧɢɹ ɩɟɪɟɦɟɧɧɵɯ ɬɢɩɚ Color, ɚ ɨɫɬɚɥɶɧɵɟ ɱɟɬɵɪɟ ɨɩɟɪɚɰɢɢ – ɞɥɹ ɜɵɱɢɫɥɟɧɢɹ ɜɵɪɚɠɟɧɢɣ ɬɢɩɚ Color. Ɍɚɛɥɢɰɚ 3 ɉɪɟɞɨɩɪɟɞɟɥɟɧɧɵɟ ɤɨɧɫɬɚɧɬɵ ɨɩɟɪɚɰɢɣ ɫ ɩɟɪɟɦɟɧɧɵɦɢ ɬɢɩɚ ɐɜɟɬ (Color) ɄɨɞɈɛɨɡɧɚɱɟɧɢɟȼɵɱɢɫɥɹɟɦɨɟ ɜɵɪɚɠɟɧɢɟɌɢɩ ɪɟɡɭɥɶɬɚɬɚɉɨɹɫɧɟɧɢɟ 1 CEqual A=B Logic ɉɨɥɧɨɟ ɫɨɜɩɚɞɟɧɢɟ. 2 CIn A And B = A Logic A ɫɨɞɟɪɠɢɬɫɹ ɜ ȼ. 3 CInclude A And B = B Logic Ⱥ ɫɨɞɟɪɠɢɬ ȼ. 4 CExclude A And B = 0 Logic A ɢ ȼ ɜɡɚɢɦɨɢɫɤɥɸɱɚɸɳɢɟ. 5 CIntersect A And B <> 0 Logic Ⱥ ɢ ȼ ɩɟɪɟɫɟɤɚɸɬɫɹ. 6 COr A Or B ɋolor ɉɨɛɢɬɧɨɟ ɜɤɥɸɱɚɸɳɟɟ ɢɥɢ. 7 CAnd A And B Color ɉɨɛɢɬɧɨɟ ɢ. 8 CXor A Xor B Color ɉɨɛɢɬɧɨɟ ɢɫɤɥɸɱɚɸɳɟɟ ɢɥɢ 9 CNot Not A Color ɉɨɛɢɬɧɨɟ ɨɬɪɢɰɚɧɢɟ ȼ ɪɹɞɟ ɡɚɩɪɨɫɨɜ ɧɟɨɛɯɨɞɢɦɨ ɭɤɚɡɚɬɶ ɬɢɩ ɨɩɟɪɚɰɢɢ ɧɚɞ ɰɜɟɬɨɦ. Ⱦɥɹ ɩɟɪɟɞɚɱɢ ɬɚɤɢɯ ɩɚɪɚɦɟɬɪɨɜ ɢɫɩɨɥɶɡɭɟɬɫɹ ɩɟɪɟɦɟɧɧɚɹ ɬɢɩɚ Integer. ȼ ɤɚɱɟɫɬɜɟ ɡɧɚɱɟɧɢɣ ɩɟɪɟɞɚɟɬɫɹ ɫɨɞɟɪɠɢɦɨɟ ɫɨɨɬɜɟɬɫɬɜɭɸɳɟɣ ɹɱɟɣɤɢ ɫɬɨɥɛɰɚ ɤɨɞ ɬɚɛɥ. 3.
2.3 ɉɪɢɜɟɞɟɧɢɟ ɢ ɩɪɟɨɛɪɚɡɨɜɚɧɢɟ ɬɢɩɨɜ ȿɫɬɶ ɞɜɚ ɩɭɬɢ ɢɫɩɨɥɶɡɨɜɚɬɶ ɩɟɪɟɦɟɧɧɭɸ ɨɞɧɨɝɨ ɬɢɩɚ ɤɚɤ ɩɟɪɟɦɟɧɧɭɸ ɞɪɭɝɨɝɨ ɬɢɩɚ. ɉɟɪɜɵɣ ɩɭɬɶ ɫɨɫɬɨɢɬ ɜ ɩɪɟɨɛɪɚɡɨɜɚɧɢɢ ɡɧɚɱɟɧɢɹ ɤ ɡɚɞɚɧɧɨɦɭ ɬɢɩɭ. Ɍɚɤ, ɞɥɹ ɩɪɟɨɛɪɚɡɨɜɚɧɢɹ ɰɟɥɨɱɢɫɥɟɧɧɨɣ ɩɟɪɟɦɟɧɧɨɣ ɤ ɞɟɣɫɬɜɢɬɟɥɶɧɨɦɭ ɬɢɩɭ, ɞɨɫɬɚɬɨɱɧɨ ɩɪɨɫɬɨ ɩɪɢɫɜɨɢɬɶ ɩɟɪɟɦɟɧɧɨɣ ɞɟɣɫɬɜɢɬɟɥɶɧɨɝɨ ɬɢɩɚ ɰɟɥɨɱɢɫɥɟɧɧɨɟ ɡɧɚɱɟɧɢɟ. ɋ ɨɛɪɚɬɧɵɦ ɩɪɟɨɛɪɚɡɨɜɚɧɢɟɦ ɫɥɨɠɧɟɟ, ɩɨɫɤɨɥɶɤɭ ɧɟ ɹɫɧɨ ɱɬɨ ɞɟɥɚɬɶ ɫ ɞɪɨɛɧɨɣ ɱɚɫɬɶɸ. ȼ ɬɚɛɥ. 4 ɩɪɢɜɟɞɟɧɵ ɜɫɟ ɬɢɩɵ, ɤɨɬɨɪɵɟ ɦɨɠɧɨ ɩɪɟɨɛɪɚɡɨɜɚɬɶ ɩɪɢɫɜɚɢɜɚɧɢɟɦ ɩɟɪɟɦɟɧɧɨɣ ɞɪɭɝɨɝɨ ɬɢɩɚ. ȼ ɬɚɛɥ. 5 ɩɪɢɜɟɞɟɧɵ ɜɫɟ ɮɭɧɤɰɢɢ ɩɪɟɨɛɪɚɡɨɜɚɧɢɹ ɬɢɩɨɜ.
CHAP2.DOC
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Ɍɢɩ ɩɟɪɟɦɟɧɧɨɣ, ɤɨɬɨɪɨɣ ɩɪɨɢɡɜɨɞɢɬɫɹ ɩɪɢɫɜɚɢɜɚɧɢɟ Real
Long
Integer
Ɍɚɛɥɢɰɚ 4 ɉɪɟɨɛɪɚɡɨɜɚɧɢɟ ɬɢɩɨɜ ɩɪɹɦɵɦ ɩɪɢɫɜɚɢɜɚɧɢɟɦ ɉɨɹɫɧɟɧɢɟ Ɍɢɩ ɜɵɪɚɠɟɧɢɹ, ɤɨɬɨɪɨɟ ɦɨɠɟɬ ɛɵɬɶ ɩɪɢɫɜɨɟɧɨ Real, Integer, Long Ɂɧɚɱɟɧɢɟ ɩɪɟɨɛɪɚɡɭɟɬɫɹ ɤ ɩɥɚɜɚɸɳɟɦɭ ɜɢɞɭ. ɉɪɢ ɩɪɟɨɛɪɚɡɨɜɚɧɢɢ ɡɧɚɱɟɧɢɹ ɜɵɪɚɠɟɧɢɹ ɬɢɩɚ Long ɜɨɡɦɨɠɧɚ ɩɨɬɟɪɹ ɬɨɱɧɨɫɬɢ. Integer, Long ɉɪɢ ɩɪɟɨɛɪɚɡɨɜɚɧɢɢ ɬɢɩɚ Integer, ɞɟɣɫɬɜɭɸɬ ɫɥɟɞɭɸɳɢɟ ɩɪɚɜɢɥɚ. Ɂɧɚɱɟɧɢɟ ɩɟɪɟɦɟɧɧɨɣ ɩɨɦɟɳɚɟɬɫɹ ɜ ɞɜɚ ɦɥɚɞɲɢɯ ɛɚɣɬɚ. ȿɫɥɢ ɡɧɚɱɟɧɢɟ ɜɵɪɚɠɟɧɢɹ ɛɨɥɶɲɟ ɥɢɛɨ ɪɚɜɧɨ ɧɨɥɸ, ɬɨ ɫɬɚɪɲɢɟ ɛɚɣɬɵ ɪɚɜɧɵ H0000, ɜ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ ɫɬɚɪɲɢɟ ɛɚɣɬɵ ɪɚɜɧɵ HFFFF. Integer, Long ɉɪɢ ɩɪɟɨɛɪɚɡɨɜɚɧɢɢ ɜɵɪɚɠɟɧɢɹ ɬɢɩɚ Long ɡɧɚɱɟɧɢɟ ɞɜɭɯ ɫɬɚɪɲɢɯ ɛɚɣɬ ɨɬɛɪɚɫɵɜɚɟɬɫɹ.
ɉɪɢ ɜɵɱɢɫɥɟɧɢɢ ɱɢɫɥɨɜɵɯ ɜɵɪɚɠɟɧɢɣ ɞɟɣɫɬɜɭɸɬ ɫɥɟɞɭɸɳɢɟ ɩɪɚɜɢɥɚ ɩɪɟɨɛɪɚɡɨɜɚɧɢɹ ɬɢɩɨɜ: ȼɵɪɚɠɟɧɢɹ ɜɵɱɢɫɥɹɸɬɫɹ ɫɥɟɜɚ ɧɚ ɩɪɚɜɨ. ȿɫɥɢ ɞɜɚ ɨɩɟɪɚɧɞɚ ɢɦɟɸɬ ɨɞɢɧ ɬɢɩ, ɬɨ ɪɟɡɭɥɶɬɚɬ ɢɦɟɟɬ ɬɨɬ ɠɟ ɬɢɩ. ȿɫɥɢ ɚɪɝɭɦɟɧɬɵ ɢɦɟɸɬ ɪɚɡɧɵɟ ɬɢɩɵ, ɬɨ ɜɵɪɚɠɟɧɢɟ ɢɦɟɟɬ ɫɬɚɪɲɢɣ ɢɡ ɞɜɭɯ ɬɢɩɨɜ. ɋɩɢɫɨɤ ɱɢɫɥɨɜɵɯ ɬɢɩɨɜ ɩɨ ɭɛɵɜɚɧɢɸ ɫɬɚɪɲɢɧɫɬɜɚ: Real, Long, Integer. 4. Ɋɟɡɭɥɶɬɚɬ ɨɩɟɪɚɰɢɢ ɞɟɥɟɧɢɹ ɞɟɣɫɬɜɢɬɟɥɶɧɵɯ ɱɢɫɟɥ (ɨɩɟɪɚɰɢɹ «/») ɜɫɟɝɞɚ ɢɦɟɟɬ ɬɢɩ Real, ɜɧɟ ɡɚɜɢɫɢɦɨɫɬɢ ɨɬ ɬɢɩɨɜ ɚɪɝɭɦɟɧɬɨɜ. ȼ ɨɬɥɢɱɢɟ ɨɬ ɩɪɟɨɛɪɚɡɨɜɚɧɢɹ ɬɢɩɨɜ ɩɪɢɜɟɞɟɧɢɟ ɬɢɩɨɜ ɩɨɡɜɨɥɹɟɬ ɩɨ-ɪɚɡɧɨɦɭ ɢɧɬɟɪɩɪɟɬɢɪɨɜɚɬɶ ɨɞɧɭ ɨɛɥɚɫɬɶ ɩɚɦɹɬɢ. Ɏɭɧɤɰɢɹ ɩɪɢɜɟɞɟɧɢɹ ɬɢɩɚ ɩɪɢɦɟɧɢɦɚ ɬɨɥɶɤɨ ɤ ɩɟɪɟɦɟɧɧɵɦ ɢɥɢ ɷɥɟɦɟɧɬɚɦ ɦɚɫɫɢɜɚ (ɩɪɟɨɛɪɚɡɨɜɚɧɢɟ ɬɢɩɨɜ ɩɪɢɦɟɧɢɦɨ ɢ ɤ ɜɵɪɚɠɟɧɢɹɦ). Ɋɟɤɨɦɟɧɞɭɟɬɫɹ ɢɫɩɨɥɶɡɨɜɚɬɶ ɩɪɢɜɟɞɟɧɢɟ ɬɢɩɨɜ ɬɨɥɶɤɨ ɞɥɹ ɬɢɩɨɜ, ɢɦɟɸɳɢɯ ɨɞɢɧɚɤɨɜɭɸ ɞɥɢɧɭ. ɇɚɩɪɢɦɟɪ, Integer ɢ Color ɢɥɢ Real ɢ Long. ɋɩɢɫɨɤ ɮɭɧɤɰɢɣ ɩɪɢɜɟɞɟɧɢɹ ɬɢɩɨɜ ɩɪɢɜɟɞɟɧ ɜ ɬɚɛɥ. 6. 1. 2. 3.
Ɍɚɛɥɢɰɚ 5 ɂɦɹ Ɍɢɩ ɮɭɧɤɰɢɢ ɚɪɝɭɦɟɧɬɚ Real Real, Integer, Long Integer Integer, Long Long Integer, Long Str Real, Integer, Long Round Real
Ɏɭɧɤɰɢɢ ɩɪɟɨɛɪɚɡɨɜɚɧɢɹ ɬɢɩɨɜ Ɉɩɢɫɚɧɢɟ
Ɍɢɩ ɪɟɡɭɥɶɬɚɬɚ Real Ⱥɧɚɥɨɝɢɱɧɨ ɩɪɹɦɨɦɭ ɩɪɢɫɜɚɢɜɚɧɢɸ
Ⱥɧɚɥɨɝɢɱɧɨ ɩɪɹɦɨɦɭ ɩɪɢɫɜɚɢɜɚɧɢɸ Ⱥɧɚɥɨɝɢɱɧɨ ɩɪɹɦɨɦɭ ɩɪɢɫɜɚɢɜɚɧɢɸ ɉɪɟɞɫɬɚɜɥɹɟɬ ɱɢɫɥɨɜɨɣ ɚɪɝɭɦɟɧɬ ɜ ɜɢɞɟ ɫɢɦɜɨɥɶɧɨɣ ɫɬɪɨɤɢ ɜ ɞɟɫɹɬɢɱɧɨɦ ɜɢɞɟ Long Ɉɤɪɭɝɥɹɟɬ ɞɟɣɫɬɜɢɬɟɥɶɧɨɟ ɡɧɚɱɟɧɢɟ ɞɨ ɛɥɢɠɚɣɲɟɝɨ ɞɥɢɧɧɨɝɨ ɰɟɥɨɝɨ. ȿɫɥɢ ɡɧɚɱɟɧɢɟ ɞɟɣɫɬɜɢɬɟɥɶɧɨɝɨ ɜɵɪɚɠɟɧɢɹ ɜɵɯɨɞɢɬ ɡɚ ɞɢɚɩɚɡɨɧ ɞɥɢɧɧɨɝɨ ɰɟɥɨɝɨ, ɬɨ ɪɟɡɭɥɶɬɚɬ ɪɚɜɟɧ ɧɭɥɸ. Truncate Real Long ɉɪɟɨɛɪɚɡɭɟɬ ɞɟɣɫɬɜɢɬɟɥɶɧɨɟ ɡɧɚɱɟɧɢɟ ɜ ɞɥɢɧɧɨɟ ɰɟɥɨɟ ɩɭɬɟɦ ɨɬɛɪɚɫɵɜɚɧɢɹ ɞɪɨɛɧɨɣ ɱɚɫɬɢ. ȿɫɥɢ ɡɧɚɱɟɧɢɟ ɞɟɣɫɬɜɢɬɟɥɶɧɨɝɨ ɜɵɪɚɠɟɧɢɹ ɜɵɯɨɞɢɬ ɡɚ ɞɢɚɩɚɡɨɧ ɞɥɢɧɧɨɝɨ ɰɟɥɨɝɨ, ɬɨ ɪɟɡɭɥɶɬɚɬ ɪɚɜɟɧ ɧɭɥɸ. LVal String Long ɉɪɟɨɛɪɚɡɭɟɬ ɞɥɢɧɧɨɟ ɰɟɥɨɟ ɢɡ ɫɢɦɜɨɥɶɧɨɝɨ ɩɪɟɞɫɬɚɜɥɟɧɢɹ ɜɨ ɜɧɭɬɪɟɧɧɟɟ. RVal String Real ɉɪɟɨɛɪɚɡɭɟɬ ɞɟɣɫɬɜɢɬɟɥɶɧɨɟ ɱɢɫɥɨ ɢɡ ɫɢɦɜɨɥɶɧɨɝɨ ɩɪɟɞɫɬɚɜɥɟɧɢɹ ɜɨ ɜɧɭɬɪɟɧɧɟɟ. StrColor Color String ɉɪɟɨɛɪɚɡɭɟɬ ɜɧɭɬɪɟɧɧɟɟ ɩɪɟɞɫɬɚɜɥɟɧɢɟ ɩɟɪɟɦɟɧɧɨɣ ɬɢɩɚ Color ɜ ɫɨɨɬɜɟɬɫɬɜɢɢ ɫ ɪɚɡɞ. «Ɂɧɚɱɟɧɢɟ ɩɟɪɟɦɟɧɧɨɣ ɬɢɩɚ ɰɜɟɬ» ValColor String Color ɉɪɟɨɛɪɚɡɭɟɬ ɫɢɦɜɨɥɶɧɨɟ ɩɪɟɞɫɬɚɜɥɟɧɢɟ ɩɟɪɟɦɟɧɧɨɣ ɬɢɩɚ Color ɜɨ ɜɧɭɬɪɟɧɧɟɟ. Color Integer Color ɂɧɬɟɪɩɪɟɬɢɪɭɟɬ ɰɟɥɨɟ ɱɢɫɥɨ ɤɚɤ ɡɧɚɱɟɧɢɟ ɬɢɩɚ Color. ɋɥɟɞɭɸɳɢɟ ɩɪɢɦɟɪɵ ɢɥɥɸɫɬɪɢɪɭɸɬ ɢɫɩɨɥɶɡɨɜɚɧɢɟ ɩɪɟɨɛɪɚɡɨɜɚɧɢɹ ɢ ɩɪɢɜɟɞɟɧɢɹ ɬɢɩɨɜ: ɉɪɢ ɜɵɱɢɫɥɟɧɢɢ ɫɥɟɞɭɸɳɢɯ ɱɟɬɵɪɟɯ ɜɵɪɚɠɟɧɢɣ, ɩɨɥɭɱɚɸɬɫɹ ɪɚɡɥɢɱɧɵɟ ɪɟɡɭɥɶɬɚɬɵ 4096*4096=0
CHAP2.DOC
Integer Long String
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Ɍɚɛɥɢɰɚ 6 Ɏɭɧɤɰɢɢ ɩɪɢɜɟɞɟɧɢɹ ɬɢɩɨɜ ɇɚɡɜɚɧɢɟɌɢɩ ɪɟɡɭɥɶɬɚɬɚɈɩɢɫɚɧɢɟ TReal Real ɑɟɬɵɪɟ ɛɚɣɬɚ, ɚɞɪɟɫɭɟɦɵɟ ɩɪɢɜɨɞɢɦɨɣ ɩɟɪɟɦɟɧɧɨɣ, ɢɧɬɟɪɩɪɟɬɢɪɭɸɬɫɹ ɤɚɤ ɞɟɣɫɬɜɢɬɟɥɶɧɨɟ ɱɢɫɥɨ. TInteger Integer Ⱦɜɚ ɛɚɣɬɚ, ɚɞɪɟɫɭɟɦɵɟ ɩɪɢɜɨɞɢɦɨɣ ɩɟɪɟɦɟɧɧɨɣ, ɢɧɬɟɪɩɪɟɬɢɪɭɸɬɫɹ ɤɚɤ ɰɟɥɨɟ ɱɢɫɥɨ. TLong Long ɑɟɬɵɪɟ ɛɚɣɬɚ, ɚɞɪɟɫɭɟɦɵɟ ɩɪɢɜɨɞɢɦɨɣ ɩɟɪɟɦɟɧɧɨɣ, ɢɧɬɟɪɩɪɟɬɢɪɭɸɬɫɹ ɤɚɤ ɞɥɢɧɧɨɟ ɰɟɥɨɟ. TRealArray RealArray Ɉɛɥɚɫɬɶ ɩɚɦɹɬɢ, ɚɞɪɟɫɭɟɦɚɹ ɩɪɢɜɨɞɢɦɨɣ ɩɟɪɟɦɟɧɧɨɣ, ɢɧɬɟɪɩɪɟɬɢɪɭɸɬɫɹ ɤɚɤ ɦɚɫɫɢɜ ɞɟɣɫɬɜɢɬɟɥɶɧɵɯ ɱɢɫɟɥ. TPRealArray PRealArray ɑɟɬɵɪɟ ɛɚɣɬɚ, ɚɞɪɟɫɭɟɦɵɟ ɩɪɢɜɨɞɢɦɨɣ ɩɟɪɟɦɟɧɧɨɣ, ɢɧɬɟɪɩɪɟɬɢɪɭɸɬɫɹ ɤɚɤ ɭɤɚɡɚɬɟɥɶ ɧɚ ɦɚɫɫɢɜ ɞɟɣɫɬɜɢɬɟɥɶɧɵɯ ɱɢɫɟɥ. TIntegerArray IntegerArray Ɉɛɥɚɫɬɶ ɩɚɦɹɬɢ, ɚɞɪɟɫɭɟɦɚɹ ɩɪɢɜɨɞɢɦɨɣ ɩɟɪɟɦɟɧɧɨɣ, ɢɧɬɟɪɩɪɟɬɢɪɭɸɬɫɹ ɤɚɤ ɦɚɫɫɢɜ ɰɟɥɵɯ ɱɢɫɟɥ. TPIntegerArray PIntegerArray ɑɟɬɵɪɟ ɛɚɣɬɚ, ɚɞɪɟɫɭɟɦɵɟ ɩɪɢɜɨɞɢɦɨɣ ɩɟɪɟɦɟɧɧɨɣ, ɢɧɬɟɪɩɪɟɬɢɪɭɸɬɫɹ ɤɚɤ ɭɤɚɡɚɬɟɥɶ ɧɚ ɦɚɫɫɢɜ ɰɟɥɵɯ ɱɢɫɟɥ. TLongArray LongArray Ɉɛɥɚɫɬɶ ɩɚɦɹɬɢ, ɚɞɪɟɫɭɟɦɚɹ ɩɪɢɜɨɞɢɦɨɣ ɩɟɪɟɦɟɧɧɨɣ, ɢɧɬɟɪɩɪɟɬɢɪɭɸɬɫɹ ɤɚɤ ɦɚɫɫɢɜ ɞɥɢɧɧɵɯ ɰɟɥɵɯ. TPLongArray PLongArray ɑɟɬɵɪɟ ɛɚɣɬɚ, ɚɞɪɟɫɭɟɦɵɟ ɩɪɢɜɨɞɢɦɨɣ ɩɟɪɟɦɟɧɧɨɣ, ɢɧɬɟɪɩɪɟɬɢɪɭɸɬɫɹ ɤɚɤ ɭɤɚɡɚɬɟɥɶ ɧɚ ɦɚɫɫɢɜ ɞɥɢɧɧɵɯ ɰɟɥɵɯ. TLogic Logic Ⱥɞɪɟɫɭɟɦɵɣ ɩɪɢɜɨɞɢɦɨɣ ɩɟɪɟɦɟɧɧɨɣ ɛɚɣɬ ɢɧɬɟɪɩɪɟɬɢɪɭɸɬɫɹ ɤɚɤ ɥɨɝɢɱɟɫɤɚɹ ɩɟɪɟɦɟɧɧɚɹ. TLogicArray LogicArray Ɉɛɥɚɫɬɶ ɩɚɦɹɬɢ, ɚɞɪɟɫɭɟɦɚɹ ɩɪɢɜɨɞɢɦɨɣ ɩɟɪɟɦɟɧɧɨɣ, ɢɧɬɟɪɩɪɟɬɢɪɭɸɬɫɹ ɤɚɤ ɦɚɫɫɢɜ ɥɨɝɢɱɟɫɤɢɯ ɩɟɪɟɦɟɧɧɵɯ. TPLogicArray LogicArray ɑɟɬɵɪɟ ɛɚɣɬɚ, ɚɞɪɟɫɭɟɦɵɟ ɩɪɢɜɨɞɢɦɨɣ ɩɟɪɟɦɟɧɧɨɣ, ɢɧɬɟɪɩɪɟɬɢɪɭɸɬɫɹ ɤɚɤ ɭɤɚɡɚɬɟɥɶ ɧɚ ɦɚɫɫɢɜ ɥɨɝɢɱɟɫɤɢɯ ɩɟɪɟɦɟɧɧɵɯ. TColor Color Ⱦɜɚ ɛɚɣɬɚ, ɚɞɪɟɫɭɟɦɵɟ ɩɪɢɜɨɞɢɦɨɣ ɩɟɪɟɦɟɧɧɨɣ, ɢɧɬɟɪɩɪɟɬɢɪɭɸɬɫɹ ɤɚɤ ɩɟɪɟɦɟɧɧɚɹ ɬɢɩɚ ɰɜɟɬ. TFuncType FuncType ɑɟɬɵɪɟ ɛɚɣɬɚ, ɚɞɪɟɫɭɟɦɵɟ ɩɪɢɜɨɞɢɦɨɣ ɩɟɪɟɦɟɧɧɨɣ, ɢɧɬɟɪɩɪɟɬɢɪɭɸɬɫɹ ɤɚɤ ɚɞɪɟɫ ɮɭɧɤɰɢɢ. TString String 256 ɛɚɣ ɨɛɥɚɫɬɢ ɩɚɦɹɬɢ, ɚɞɪɟɫɭɟɦɨɣ ɩɪɢɜɨɞɢɦɨɣ ɩɟɪɟɦɟɧɧɨɣ, ɢɧɬɟɪɩɪɟɬɢɪɭɸɬɫɹ ɤɚɤ ɫɬɪɨɤɚ ɫɢɦɜɨɥɨɜ. TPString PString ɑɟɬɵɪɟ ɛɚɣɬɚ, ɚɞɪɟɫɭɟɦɵɟ ɩɪɢɜɨɞɢɦɨɣ ɩɟɪɟɦɟɧɧɨɣ, ɢɧɬɟɪɩɪɟɬɢɪɭɸɬɫɹ ɤɚɤ ɭɤɚɡɚɬɟɥɶ ɧɚ ɫɬɪɨɤɭ ɫɢɦɜɨɥɨɜ. TVisual Visual ɑɟɬɵɪɟ ɛɚɣɬɚ, ɚɞɪɟɫɭɟɦɵɟ ɩɪɢɜɨɞɢɦɨɣ ɩɟɪɟɦɟɧɧɨɣ, ɢɧɬɟɪɩɪɟɬɢɪɭɸɬɫɹ ɤɚɤ ɨɬɨɛɪɚɠɚɟɦɵɣ ɷɥɟɦɟɧɬ. TPointer Pointer ɑɟɬɵɪɟ ɛɚɣɬɚ, ɚɞɪɟɫɭɟɦɵɟ ɩɪɢɜɨɞɢɦɨɣ ɩɟɪɟɦɟɧɧɨɣ, ɢɧɬɟɪɩɪɟɬɢɪɭɸɬɫɹ ɤɚɤ ɚɞɪɟɫ. ɉɨɫɤɨɥɶɤɭ ɤɨɧɫɬɚɧɬɚ 4096 ɢɦɟɟɬ ɬɢɩ Integer, ɚ 4096*4096=16777216=256*65536 , ɬɨ ɟɫɬɶ ɦɥɚɞɲɢɟ ɞɜɚ ɛɚɣɬɚ ɪɟɡɭɥɶɬɚɬɚ ɪɚɜɧɵ ɧɭɥɸ. Long(4096*4096)=0 ɉɨɫɤɨɥɶɤɭ ɨɛɚ ɫɨɦɧɨɠɢɬɟɥɹ ɢɦɟɟɬ ɬɢɩ Integer, ɬɨ ɢ ɜɵɪɚɠɟɧɢɟ ɢɦɟɟɬ ɬɢɩ Integer. ɋɥɟɞɨɜɚɬɟɥɶɧɨ, ɪɟɡɭɥɶɬɚɬ ɭɦɧɨɠɟɧɢɹ ɪɚɜɟɧ ɧɭɥɸ, ɤɨɬɨɪɵɣ ɡɚɬɟɦ ɩɪɟɨɛɪɚɡɭɟɬɫɹ ɤ ɬɢɩɭ Long. Long(4096)*4096=16777216 ɉɨɫɤɨɥɶɤɭ ɩɟɪɜɵɣ ɫɨɦɧɨɠɢɬɟɥɶ ɢɦɟɟɬ ɬɢɩ ɞɥɢɧɧɨɟ ɰɟɥɨɟ, ɬɨ ɢ ɜɵɪɚɠɟɧɢɟ ɢɦɟɟɬ ɬɢɩ ɞɥɢɧɧɨɟ ɰɟɥɨɟ. 4096.0*4096=1.677722E+7 ɉɨɫɤɨɥɶɤɭ ɩɟɪɜɵɣ ɫɨɦɧɨɠɢɬɟɥɶ ɢɦɟɟɬ ɬɢɩ Real, ɬɨ ɢ ɜɵɪɚɠɟɧɢɟ ɢɦɟɟɬ ɬɢɩ Real. ɂɡ-ɡɚ ɧɟɞɨɫɬɚɬɤɚ ɬɨɱɧɨɫɬɢ ɩɪɨɢɡɨɲɥɚ ɩɨɬɟɪɹ ɬɨɱɧɨɫɬɢ ɜ ɫɟɞɶɦɨɦ ɡɧɚɤɟ. ȼ ɫɥɟɞɭɸɳɟɦ ɩɪɢɦɟɪɟ, ɢɫɩɨɥɶɡɭɹ ɩɪɢɜɟɞɟɧɢɟ ɬɢɩɨɜ, ɜ ɦɚɫɫɢɜ ɞɟɣɫɬɜɢɬɟɥɶɧɵɯ ɱɢɫɟɥ A ɪɚɡɦɟɪɨɦ ɜ 66 ɷɥɟɦɟɧɬɨɜ ɫɤɥɚɞɵɜɚɸɬɫɹ: ɞɟɣɫɬɜɢɬɟɥɶɧɨɟ ɱɢɫɥɨ ɜ ɩɟɪɜɵɣ ɷɥɟɦɟɧɬ ɦɚɫɫɢɜɚ; ɞɥɢɧɧɨɟ ɰɟɥɨɟ ɜɨ ɜɬɨɪɨɣ ɷɥɟɦɟɧɬ ɦɚɫɫɢɜɚ ɢ ɫɢɦɜɨɥɶɧɭɸ ɫɬɪɨɤɭ ɜ ɷɥɟɦɟɧɬɵ ɫ 3 ɩɨ 66. A[1]= 1.677722E+7 TLong(A[2])= 16777216 TString(A[3])=‘ɉɪɢɦɟɪ ɩɪɢɜɟɞɟɧɢɹ ɬɢɩɨɜ’
CHAP2.DOC
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ɇɟɨɛɯɨɞɢɦɨ ɨɬɦɟɬɢɬɶ, ɱɬɨ ɷɥɟɦɟɧɬɵ ɦɚɫɫɢɜɚ A, ɧɚɱɢɧɚɹ ɫɨ ɜɬɨɪɨɝɨ, ɩɨɫɥɟ ɜɵɩɨɥɧɟɧɢɹ ɩɪɢɜɟɞɟɧɧɨɝɨ ɜɵɲɟ ɮɪɚɝɦɟɧɬɚ ɩɪɨɝɪɚɦɦɵ ɧɟ ɪɟɤɨɦɟɧɞɭɟɬɫɹ ɢɫɩɨɥɶɡɨɜɚɬɶ ɤɚɤ ɞɟɣɫɬɜɢɬɟɥɶɧɵɟ ɱɢɫɥɚ, ɩɨɫɤɨɥɶɤɭ ɷɥɟɦɟɧɬ A[2] ɫɨɞɟɪɠɢɬ ɡɧɚɱɟɧɢɟ 2.350988ȿ-38, ɚ ɷɥɟɦɟɧɬ A[5] – ɡɧɚɱɟɧɢɟ -4.577438ȿ-18. Ɂɧɚɱɟɧɢɟ ɷɥɟɦɟɧɬɨɜ, ɧɚɱɢɧɚɹ ɫ A[8] (ɫɢɦɜɨɥɶɧɚɹ ɫɬɪɨɤɚ ‘ɉɪɢɦɟɪ ɩɪɢɜɟɞɟɧɢɹ ɬɢɩɨɜ’ ɫɨɞɟɪɠɢɬ 23 ɫɢɦɜɨɥɚ ɢ ɡɚɧɢɦɚɟɬ 24 ɛɚɣɬɚ, ɬɨ ɟɫɬɶ ɲɟɫɬɶ ɷɥɟɦɟɧɬɨɜ ɦɚɫɫɢɜɚ) ɜɨɨɛɳɟ ɧɟ ɡɚɜɢɫɹɬ ɨɬ ɩɪɢɜɟɞɟɧɧɨɝɨ ɮɪɚɝɦɟɧɬɚ ɩɪɨɝɪɚɦɦɵ ɢ ɫɨɞɟɪɠɚɬ «ɦɭɫɨɪ», ɤɨɬɨɪɵɣ ɬɚɦ ɧɚɯɨɞɢɥɫɹ ɪɚɧɟɟ. ȼ ɫɩɢɫɤɟ ɬɢɩɨɜ ɨɩɪɟɞɟɥɟɧɵ ɬɨɥɶɤɨ ɨɞɧɨɦɟɪɧɵɟ ɦɚɫɫɢɜɵ. Ɉɞɧɚɤɨ, ɩɪɢ ɧɟɨɛɯɨɞɢɦɨɫɬɢ, ɜɨɡɦɨɠɧɨ ɢɫɩɨɥɶɡɨɜɚɧɢɟ ɞɜɭɦɟɪɧɵɯ ɦɚɫɫɢɜɨɜ. Ⱦɥɹ ɷɬɨɝɨ ɜ ɨɞɧɨɦɟɪɧɵɣ ɦɚɫɫɢɜ A ɧɟɨɛɯɨɞɢɦɨ ɩɨɦɟɫɬɢɬɶ ɭɤɚɡɚɬɟɥɢ ɧɚ ɨɞɧɨɦɟɪɧɵɟ ɦɚɫɫɢɜɵ. ɉɪɢ ɷɬɨɦ I,J-ɣ ɷɥɟɦɟɧɬ ɞɜɭɦɟɪɧɨɝɨ ɦɚɫɫɢɜɚ ɡɚɩɢɫɵɜɚɟɬɫɹ ɜ ɜɢɞɟ: TPRealArray(A[I])^[J] ȼ ɷɬɨɦ ɩɪɢɦɟɪɟ ɢɫɩɨɥɶɡɨɜɚɧɚ ɮɭɧɤɰɢɹ ɩɪɢɜɟɞɟɧɢɹ ɬɢɩɨɜ TPRealArray, ɭɤɚɡɵɜɚɸɳɚɹ, ɱɬɨ I-ɣ ɷɥɟɦɟɧɬ ɦɚɫɫɢɜɚ A ɧɭɠɧɨ ɢɧɬɟɪɩɪɟɬɢɪɨɜɚɬɶ ɤɚɤ ɭɤɚɡɚɬɟɥɶ ɧɚ ɨɞɧɨɦɟɪɧɵɣ ɦɚɫɫɢɜ ɞɟɣɫɬɜɢɬɟɥɶɧɵɯ ɱɢɫɟɥ, ɢ ɨɩɟɪɚɰɢɹ «^» ɭɤɚɡɵɜɚɸɳɚɹ, ɱɬɨ ɜɦɟɫɬɨ ɭɤɚɡɚɬɟɥɹ ɧɚ ɦɚɫɫɢɜ TPRealArray(A[I]) ɢɫɩɨɥɶɡɭɟɬɫɹ ɦɚɫɫɢɜ, ɧɚ ɤɨɬɨɪɵɣ ɨɧ ɭɤɚɡɵɜɚɟɬ. Ɍɚɤɢɦ ɨɛɪɚɡɨɦ, ɢɫɩɨɥɶɡɨɜɚɧɢɟ ɮɭɧɤɰɢɣ ɩɪɢɜɟɞɟɧɢɹ ɬɢɩɨɜ ɩɨɡɜɨɥɹɟɬ ɢɡ ɨɞɧɨɦɟɪɧɵɯ ɦɚɫɫɢɜɨɜ ɫɬɪɨɢɬɶ ɫɬɪɭɤɬɭɪɵ ɩɪɨɢɡɜɨɥɶɧɨɣ ɫɥɨɠɧɨɫɬɢ. ȼ ɹɡɵɤɚɯ ɩɪɨɝɪɚɦɦɢɪɨɜɚɧɢɹ, ɬɚɤɢɯ ɤɚɤ C ɢ ɉɚɫɤɚɥɶ, ɫɭɳɟɫɬɜɭɟɬ ɜɨɡɦɨɠɧɨɫɬɶ ɫɬɪɨɢɬɶ ɩɨɥɶɡɨɜɚɬɟɥɶɫɤɢɟ ɬɢɩɵ ɞɚɧɧɵɯ. ɉɪɢ ɪɚɡɪɚɛɨɬɤɟ ɫɬɚɧɞɚɪɬɚ ɷɬɢ ɜɨɡɦɨɠɧɨɫɬɢ ɛɵɥɢ ɢɫɤɥɸɱɟɧɵ, ɩɨɫɤɨɥɶɤɭ ɢɫɩɨɥɶɡɨɜɚɧɢɟ ɩɨɥɶɡɨɜɚɬɟɥɶɫɤɢɯ ɬɢɩɨɜ, ɨɛɥɟɝɱɚɹ ɧɚɩɢɫɚɧɢɟ ɩɪɨɝɪɚɦɦ, ɫɢɥɶɧɨ ɡɚɬɪɭɞɧɹɟɬ ɪɚɡɪɚɛɨɬɤɭ ɤɨɦɩɢɥɹɬɨɪɚ ɢɥɢ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ, ɚ ɩɪɢ ɢɫɩɨɥɶɡɨɜɚɧɢɢ ɷɬɨɝɨ ɹɡɵɤɚ ɞɥɹ ɨɩɢɫɚɧɢɹ ɤɨɦɩɨɧɟɧɬɨɜ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚ ɧɟɨɛɯɨɞɢɦɨɫɬɶ ɜ ɩɨɥɶɡɨɜɚɬɟɥɶɫɤɢɯ ɬɢɩɚɯ ɞɚɧɧɵɯ ɜɨɡɧɢɤɚɟɬ ɱɪɟɡɜɵɱɚɣɧɨ ɪɟɞɤɨ. ɇɚɩɪɢɦɟɪ, ɩɪɢ ɨɩɢɫɚɧɢɢ ɩɪɢɦɟɪɨɜ ɜɫɟɯ ɤɨɦɩɨɧɟɧɬɨɜ, ɩɪɢɜɟɞɟɧɧɵɯ ɜ ɞɚɧɧɨɣ ɤɧɢɝɟ, ɬɚɤɚɹ ɧɟɨɛɯɨɞɢɦɨɫɬɶ ɧɢ ɪɚɡɭ ɧɟ ɜɨɡɧɢɤɥɚ.
2.4 Ɉɩɟɪɚɰɢɢ ȼ ɞɚɧɧɨɦ ɪɚɡɞɟɥɟ ɩɪɢɜɟɞɟɧɵ ɜɫɟ ɨɩɟɪɚɰɢɢ, ɤɨɬɨɪɵɟ ɦɨɝɭɬ ɛɵɬɶ ɢɫɩɨɥɶɡɨɜɚɧɵ ɩɪɢ ɩɨɫɬɪɨɟɧɢɢ ɜɵɪɚɠɟɧɢɣ ɪɚɡɥɢɱɧɨɝɨ ɬɢɩɚ. ȼ ɬɚɛɥ. 7 ɩɪɢɜɟɞɟɧɵ ɨɩɟɪɚɰɢɢ, ɤɨɬɨɪɵɟ ɞɨɩɭɫɬɢɦɵ ɜ ɰɟɥɨɱɢɫɥɟɧɧɵɯ ɜɵɪɚɠɟɧɢɹɯ (ɜɵɪɚɠɟɧɢɹɯ ɬɢɩɚ Integer ɢɥɢ Long). ȼ ɬɚɛɥ. 8 – ɫɩɢɫɨɤ, ɞɨɩɨɥɧɹɸɳɢɣ ɫɩɢɫɨɤ ɨɩɟɪɚɰɢɣ ɢɡ ɬɚɛɥ. 7 ɞɨ ɩɨɥɧɨɝɨ ɫɩɢɫɤɚ ɨɩɟɪɚɰɢɣ, ɞɨɩɭɫɬɢɦɵɯ ɜ ɜɵɪɚɠɟɧɢɹɯ ɞɟɣɫɬɜɢɬɟɥɶɧɨɝɨ ɬɢɩɚ. ȼ ɬɚɛɥ. 9 – ɨɩɟɪɚɰɢɢ, ɞɨɩɭɫɬɢɦɵɟ ɩɪɢ ɩɨɫɬɪɨɟɧɢɢ ɥɨɝɢɱɟɫɤɢɯ ɜɵɪɚɠɟɧɢɣ. ȼ ɬɚɛɥ. 10 –ɞɥɹ ɜɵɪɚɠɟɧɢɣ ɬɢɩɚ ɫɢɦɜɨɥɶɧɚɹ ɫɬɪɨɤɚ. ȼ ɬɚɛɥ. 3 – ɞɥɹ ɜɵɪɚɠɟɧɢɣ ɬɢɩɚ Color. Ɍɚɛɥɢɰɚ 7 Ɉɩɟɪɚɰɢɢ, ɞɨɩɭɫɬɢɦɵɟ ɜ ɰɟɥɨɱɢɫɥɟɧɧɵɯ ɜɵɪɚɠɟɧɢɹɯ ɍɪɨɜɟɧɶ ɈɛɨɡɧɚɌɢɩ 1-ɝɨ Ɍɢɩ 2-ɝɨ Ɍɢɩ ɇɚɡɜɚɧɢɟ ɨɩɟɪɚɰɢɢ ɩɪɢɨɪɢɬɟɬɚ ɱɟɧɢɟ ɨɩɟɪɚɧɞɚ ɨɩɟɪɚɧɞɚ ɪɟɡɭɥɶɬɚɬɚ 1 * Integer Integer Integer ɍɦɧɨɠɟɧɢɟ 1 * Long Integer Long ɍɦɧɨɠɟɧɢɟ 1 * Integer Long Long ɍɦɧɨɠɟɧɢɟ 1 * Long Long Long ɍɦɧɨɠɟɧɢɟ 1 Div Integer Integer Integer ɐɟɥɨɱɢɫɥɟɧɧɨɟ ɞɟɥɟɧɢɟ 1 Div Integer Long Long ɐɟɥɨɱɢɫɥɟɧɧɨɟ ɞɟɥɟɧɢɟ 1 Div Long Integer Long ɐɟɥɨɱɢɫɥɟɧɧɨɟ ɞɟɥɟɧɢɟ 1 Div Long Long Long ɐɟɥɨɱɢɫɥɟɧɧɨɟ ɞɟɥɟɧɢɟ 1 Mod Integer Integer Integer Ɉɫɬɚɬɨɤ ɨɬ ɞɟɥɟɧɢɹ 1 Mod Long Integer Long Ɉɫɬɚɬɨɤ ɨɬ ɞɟɥɟɧɢɹ 1 Mod Integer Long Long Ɉɫɬɚɬɨɤ ɨɬ ɞɟɥɟɧɢɹ 1 Mod Long Long Long Ɉɫɬɚɬɨɤ ɨɬ ɞɟɥɟɧɢɹ 2 + Integer Integer Integer ɋɥɨɠɟɧɢɟ 2 + Integer Long Long ɋɥɨɠɟɧɢɟ 2 + Long Integer Long ɋɥɨɠɟɧɢɟ 2 + Long Long Long ɋɥɨɠɟɧɢɟ 2 – Integer Integer Integer ȼɵɱɢɬɚɧɢɟ 2 – Integer Long Long ȼɵɱɢɬɚɧɢɟ 2 – Long Integer Long ȼɵɱɢɬɚɧɢɟ 2 – Long Long Long ȼɵɱɢɬɚɧɢɟ 3 And Integer Integer Integer ɉɨɛɢɬɧɨɟ ɂ 3 And Long Long Long ɉɨɛɢɬɧɨɟ ɂ 3 Or Integer Integer Integer ɉɨɛɢɬɧɨɟ ɜɤɥɸɱɚɸɳɟɟ ɂɅɂ
CHAP2.DOC
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ɍɪɨɜɟɧɶ ɩɪɢɨɪɢɬɟɬɚ 3 3 3 3 3
Ɍɚɛɥɢɰɚ 7 Ɉɩɟɪɚɰɢɢ, ɞɨɩɭɫɬɢɦɵɟ ɜ ɰɟɥɨɱɢɫɥɟɧɧɵɯ ɜɵɪɚɠɟɧɢɹɯ (ɩɪɨɞɨɥɠɟɧɢɟ) ɈɛɨɡɧɚɌɢɩ 1-ɝɨ Ɍɢɩ 2-ɝɨ Ɍɢɩ ɇɚɡɜɚɧɢɟ ɨɩɟɪɚɰɢɢ ɱɟɧɢɟ ɨɩɟɪɚɧɞɚ ɨɩɟɪɚɧɞɚ ɪɟɡɭɥɶɬɚɬɚ Or Long Long Long ɉɨɛɢɬɧɨɟ ɜɤɥɸɱɚɸɳɟɟ ɂɅɂ Xor Integer Integer Integer ɉɨɛɢɬɧɨɟ ɢɫɤɥɸɱɚɸɳɟɟ ɂɅɂ Xor Long Long Long ɉɨɛɢɬɧɨɟ ɢɫɤɥɸɱɚɸɳɟɟ ɂɅɂ Not Integer Integer Integer ɉɨɛɢɬɧɨɟ ɨɬɪɢɰɚɧɢɟ Not Long Long Long ɉɨɛɢɬɧɨɟ ɨɬɪɢɰɚɧɢɟ
Ɍɚɛɥɢɰɚ 8 Ɉɩɟɪɚɰɢɢ, ɞɨɩɨɥɧɹɸɳɢɟ ɫɩɢɫɨɤ ɨɩɟɪɚɰɢɣ ɢɡ ɬɚɛɥ. 7 ɞɨ ɩɨɥɧɨɝɨ ɫɩɢɫɤɚ ɨɩɟɪɚɰɢɣ, ɞɨɩɭɫɬɢɦɵɯ ɜ ɜɵɪɚɠɟɧɢɹɯ ɞɟɣɫɬɜɢɬɟɥɶɧɨɝɨ ɬɢɩɚ. ɍɪɨɜɟɧɶ ɈɛɨɡɧɚɌɢɩ 1-ɝɨ Ɍɢɩ 2-ɝɨ Ɍɢɩ ɇɚɡɜɚɧɢɟ ɨɩɟɪɚɰɢɢ ɩɪɢɨɪɢɬɟɬɚ ɱɟɧɢɟ ɨɩɟɪɚɧɞɚ ɨɩɟɪɚɧɞɚ ɪɟɡɭɥɶɬɚɬɚ 1 * Real Integer, Real, Long Real ɍɦɧɨɠɟɧɢɟ 1 / Integer, Real, Long Integer, Real, Long Real Ⱦɟɥɟɧɢɟ 1 RMod Integer, Real, Long Integer, Real, Long Real Ɉɫɬɚɬɨɤ ɨɬ ɞɟɥɟɧɢɹ 2 + Real Integer, Real, Long Real ɋɥɨɠɟɧɢɟ 2 – Real Integer, Real, Long Real ȼɵɱɢɬɚɧɢɟ
ɍɪɨɜɟɧɶ ɩɪɢɨɪɢɬ. 1 1 1 1 1 1 2 2 2 2
Ɍɚɛɥɢɰɚ 9 Ɉɩɟɪɚɰɢɢ, ɞɨɩɭɫɬɢɦɵɟ ɩɪɢ ɩɨɫɬɪɨɟɧɢɢ ɥɨɝɢɱɟɫɤɢɯ ɜɵɪɚɠɟɧɢɣ ɈɛɨɡɧɚɌɢɩ 1-ɝɨ Ɍɢɩ 2-ɝɨ Ɍɢɩ ɇɚɡɜɚɧɢɟ ɨɩɟɪɚɰɢɢ ɱɟɧɢɟ ɨɩɟɪɚɧɞɚ ɨɩɟɪɚɧɞɚ ɪɟɡɭɥɶɬɚɬɚ > Integer, Real, Long Integer, Real, Long Logic Ȼɨɥɶɲɟ < Integer, Real, Long Integer, Real, Long Logic Ɇɟɧɶɲɟ >= Integer, Real, Long Integer, Real, Long Logic Ȼɨɥɶɲɟ ɢɥɢ ɪɚɜɧɨ <= Integer, Real, Long Integer, Real, Long Logic Ɇɟɧɶɲɟ ɢɥɢ ɪɚɜɧɨ = Integer, Real, Long Integer, Real, Long Logic Ɋɚɜɧɨ <> Integer, Real, Long Integer, Real, Long Logic ɇɟ ɪɚɜɧɨ And Logic Logic Logic Ʌɨɝɢɱɟɫɤɨɟ ɂ Or Logic Logic Logic Ʌɨɝɢɱɟɫɤɨɟ ɜɤɥɸɱɚɸɳɟɟ ɂɅɂ Xor Logic Logic Logic Ʌɨɝɢɱɟɫɤɨɟ ɢɫɤɥɸɱɚɸɳɟɟ ɂɅɂ Not Logic Logic Logic Ʌɨɝɢɱɟɫɤɨɟ ɨɬɪɢɰɚɧɢɟ
ɍɪɨɜɟɧɶ ɩɪɢɨɪɢɬɟɬɚ 1
Ɉɛɨɡɧɚɱɟɧɢɟ +
Ɍɚɛɥɢɰɚ 10 Ɉɩɟɪɚɰɢɢ ɞɥɹ ɜɵɪɚɠɟɧɢɣ ɬɢɩɚ ɫɢɦɜɨɥɶɧɚɹ ɫɬɪɨɤɚ Ɍɢɩ 1-ɝɨ Ɍɢɩ 2-ɝɨ Ɍɢɩ ɇɚɡɜɚɧɢɟ ɨɩɟɪɚɰɢɢ ɨɩɟɪɚɧɞɚ ɨɩɟɪɚɧɞɚ ɪɟɡɭɥɶɬɚɬɚ String String String Ʉɨɧɤɚɬɟɧɚɰɢɹ (ɫɰɟɩɤɚ) ɫɬɪɨɤ.
ȼɨ ɜɫɟɯ ɬɚɛɥɢɰɚɯ ɨɩɟɪɚɰɢɢ ɪɚɡɦɟɳɚɸɬɫɹ ɩɨ ɭɛɵɜɚɧɢɸ ɩɪɢɨɪɢɬɟɬɚ. Ⱦɥɹ ɤɚɠɞɨɣ ɨɩɟɪɚɰɢɢ ɭɤɚɡɚɧɵ ɞɨɩɭɫɬɢɦɵɟ ɬɢɩɵ ɨɩɟɪɚɧɞɨɜ, ɢ ɬɢɩ ɪɟɡɭɥɶɬɚɬɚ, ɜ ɡɚɜɢɫɢɦɨɫɬɢ ɨɬ ɬɢɩɨɜ ɨɩɟɪɚɧɞɨɜ. ȼ ɬɚɛɥ. 8 ɩɪɢɜɨɞɢɬɫɹ ɧɟɨɛɵɱɧɚɹ ɨɩɟɪɚɰɢɹ RMod – ɨɫɬɚɬɨɤ ɨɬ ɞɟɥɟɧɢɹ ɞɟɣɫɬɜɢɬɟɥɶɧɵɯ ɱɢɫɟɥ. Ɋɟɡɭɥɶɬɚɬ ɷɬɨɣ ɮɭɧɤɰɢɢ ɪɚɜɟɧ ɪɚɡɧɨɫɬɢ ɦɟɠɞɭ ɩɟɪɜɵɦ ɨɩɟɪɚɧɞɨɦ ɢ ɜɬɨɪɵɦ ɨɩɟɪɚɧɞɨɦ, ɭɦɧɨɠɟɧɧɵɦ ɧɚ ɰɟɥɭɸ ɱɚɫɬɶ ɨɬɧɨɲɟɧɢɹ ɩɟɪɜɨɝɨ ɨɩɟɪɚɧɞɚ ɤɨ ɜɬɨɪɨɦɭ. Ʉɪɨɦɟ ɨɩɟɪɚɰɢɣ, ɩɪɢɜɟɞɟɧɧɵɯ ɜ ɬɚɛɥ. 3 ɢ ɬɚɛɥ. 7–10, ɨɩɪɟɞɟɥɟɧɵ ɞɜɟ ɜɡɚɢɦɧɨ ɨɛɪɚɬɧɵɟ ɨɩɟɪɚɰɢɢ ɞɥɹ ɪɚɛɨɬɵ ɫ ɚɞɪɟɫɚɦɢ ɢ ɭɤɚɡɚɬɟɥɹɦɢ: ^ – ɫɬɚɜɢɬɫɹ ɩɨɫɥɟ ɩɟɪɟɦɟɧɧɨɣ ɬɢɩɚ ɭɤɚɡɚɬɟɥɶ. Ɉɡɧɚɱɚɟɬ, ɱɬɨ ɜɦɟɫɬɨ ɭɤɚɡɚɬɟɥɹ ɜ ɜɵɪɚɠɟɧɢɢ ɢɫɩɨɥɶɡɭɟɬɫɹ ɩɟɪɟɦɟɧɧɚɹ ɢɥɢ ɦɚɫɫɢɜ, ɧɚ ɤɨɬɨɪɵɣ ɭɤɚɡɵɜɚɟɬ ɷɬɨɬ ɭɤɚɡɚɬɟɥɶ. ɇɟ ɞɨɩɭɫɤɚɟɬɫɹ ɩɨɫɥɟ ɩɟɪɟɦɟɧɧɵɯ ɬɢɩɚ Pointer. @ – ɫɬɚɜɢɬɫɹ ɩɟɪɟɞ ɢɦɟɧɟɦ ɩɟɪɟɦɟɧɧɨɣ ɥɸɛɨɝɨ ɬɢɩɚ. Ɉɡɧɚɱɚɟɬ, ɱɬɨ ɜ ɜɵɪɚɠɟɧɢɢ ɭɱɚɫɬɜɭɟɬ ɧɟ ɩɟɪɟɦɟɧɧɚɹ, ɚ ɚɞɪɟɫ ɩɟɪɟɦɟɧɧɨɣ. ɂɫɩɨɥɶɡɭɟɬɫɹ ɩɪɢ ɩɪɢɫɜɨɟɧɢɢ ɚɞɪɟɫɨɜ ɩɟɪɟɦɟɧɧɵɯ ɢɥɢ ɦɚɫɫɢɜɨɜ ɩɟɪɟɦɟɧɧɵɦ ɬɢɩɚ ɭɤɚɡɚɬɟɥɶ.
CHAP2.DOC
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2.5 ɉɪɟɞɨɩɪɟɞɟɥɟɧɧɵɟ ɤɨɧɫɬɚɧɬɵ ɉɪɢ ɨɩɢɫɚɧɢɢ ɪɚɡɥɢɱɧɵɯ ɤɨɦɩɨɧɟɧɬɨɜ ɜɨɡɧɢɤɚɟɬ ɧɟɨɛɯɨɞɢɦɨɫɬɶ ɜ ɢɫɩɨɥɶɡɨɜɚɧɢɢ ɧɟɤɨɬɨɪɨɝɨ ɧɚɛɨɪɚ ɫɬɚɧɞɚɪɬɢɡɢɪɨɜɚɧɧɵɯ ɤɨɧɫɬɚɧɬ. ɋɬɚɧɞɚɪɬɧɨɫɬɶ ɧɚɛɨɪɚ ɤɨɧɫɬɚɧɬ ɨɫɨɛɟɧɧɨ ɧɟɨɛɯɨɞɢɦɚ ɩɪɢ ɨɛɦɟɧɟ ɦɟɠɞɭ ɤɨɦɩɨɧɟɧɬɚɦɢ. ȼɫɟ ɤɨɧɫɬɚɧɬɵ, ɩɪɢɜɟɞɟɧɧɵɟ ɜ ɬɚɛɥ. 11, ɨɩɢɫɵɜɚɸɬɫɹ ɜ ɬɟɯ ɪɚɡɞɟɥɚɯ, ɝɞɟ ɨɧɢ ɢɫɩɨɥɶɡɭɸɬɫɹ. ȼ ɬɚɛɥ. 11 ɞɥɹ ɤɚɠɞɨɣ ɤɨɧɫɬɚɧɬɵ ɭɤɚɡɵɜɚɟɬɫɹ ɟɟ ɬɢɩ, ɡɧɚɱɟɧɢɟ ɢ ɧɚɡɜɚɧɢɹ ɪɚɡɞɟɥɨɜ, ɜ ɤɨɬɨɪɵɯ ɨɧɚ ɨɩɢɫɵɜɚɟɬɫɹ. Ɍɚɛɥɢɰɚ 11 ɉɪɟɞɨɩɪɟɞɟɥɟɧɧɵɟ ɤɨɧɫɬɚɧɬɵ ɂɞɟɧɬɢɮɢɤɚɬɨɪɌɢɩɁɧɚɱɟɧɢɟɊɚɡɞɟɥ ɒɟɫɬɧɚɞ. Ⱦɟɫɹɬɢɱ. BackInSignals Integer H0005 5 Ɂɚɩɪɨɫɵ ɤ ɤɨɦɩɨɧɟɧɬɭ ɫɟɬɶ BackOutSignals Integer H0006 6 Ɂɚɩɪɨɫɵ ɤ ɤɨɦɩɨɧɟɧɬɭ ɫɟɬɶ BackɊarameters Integer H0007 7 Ɂɚɩɪɨɫɵ ɤ ɤɨɦɩɨɧɟɧɬɭ ɫɟɬɶ Binary Integer H0001 1 Ɂɚɩɪɨɫɵ ɤɨɦɩɨɧɟɧɬɚ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɨɬɜɟɬɚ BinaryPrep Integer H0000 0 Ɂɚɩɪɨɫɵ ɤɨɦɩɨɧɟɧɬɚ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ BynaryCoded Integer H0003 3 Ɂɚɩɪɨɫɵ ɤɨɦɩɨɧɟɧɬɚ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɨɬɜɟɬɚ CAnd Integer H0007 7 Ɉɩɟɪɚɰɢɢ ɫ ɩɟɪɟɦɟɧɧɵɦɢ ɬɢɩɚ ɰɜɟɬ (Color) Cascad Integer H0002 2 Ɂɚɩɪɨɫɵ ɤ ɤɨɦɩɨɧɟɧɬɭ ɫɟɬɶ CEqual Integer H0001 1 Ɉɩɟɪɚɰɢɢ ɫ ɩɟɪɟɦɟɧɧɵɦɢ ɬɢɩɚ ɰɜɟɬ (Color) CExclude Integer H0004 4 Ɉɩɟɪɚɰɢɢ ɫ ɩɟɪɟɦɟɧɧɵɦɢ ɬɢɩɚ ɰɜɟɬ (Color) CicleFor Integer H0003 3 Ɂɚɩɪɨɫɵ ɤ ɤɨɦɩɨɧɟɧɬɭ ɫɟɬɶ CicleUntil Integer H0004 4 Ɂɚɩɪɨɫɵ ɤ ɤɨɦɩɨɧɟɧɬɭ ɫɟɬɶ CIn Integer H0002 2 Ɉɩɟɪɚɰɢɢ ɫ ɩɟɪɟɦɟɧɧɵɦɢ ɬɢɩɚ ɰɜɟɬ (Color) CInclude Integer H0003 3 Ɉɩɟɪɚɰɢɢ ɫ ɩɟɪɟɦɟɧɧɵɦɢ ɬɢɩɚ ɰɜɟɬ (Color) CIntersect Integer H0005 5 Ɉɩɟɪɚɰɢɢ ɫ ɩɟɪɟɦɟɧɧɵɦɢ ɬɢɩɚ ɰɜɟɬ (Color) CNot Integer H0009 9 Ɉɩɟɪɚɰɢɢ ɫ ɩɟɪɟɦɟɧɧɵɦɢ ɬɢɩɚ ɰɜɟɬ (Color) COr Integer H0006 6 Ɉɩɟɪɚɰɢɢ ɫ ɩɟɪɟɦɟɧɧɵɦɢ ɬɢɩɚ ɰɜɟɬ (Color) CXor Integer H0008 8 Ɉɩɟɪɚɰɢɢ ɫ ɩɟɪɟɦɟɧɧɵɦɢ ɬɢɩɚ ɰɜɟɬ (Color) Element Integer H0000 0 Ɂɚɩɪɨɫɵ ɤ ɤɨɦɩɨɧɟɧɬɭ ɫɟɬɶ Empty Integer H0000 0 Ɂɚɩɪɨɫɵ ɤɨɦɩɨɧɟɧɬɚ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɨɬɜɟɬɚ EmptyPrep Integer H0003 3 Ɂɚɩɪɨɫɵ ɤɨɦɩɨɧɟɧɬɚ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ False Logic H00 FuncPrep Integer H0005 5 Ɂɚɩɪɨɫɵ ɤɨɦɩɨɧɟɧɬɚ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ InSignalMask Integer H0003 3 Ɂɚɩɪɨɫɵ ɤ ɤɨɦɩɨɧɟɧɬɭ ɫɟɬɶ InSignals Integer H0000 0 Ɂɚɩɪɨɫɵ ɤ ɤɨɦɩɨɧɟɧɬɭ ɫɟɬɶ Layer Integer H0001 1 Ɂɚɩɪɨɫɵ ɤ ɤɨɦɩɨɧɟɧɬɭ ɫɟɬɶ MainVisual Visible ɂɧɬɟɪɮɟɣɫɧɵɟ ɮɭɧɤɰɢɢ Major Integer H0002 2 Ɂɚɩɪɨɫɵ ɤɨɦɩɨɧɟɧɬɚ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɨɬɜɟɬɚ mIntegerArray Integer H0002 2 Ɏɭɧɤɰɢɢ ɭɩɪɚɜɥɟɧɢɹ ɩɚɦɹɬɶɸ mLogicArray Integer H0001 1 Ɏɭɧɤɰɢɢ ɭɩɪɚɜɥɟɧɢɹ ɩɚɦɹɬɶɸ mLongArray Integer H0004 4 Ɏɭɧɤɰɢɢ ɭɩɪɚɜɥɟɧɢɹ ɩɚɦɹɬɶɸ ModPrep Integer H0004 4 Ɂɚɩɪɨɫɵ ɤɨɦɩɨɧɟɧɬɚ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ mRealArray Integer H0004 4 Ɏɭɧɤɰɢɢ ɭɩɪɚɜɥɟɧɢɹ ɩɚɦɹɬɶɸ Null Pointer H00000000 ɧɟɬ Ordered Integer H0002 2 Ɂɚɩɪɨɫɵ ɤɨɦɩɨɧɟɧɬɚ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ OutSignals Integer H0001 1 Ɂɚɩɪɨɫɵ ɤ ɤɨɦɩɨɧɟɧɬɭ ɫɟɬɶ Parameters Integer H0002 2 Ɂɚɩɪɨɫɵ ɤ ɤɨɦɩɨɧɟɧɬɭ ɫɟɬɶ ParamMask Integer H0004 4 Ɂɚɩɪɨɫɵ ɤ ɤɨɦɩɨɧɟɧɬɭ ɫɟɬɶ PositPrep Integer H0006 6 Ɂɚɩɪɨɫɵ ɤɨɦɩɨɧɟɧɬɚ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ tbAnswers Integer H0004 4 əɡɵɤ ɨɩɢɫɚɧɢɹ ɡɚɞɚɱɧɢɤɚ tbCalcAnswers Integer H0006 6 əɡɵɤ ɨɩɢɫɚɧɢɹ ɡɚɞɚɱɧɢɤɚ tbCalcReliability Integer H0007 7 əɡɵɤ ɨɩɢɫɚɧɢɹ ɡɚɞɚɱɧɢɤɚ tbColor Integer H0001 1 əɡɵɤ ɨɩɢɫɚɧɢɹ ɡɚɞɚɱɧɢɤɚ tbComment Integer H000A 10 əɡɵɤ ɨɩɢɫɚɧɢɹ ɡɚɞɚɱɧɢɤɚ tbEstimation Integer H0009 9 əɡɵɤ ɨɩɢɫɚɧɢɹ ɡɚɞɚɱɧɢɤɚ
CHAP2.DOC
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Ɍɚɛɥɢɰɚ 11 ɉɪɟɞɨɩɪɟɞɟɥɟɧɧɵɟ ɤɨɧɫɬɚɧɬɵ (ɩɪɨɞɨɥɠɟɧɢɟ) ɂɞɟɧɬɢɮɢɤɚɬɨɪɌɢɩɁɧɚɱɟɧɢɟɊɚɡɞɟɥ ɒɟɫɬɧɚɞ. Ⱦɟɫɹɬɢɱ. tbInput Integer H0002 2 əɡɵɤ ɨɩɢɫɚɧɢɹ ɡɚɞɚɱɧɢɤɚ tbPrepared Integer H0003 3 əɡɵɤ ɨɩɢɫɚɧɢɹ ɡɚɞɚɱɧɢɤɚ tbReliability Integer H0005 5 əɡɵɤ ɨɩɢɫɚɧɢɹ ɡɚɞɚɱɧɢɤɚ tbWeight Integer H0008 8 əɡɵɤ ɨɩɢɫɚɧɢɹ ɡɚɞɚɱɧɢɤɚ True Logic HFF 255 (-1) UnknownLong Integer H0000 0 ɇɟɨɩɪɟɞɟɥɟɧɧɵɟ ɡɧɚɱɟɧɢɹ UnknownReal Real ɧɟɬ 1E-40 ɇɟɨɩɪɟɞɟɥɟɧɧɵɟ ɡɧɚɱɟɧɢɹ UnOrdered Integer H0001 1 Ɂɚɩɪɨɫɵ ɤɨɦɩɨɧɟɧɬɚ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ UserType Integer HFFFF -1 ɋɬɪɭɤɬɭɪɧɚɹ ɟɞɢɧɢɰɚ, ɨɩɪɟɞɟɥɟɧɧɚɹ ɩɨɥɶɡɨɜɚɬɟɥɟɦ. Ɍɪɢ ɩɪɟɞɨɩɪɟɞɟɥɟɧɧɵɟ ɤɨɧɫɬɚɧɬɵ, ɩɪɢɜɟɞɟɧɧɵɟ ɜ ɬɚɛɥ.11, ɧɟ ɨɩɢɫɵɜɚɸɬɫɹ ɧɢ ɜ ɨɞɧɨɦ ɪɚɡɞɟɥɟ ɞɚɧɧɨɣ ɤɧɢɝɢ. ɗɬɨ ɤɨɧɫɬɚɧɬɵ ɨɛɳɟɝɨ ɩɨɥɶɡɨɜɚɧɢɹ. ɂɯ ɡɧɚɱɟɧɢɟ: True – ɡɧɚɱɟɧɢɟ ɢɫɬɢɧɚ ɞɥɹ ɩɪɢɫɜɚɢɜɚɧɢɹ ɩɟɪɟɦɟɧɧɵɦ ɥɨɝɢɱɟɫɤɨɝɨ ɬɢɩɚ. False – ɡɧɚɱɟɧɢɟ ɥɨɠɶ ɞɥɹ ɩɪɢɫɜɚɢɜɚɧɢɹ ɩɟɪɟɦɟɧɧɵɦ ɥɨɝɢɱɟɫɤɨɝɨ ɬɢɩɚ. Null – ɩɭɫɬɨɣ ɭɤɚɡɚɬɟɥɶ. ɂɫɩɨɥɶɡɭɟɬɫɹ ɞɥɹ ɫɪɚɜɧɟɧɢɹ ɢɥɢ ɩɪɢɫɜɚɢɜɚɧɢɹ ɩɟɪɟɦɟɧɧɵɯ ɜɫɟɯ ɬɢɩɨɜ ɭɤɚɡɚɬɟɥɟɣ.
2.6 ɂɧɬɟɪɮɟɣɫɧɵɟ ɮɭɧɤɰɢɢ ɑɚɫɬɨ ɩɪɢ ɨɛɭɱɟɧɢɢ ɢ ɬɟɫɬɢɪɨɜɚɧɢɢ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ɜɨɡɧɢɤɚɟɬ ɧɟɨɛɯɨɞɢɦɨɫɬɶ ɨɬɨɛɪɚɠɚɬɶ ɧɚ ɷɤɪɚɧ ɧɟɤɨɬɨɪɭɸ ɢɧɮɨɪɦɚɰɢɸ. ɇɚɩɪɢɦɟɪ, ɱɢɫɥɨ ɩɪɟɞɴɹɜɥɟɧɢɣ ɩɪɢɦɟɪɨɜ ɫɟɬɢ, ɦɚɤɫɢɦɚɥɶɧɭɸ ɨɰɟɧɤɭ ɢ ɬ.ɩ. ȼɪɹɞ ɥɢ ɪɚɡɭɦɧɨ ɢɫɩɨɥɶɡɨɜɚɬɶ ɹɡɵɤ ɨɩɢɫɚɧɢɹ ɤɨɦɩɨɧɟɧɬɨɜ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚ ɞɥɹ ɨɩɢɫɚɧɢɹ ɢɧɬɟɪɮɟɣɫɚ. ɗɬɨ ɩɪɨɬɢɜɨɪɟɱɢɬ ɬɪɟɛɨɜɚɧɢɸ ɩɟɪɟɧɨɫɢɦɨɫɬɢ ɬɟɤɫɬɚ ɨɩɢɫɚɧɢɹ ɤɨɦɩɨɧɟɧɬɨɜ ɦɟɠɞɭ ɪɚɡɧɵɦɢ ɩɥɚɬɮɨɪɦɚɦɢ ɢ ɨɩɟɪɚɰɢɨɧɧɵɦɢ ɫɢɫɬɟɦɚɦɢ. Ⱦɥɹ ɨɛɥɟɝɱɟɧɢɹ ɫɨɡɞɚɧɢɹ ɢɧɬɟɪɮɟɣɫɚ, ɫ ɨɞɧɨɣ ɫɬɨɪɨɧɵ, ɢ ɨɛɟɫɩɟɱɟɧɢɹ ɭɧɢɜɟɪɫɚɥɶɧɨɫɬɢ ɨɩɢɫɚɧɢɹ ɤɨɦɩɨɧɟɧɬɨɜ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚ, ɫ ɞɪɭɝɨɣ, ɩɪɟɞɥɨɠɟɧ ɧɚɛɨɪ ɢɧɬɟɪɮɟɣɫɧɵɯ ɮɭɧɤɰɢɣ. ɉɨɞɞɟɪɠɤɭ ɷɬɢɯ ɮɭɧɤɰɢɣ ɧɟɫɥɨɠɧɨ ɨɪɝɚɧɢɡɨɜɚɬɶ ɜ ɥɸɛɨɣ ɨɩɟɪɚɰɢɨɧɧɨɣ ɫɪɟɞɟ ɢ ɧɚ ɥɸɛɨɣ ɩɥɚɬɮɨɪɦɟ. ȼ ɨɫɧɨɜɭ ɧɚɛɨɪɚ ɢɧɬɟɪɮɟɣɫɧɵɯ ɮɭɧɤɰɢɣ ɩɨɥɨɠɟɧ ɩɪɢɧɰɢɩ ɨɛɴɟɤɬɧɨ-ɨɪɢɟɧɬɢɪɨɜɚɧɧɨɣ ɦɚɲɢɧɵ ɩɨɬɨɤɚ ɫɨɛɵɬɢɣ. ɉɪɢɦɟɪɨɦ ɬɚɤɢɯ ɫɢɫɬɟɦ ɦɨɠɟɬ ɫɥɭɠɢɬɶ ɢɧɫɬɪɭɦɟɧɬɚɥɶɧɚɹ ɛɢɛɥɢɨɬɟɤɚ ɨɛɴɟɤɬɨɜ (ɤɥɚɫɫɨɜ) Turbo Vision ɮɢɪɦɵ Ȼɨɪɥɚɧɞ, ɢɥɢ ɨɤɨɧɧɵɣ ɢɧɬɟɪɮɟɣɫ Windows ɮɢɪɦɵ Ɇɚɣɤɪɨɫɨɮɬ. ɉɪɟɞɥɚɝɚɟɦɵɣ ɜ ɞɚɧɧɨɦ ɪɚɡɞɟɥɟ ɧɚɛɨɪ ɢɧɬɟɪɮɟɣɫɧɵɯ ɮɭɧɤɰɢɣ ɛɟɞɧɟɟ ɥɸɛɨɣ ɢɡ ɜɵɲɟ ɧɚɡɜɚɧɧɵɯ ɢɧɬɟɪɮɟɣɫɧɵɯ ɛɢɛɥɢɨɬɟɤ, ɧɨ ɩɨɡɜɨɥɹɟɬ ɨɪɝɚɧɢɡɨɜɚɬɶ ɞɨɫɬɚɬɨɱɧɨ ɤɪɚɫɢɜɵɣ ɢ ɭɞɨɛɧɵɣ ɢɧɬɟɪɮɟɣɫ.
2.6.1 ɋɬɪɭɤɬɭɪɚ ɞɚɧɧɵɯ ɢɧɬɟɪɮɟɣɫɧɵɯ ɮɭɧɤɰɢɣ ɗɥɟɦɟɧɬɨɦ ɞɚɧɧɵɯ ɜ ɫɬɪɭɤɬɭɪɟ ɢɧɬɟɪɮɟɣɫɚ ɹɜɥɹɟɬɫɹ ɨɬɨɛɪɚɠɚɟɦɵɣ ɷɥɟɦɟɧɬ. Ʉɚɠɞɵɣ ɨɬɨɛɪɚɠɚɟɦɵɣ ɷɥɟɦɟɧɬ ɢɦɟɟɬ ɫɜɨɢ ɤɨɨɪɞɢɧɚɬɵ ɨɬɧɨɫɢɬɟɥɶɧɨ ɧɚɱɚɥɚ ɜɥɚɞɟɥɶɰɚ – ɨɬɨɛɪɚɠɚɟɦɨɝɨ ɷɥɟɦɟɧɬɚ, ɫɨɞɟɪɠɚɳɟɝɨ ɞɚɧɧɵɣ. ȼɥɚɞɟɥɶɰɟɦ ɨɬɨɛɪɚɠɚɟɦɨɝɨ ɷɥɟɦɟɧɬɚ ɦɨɠɟɬ ɹɜɥɹɬɶɫɹ ɨɞɧɨ ɢɡ ɨɤɨɧ ɢɥɢ ɞɢɚɥɨɝɨɜ ɢɥɢ ɝɥɚɜɧɵɣ ɷɥɟɦɟɧɬ. Ƚɥɚɜɧɵɣ ɨɬɨɛɪɚɠɚɟɦɵɣ ɷɥɟɦɟɧɬ ɹɜɥɹɟɬɫɹ ɩɪɟɞɨɩɪɟɞɟɥɟɧɧɵɦ ɢ ɨɛɨɡɧɚɱɚɟɬɫɹ ɩɟɪɟɦɟɧɧɨɣ MainVisual. ȼ ɩɪɨɝɪɚɦɦɚɯ ɤɨɦɩɨɧɟɧɬɨɜ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚ ɧɟ ɞɨɩɭɫɤɚɟɬɫɹ ɢɡɦɟɧɟɧɢɟ ɡɧɚɱɟɧɢɹ ɩɟɪɟɦɟɧɧɨɣ MainVisual. ɗɬɭ ɩɟɪɟɦɟɧɧɭɸ ɫɬɨɢɬ ɪɚɫɫɦɚɬɪɢɜɚɬɶ ɤɚɤ ɤɨɧɫɬɚɧɬɭ, ɨɞɧɚɤɨ, ɜ ɨɬɥɢɱɢɟ ɨɬ ɜɫɟɯ ɨɫɬɚɥɶɧɵɯ ɤɨɧɫɬɚɧɬ ɟɟ ɡɧɚɱɟɧɢɟ ɨɩɪɟɞɟɥɹɟɬɫɹ ɧɟ ɞɚɧɧɵɦ ɫɬɚɧɞɚɪɬɨɦ, ɚ ɪɚɡɪɚɛɨɬɱɢɤɨɦ ɛɢɛɥɢɨɬɟɤɢ ɢɧɬɟɪɮɟɣɫɧɵɯ ɮɭɧɤɰɢɣ. ȼ ɞɚɧɧɨɣ ɪɚɛɨɬɟ ɧɟ ɪɚɫɫɦɚɬɪɢɜɚɟɬɫɹ ɫɩɨɫɨɛ ɪɟɚɥɢɡɚɰɢɢ ɢɧɬɟɪɮɟɣɫɧɵɯ ɮɭɧɤɰɢɣ ɢ ɧɟ ɨɛɫɭɠɞɚɸɬɫɹ ɡɧɚɱɟɧɢɹ ɩɟɪɟɦɟɧɧɵɯ ɬɢɩɚ Visible. ɉɨ ɷɬɨɦɭ ɫɬɚɜɢɬɫɹ ɟɞɢɧɫɬɜɟɧɧɨɟ ɭɫɥɨɜɢɟ – ɩɟɪɟɦɟɧɧɵɟ ɬɢɩɚ Visible ɦɨɝɭɬ ɢɡɦɟɧɹɬɶ ɫɜɨɢ ɡɧɚɱɟɧɢɹ ɬɨɥɶɤɨ ɩɪɢ ɜɵɡɨɜɟ ɢɧɬɟɪɮɟɣɫɧɵɯ ɮɭɧɤɰɢɣ ɢɥɢ ɩɪɢ ɩɪɢɫɜɚɢɜɚɧɢɢ ɢɦ ɡɧɚɱɟɧɢɣ ɞɪɭɝɨɣ ɩɟɪɟɦɟɧɧɨɣ ɬɨɝɨ ɠɟ ɬɢɩɚ. ȼɫɟ ɨɬɨɛɪɚɠɚɟɦɵɟ ɷɥɟɦɟɧɬɵ ɫɨɡɞɚɸɬɫɹ ɢɧɬɟɪɮɟɣɫɧɵɦɢ ɮɭɧɤɰɢɹɦɢ, ɧɚɡɵɜɚɸɳɢɦɢɫɹ, ɬɚɤ ɠɟ ɤɚɤ ɢ ɫɚɦ ɨɬɨɛɪɚɠɚɟɦɵɣ ɷɥɟɦɟɧɬ. ɂɧɬɟɪɮɟɣɫɧɵɟ ɮɭɧɤɰɢɢ ɫɨɡɞɚɸɳɢɟ ɨɬɨɛɪɚɠɚɟɦɵɟ ɷɥɟɦɟɧɬɵ ɜɨɡɜɪɚɳɚɸɬ ɡɧɚɱɟɧɢɹ ɬɢɩɚ Visible. ȿɫɥɢ ɩɪɢ ɜɵɡɨɜɟ ɫɨɡɞɚɸɳɟɣ ɨɬɨɛɪɚɠɚɟɦɵɣ ɷɥɟɦɟɧɬ ɮɭɧɤɰɢɢ ɷɥɟɦɟɧɬ ɧɟ ɛɵɥ ɫɨɡɞɚɧ, ɬɨ ɮɭɧɤɰɢɹ ɜɨɡɜɪɚɳɚɟɬ ɡɧɚɱɟɧɢɟ Null.
2.6.2 ɋɨɝɥɚɲɟɧɢɟ ɨ ɩɟɪɟɞɚɱɟ ɡɧɚɱɟɧɢɣ ɨɬɨɛɪɚɠɚɟɦɵɦ ɷɥɟɦɟɧɬɚɦ Ɉɬɨɛɪɚɠɚɟɦɵɟ ɷɥɟɦɟɧɬɵ ɦɨɝɭɬ ɛɵɬɶ ɫɜɹɡɚɧɵ ɫ ɨɛɵɱɧɵɦɢ ɩɟɪɟɦɟɧɧɵɦɢ. ȼ ɫɥɟɞɭɸɳɢɯ ɪɚɡɞɟɥɚɯ ɨɩɢɫɚɧɵ ɨɬɨɛɪɚɠɚɟɦɵɟ ɷɥɟɦɟɧɬɵ, ɞɥɹ ɤɨɬɨɪɵɯ ɞɨɥɠɧɚ ɛɵɬɶ ɭɫɬɚɧɨɜɥɟɧɚ ɬɚɤɚɹ ɫɜɹɡɶ. ɉɪɢ ɢɡɦɟɧɟɧɢɢ ɡɧɚɱɟɧɢɹ ɫɜɹɡɚɧɧɨɣ ɩɟɪɟɦɟɧɧɨɣ ɩɪɨɝɪɚɦɦɧɵɦ ɩɭɬɟɦ ɞɥɹ ɨɬɨɛɪɚɠɟɧɢɹ ɷɬɨɝɨ ɢɡɦɟɧɟɧɢɹ ɞɨɥɠɧɚ ɛɵɬɶ ɜɵɡɜɚɧɚ ɮɭɧɤɰɢɹ Refresh, ɫ ɩɟɪɟɦɟɧɧɨɣ ɬɢɩɚ Visible ɞɚɧɧɨɝɨ ɷɥɟɦɟɧɬɚ ɜ ɤɚɱɟɫɬɜɟ ɩɚɪɚɦɟɬɪɚ. ɉɪɢ ɫɜɹɡɵ-
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ɜɚɧɢɢ ɩɟɪɟɦɟɧɧɨɣ ɢ ɨɬɨɛɪɚɠɚɟɦɨɝɨ ɷɥɟɦɟɧɬɚ ɧɟɨɛɯɨɞɢɦɨ ɫɨɜɩɚɞɟɧɢɟ ɬɢɩɚ ɩɟɪɟɦɟɧɧɨɣ ɫ ɫɜɹɡɵɜɚɟɦɵɦ ɩɟɪɟɦɟɧɧɨɣ ɷɬɨɝɨ ɨɬɨɛɪɚɠɚɟɦɨɝɨ ɷɥɟɦɟɧɬɚ.
2.6.3 ɉɟɪɟɱɟɧɶ ɨɬɨɛɪɚɠɚɟɦɵɯ ɷɥɟɦɟɧɬɨɜ ɇɚɡɜɚɧɢɟ ɷɥɟɦɟɧɬɚ: Window (ɨɤɧɨ). ɉɚɪɚɦɟɬɪɵ ɩɪɢ ɫɨɡɞɚɧɢɢ: BeginX, BeginY – Ʉɨɨɪɞɢɧɚɬɵ ɜɟɪɯɧɟɝɨ ɥɟɜɨɝɨ ɭɝɥɚ ɨɤɧɚ ɨɬɧɨɫɢɬɟɥɶɧɨ ɜɥɚɞɟɥɶɰɚ. SizeX, SizeY – Ƚɨɪɢɡɨɧɬɚɥɶɧɵɣ ɢ ɜɟɪɬɢɤɚɥɶɧɵɣ ɪɚɡɦɟɪɵ ɨɤɧɚ. ScrollX, ScrollY – ɐɟɥɨɱɢɫɥɟɧɧɵɟ ɩɚɪɚɦɟɬɪɵ, ɡɚɞɚɸɳɢɟ ɧɚɥɢɱɢɟ ɭ ɨɤɧɚ ɝɨɪɢɡɨɧɬɚɥɶɧɨɣ ɢ ɜɟɪɬɢɤɚɥɶɧɨɣ ɩɨɥɨɫɵ ɩɪɨɤɪɭɬɤɢ. ȿɫɥɢ ɡɧɚɱɟɧɢɟ ɩɚɪɚɦɟɬɪɚ ɪɚɜɧɨ ɧɭɥɸ, ɬɨ ɫɨɨɬɜɟɬɫɬɜɭɸɳɚɹ ɩɨɥɨɫɚ ɩɪɨɤɪɭɬɤɢ ɨɬɫɭɬɫɬɜɭɟɬ, ɩɪɢ ɥɸɛɨɦ ɞɪɭɝɨɦ ɡɧɚɱɟɧɢɢ ɩɚɪɚɦɟɬɪɚ ɜ ɨɤɧɨ ɜɤɥɸɱɚɟɬɫɹ ɫɨɨɬɜɟɬɫɬɜɭɸɳɚɹ ɩɨɥɨɫɚ ɩɪɨɤɪɭɬɤɢ. Text – ɇɚɡɜɚɧɢɟ ɨɤɧɚ. Ɉɩɢɫɚɧɢɟ ɷɥɟɦɟɧɬɚ. ɗɥɟɦɟɧɬ Window ɦɨɠɟɬ ɹɜɥɹɬɶɫɹ ɜɥɚɞɟɥɶɰɟɦ ɥɸɛɵɯ ɞɪɭɝɢɯ ɨɬɨɛɪɚɠɚɟɦɵɯ ɷɥɟɦɟɧɬɨɜ, ɤɪɨɦɟ ɷɥɟɦɟɧɬɨɜ ɬɢɩɚ Dialog. ɉɪɢ ɜɵɡɨɜɟ ɮɭɧɤɰɢɢ Refresh, ɫ ɷɥɟɦɟɧɬɨɦ ɬɢɩɚ Window ɜ ɤɚɱɟɫɬɜɟ ɩɚɪɚɦɟɬɪɚ, ɨɛɧɨɜɥɹɟɬɫɹ ɢɡɨɛɪɚɠɟɧɢɟ ɧɟ ɬɨɥɶɤɨ ɫɚɦɨɝɨ ɨɤɧɚ, ɧɨ ɢ ɜɫɟɯ ɨɬɨɛɪɚɠɚɟɦɵɯ ɷɥɟɦɟɧɬɨɜ, ɞɥɹ ɤɨɬɨɪɵɯ ɷɬɨ ɨɤɧɨ ɹɜɥɹɟɬɫɹ ɜɥɚɞɟɥɶɰɟɦ. Ⱦɥɹ ɨɬɨɛɪɚɠɟɧɢɹ ɫɨɡɞɚɧɧɨɝɨ ɨɤɧɚ ɧɚ ɷɤɪɚɧɟ ɧɟɨɛɯɨɞɢɦɨ ɜɫɬɚɜɢɬɶ ɟɝɨ (ɜɵɡɜɚɬɶ ɮɭɧɤɰɢɸ Insert, ɫ ɞɚɧɧɵɦ ɨɤɧɨɦ ɜ ɤɚɱɟɫɬɜɟ ɜɬɨɪɨɝɨ ɩɚɪɚɦɟɬɪɚ.) ɜ ɨɬɨɛɪɚɠɟɧɧɨɟ ɧɚ ɷɤɪɚɧ ɨɤɧɨ, ɞɢɚɥɨɝ ɢɥɢ ɜ ɨɬɨɛɪɚɠɚɟɦɵɣ ɷɥɟɦɟɧɬ MainVisible. Ⱦɥɹ ɬɨɝɨ ɱɬɨɛɵ ɭɛɪɚɬɶ ɨɤɧɨ ɫ ɷɤɪɚɧɚ, ɧɟɨɛɯɨɞɢɦɨ ɜɵɡɜɚɬɶ ɮɭɧɤɰɢɸ Delete, ɫ ɞɚɧɧɵɦ ɨɤɧɨɦ ɜ ɤɚɱɟɫɬɜɟ ɩɚɪɚɦɟɬɪɚ. Ⱦɥɹ ɭɧɢɱɬɨɠɟɧɢɹ ɨɤɧɚ ɧɟɨɛɯɨɞɢɦɨ ɜɵɡɜɚɬɶ ɮɭɧɤɰɢɸ Erase, ɫ ɞɚɧɧɵɦ ɨɤɧɨɦ ɜ ɤɚɱɟɫɬɜɟ ɩɚɪɚɦɟɬɪɚ. ɉɪɢ ɷɬɨɦ ɭɧɢɱɬɨɠɚɸɬɫɹ ɬɚɤ ɠɟ ɢ ɜɫɟ ɨɬɨɛɪɚɠɚɟɦɵɟ ɷɥɟɦɟɧɬɵ, ɞɥɹ ɤɨɬɨɪɵɯ ɞɚɧɧɨɟ ɨɤɧɨ ɹɜɥɹɥɨɫɶ ɜɥɚɞɟɥɶɰɟɦ. ɇɚɡɜɚɧɢɟ ɷɥɟɦɟɧɬɚ: Dialog (Ⱦɢɚɥɨɝ). ɉɚɪɚɦɟɬɪɵ ɩɪɢ ɫɨɡɞɚɧɢɢ: BeginX, BeginY – Ʉɨɨɪɞɢɧɚɬɵ ɜɟɪɯɧɟɝɨ ɥɟɜɨɝɨ ɭɝɥɚ ɨɤɧɚ ɞɢɚɥɨɝɚ ɨɬɧɨɫɢɬɟɥɶɧɨ ɜɥɚɞɟɥɶɰɚ. SizeX, SizeY – Ƚɨɪɢɡɨɧɬɚɥɶɧɵɣ ɢ ɜɟɪɬɢɤɚɥɶɧɵɣ ɪɚɡɦɟɪɵ ɨɤɧɚ ɞɢɚɥɨɝɚ. ScrollX, ScrollY – ɐɟɥɨɱɢɫɥɟɧɧɵɟ ɩɚɪɚɦɟɬɪɵ, ɡɚɞɚɸɳɢɟ ɧɚɥɢɱɢɟ ɭ ɨɤɧɚ ɞɢɚɥɨɝɚ ɝɨɪɢɡɨɧɬɚɥɶɧɨɣ ɢ ɜɟɪɬɢɤɚɥɶɧɨɣ ɩɨɥɨɫɵ ɩɪɨɤɪɭɬɤɢ. ȿɫɥɢ ɡɧɚɱɟɧɢɟ ɩɚɪɚɦɟɬɪɚ ɪɚɜɧɨ ɧɭɥɸ, ɬɨ ɫɨɨɬɜɟɬɫɬɜɭɸɳɚɹ ɩɨɥɨɫɚ ɩɪɨɤɪɭɬɤɢ ɨɬɫɭɬɫɬɜɭɟɬ, ɩɪɢ ɥɸɛɨɦ ɞɪɭɝɨɦ ɡɧɚɱɟɧɢɢ ɩɚɪɚɦɟɬɪɚ ɜ ɨɤɧɨ ɞɢɚɥɨɝɚ ɜɤɥɸɱɚɟɬɫɹ ɫɨɨɬɜɟɬɫɬɜɭɸɳɚɹ ɩɨɥɨɫɚ ɩɪɨɤɪɭɬɤɢ. Text – ɇɚɡɜɚɧɢɟ ɨɤɧɚ ɞɢɚɥɨɝɚ. Ɉɩɢɫɚɧɢɟ ɷɥɟɦɟɧɬɚ. ɗɥɟɦɟɧɬ Dialog ɦɨɠɟɬ ɹɜɥɹɬɶɫɹ ɜɥɚɞɟɥɶɰɟɦ ɥɸɛɵɯ ɞɪɭɝɢɯ ɨɬɨɛɪɚɠɚɟɦɵɯ ɷɥɟɦɟɧɬɨɜ. ɗɬɨɬ ɷɥɟɦɟɧɬ ɹɜɥɹɟɬɫɹ ɦɨɞɚɥɶɧɵɦ, ɬɨ ɟɫɬɶ ɜɨ ɜɪɟɦɹ ɪɚɛɨɬɵ ɞɢɚɥɨɝɚ ɧɟɜɨɡɦɨɠɟɧ ɜɵɡɨɜ ɦɟɧɸ, ɩɟɪɟɯɨɞ ɜ ɞɪɭɝɢɟ ɨɤɧɚ ɢ ɞɢɚɥɨɝɢ ɢ ɬ.ɞ. ȿɫɥɢ ɨɞɧɨɜɪɟɦɟɧɧɨ ɚɤɬɢɜɧɨ ɧɟɫɤɨɥɶɤɨ ɞɢɚɥɨɝɨɜ, ɬɨ ɦɨɞɚɥɶɧɵɦ ɹɜɥɹɟɬɫɹ ɩɨɫɥɟɞɧɢɣ ɩɨ ɩɨɪɹɞɤɭ ɨɬɨɛɪɚɠɟɧɢɹ ɧɚ ɷɤɪɚɧɟ. ɉɪɢ ɡɚɤɪɵɬɢɢ ɬɟɤɭɳɟɝɨ ɞɢɚɥɨɝɚ ɦɨɞɚɥɶɧɨɫɬɶ ɩɟɪɟɯɨɞɢɬ ɤ ɩɪɟɞɵɞɭɳɟɦɭ ɢ ɬ.ɞ. ɉɪɢ ɜɵɡɨɜɟ ɮɭɧɤɰɢɢ Refresh, ɫ ɷɥɟɦɟɧɬɨɦ ɬɢɩɚ Dialog ɜ ɤɚɱɟɫɬɜɟ ɩɚɪɚɦɟɬɪɚ, ɨɛɧɨɜɥɹɟɬɫɹ ɢɡɨɛɪɚɠɟɧɢɟ ɧɟ ɬɨɥɶɤɨ ɫɚɦɨɝɨ ɨɤɧɚ ɞɢɚɥɨɝɚ, ɧɨ ɢ ɜɫɟɯ ɨɬɨɛɪɚɠɚɟɦɵɯ ɷɥɟɦɟɧɬɨɜ, ɞɥɹ ɤɨɬɨɪɵɯ ɷɬɨɬ ɞɢɚɥɨɝ ɹɜɥɹɟɬɫɹ ɜɥɚɞɟɥɶɰɟɦ. Ⱦɥɹ ɨɬɨɛɪɚɠɟɧɢɹ ɫɨɡɞɚɧɧɨɝɨ ɞɢɚɥɨɝɚ ɧɚ ɷɤɪɚɧɟ ɧɟɨɛɯɨɞɢɦɨ ɜɫɬɚɜɢɬɶ ɟɝɨ (ɜɵɡɜɚɬɶ ɮɭɧɤɰɢɸ Insert, ɫ ɞɚɧɧɵɦ ɞɢɚɥɨɝɨɦ ɜ ɤɚɱɟɫɬɜɟ ɜɬɨɪɨɝɨ ɩɚɪɚɦɟɬɪɚ.) ɜ ɨɬɨɛɪɚɠɚɟɦɵɣ ɷɥɟɦɟɧɬ MainVisible. ɋ ɷɬɨɝɨ ɦɨɦɟɧɬɚ ɞɢɚɥɨɝ ɫɬɚɧɨɜɢɬɫɹ ɦɨɞɚɥɶɧɵɦ. Ɉɧ ɨɫɬɚɟɬɫɹ ɦɨɞɚɥɶɧɵɦ ɥɢɛɨ ɞɨ ɜɫɬɚɜɤɢ ɜ MainVisible ɞɪɭɝɨɝɨ ɞɢɚɥɨɝɚ, ɥɢɛɨ ɞɨ ɡɚɤɪɵɬɢɹ ɞɢɚɥɨɝɚ. ȿɫɥɢ ɦɨɞɚɥɶɧɨɫɬɶ ɩɨɬɟɪɹɧɚ ɞɢɚɥɨɝɨɦ ɢɡ-ɡɚ ɡɚɩɭɫɤɚ ɫɥɟɞɭɸɳɟɝɨ ɞɢɚɥɨɝɚ, ɬɨ ɩɪɢ ɡɚɤɪɵɬɢɢ ɩɨɫɥɟɞɧɟɝɨ ɞɢɚɥɨɝɚ ɫɬɚɬɭɫ ɦɨɞɚɥɶɧɨɫɬɢ ɜɨɫɫɬɚɧɚɜɥɢɜɚɟɬɫɹ. Ⱦɥɹ ɬɨɝɨ ɱɬɨɛɵ ɡɚɤɪɵɬɶ ɞɢɚɥɨɝ, ɧɟɨɛɯɨɞɢɦɨ ɜɵɡɜɚɬɶ ɮɭɧɤɰɢɸ Delete, ɫ ɞɚɧɧɵɦ ɞɢɚɥɨɝɨɦ ɜ ɤɚɱɟɫɬɜɟ ɩɚɪɚɦɟɬɪɚ. Ⱦɥɹ ɭɧɢɱɬɨɠɟɧɢɹ ɞɢɚɥɨɝɚ ɧɟɨɛɯɨɞɢɦɨ ɜɵɡɜɚɬɶ ɮɭɧɤɰɢɸ Erase, ɫ ɞɚɧɧɵɦ ɞɢɚɥɨɝɨɦ ɜ ɤɚɱɟɫɬɜɟ ɩɚɪɚɦɟɬɪɚ. ɉɪɢ ɷɬɨɦ ɭɧɢɱɬɨɠɚɸɬɫɹ ɬɚɤ ɠɟ ɢ ɜɫɟ ɨɬɨɛɪɚɠɚɟɦɵɟ ɷɥɟɦɟɧɬɵ, ɞɥɹ ɤɨɬɨɪɵɯ ɞɚɧɧɵɣ ɞɢɚɥɨɝ ɹɜɥɹɥɫɹ ɜɥɚɞɟɥɶɰɟɦ. ɇɚɡɜɚɧɢɟ ɷɥɟɦɟɧɬɚ: Label (ɦɟɬɤɚ). ɉɚɪɚɦɟɬɪɵ ɩɪɢ ɫɨɡɞɚɧɢɢ: BeginX, BeginY – Ʉɨɨɪɞɢɧɚɬɵ ɜɟɪɯɧɟɝɨ ɥɟɜɨɝɨ ɭɝɥɚ ɦɟɬɤɢ ɨɬɧɨɫɢɬɟɥɶɧɨ ɜɥɚɞɟɥɶɰɚ. SizeX, SizeY – Ƚɨɪɢɡɨɧɬɚɥɶɧɵɣ ɢ ɜɟɪɬɢɤɚɥɶɧɵɣ ɪɚɡɦɟɪɵ ɦɟɬɤɢ. Text – ɬɟɤɫɬ ɦɟɬɤɢ. Ɉɩɢɫɚɧɢɟ ɷɥɟɦɟɧɬɚ. ɗɥɟɦɟɧɬ Label ɧɟ ɦɨɠɟɬ ɹɜɥɹɬɶɫɹ ɜɥɚɞɟɥɶɰɟɦ ɞɪɭɝɢɯ ɨɬɨɛɪɚɠɚɟɦɵɯ ɷɥɟɦɟɧɬɨɜ. ɗɬɨɬ ɷɥɟɦɟɧɬ ɧɟ ɫɜɹɡɚɧ ɫ ɩɟɪɟɦɟɧɧɵɦɢ. Ʉɚɤ ɩɪɚɜɢɥɨ, ɨɧ ɢɫɩɨɥɶɡɭɟɬɫɹ ɞɥɹ ɨɪɝɚɧɢɡɚɰɢɢ ɨɬɨɛɪɚɠɟɧɢɹ ɜ ɨɤɧɟ ɢɥɢ ɞɢɚɥɨɝɟ ɫɬɚɬɢɱɟɫɤɨɣ ɢɧɮɨɪɦɚɰɢɢ ɢɥɢ ɜ ɤɚɱɟɫɬɜɟ ɩɨɞɩɢɫɢ ɩɨɥɹ ɜɜɨɞɚ. ȿɫɥɢ ɦɟɬɤɚ ɫɜɹɡɚɧɚ ɫ ɩɨɥɟɦ ɜɜɨɞɚ, ɬɨ ɩɟɪɟɞɚɱɚ ɭɩɪɚɜɥɟɧɢɹ ɷɬɨɣ ɦɟɬɤɟ ɚɜɬɨɦɚɬɢɱɟɫɤɢ ɜɥɟɱɟɬ ɡɚ ɫɨɛɨɣ ɩɟɪɟɞɚɱɭ ɭɩɪɚɜɥɟɧɢɹ ɫɜɹɡɚɧɧɨɦɭ ɫ ɧɟɣ ɩɨɥɸ. Ⱦɥɹ ɨɪɝɚɧɢɡɚɰɢɢ ɫɜɹɡɢ ɦɟɬɤɢ ɫ ɩɨɥɟɦ ɧɟɨɛɯɨɞɢɦɨ ɜɵɡɜɚɬɶ ɮɭɧɤɰɢɸ Link, ɩɟɪɟɞɚɜ ɟɣ ɜ ɤɚɱɟɫɬɜɟ ɩɟɪɜɨɝɨ ɩɚɪɚɦɟɬɪɚ ɦɟɬɤɭ, ɚ ɜɬɨɪɵɦ ɩɚɪɚɦɟɬɪɨɦ ɬɨɬ ɷɥɟɦɟɧɬ, ɫ ɤɨɬɨɪɵɦ ɧɟɨɛɯɨɞɢ-
CHAP2.DOC
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ɦɨ ɭɫɬɚɧɨɜɢɬɶ ɫɜɹɡɶ. ɋɜɹɡɶ ɦɨɠɟɬ ɛɵɬɶ ɭɫɬɚɧɨɜɥɟɧɚ ɬɨɥɶɤɨ ɫ ɨɞɧɢɦ ɷɥɟɦɟɧɬɨɦ. ɉɪɢ ɩɨɜɬɨɪɧɨɦ ɜɵɡɨɜɟ ɮɭɧɤɰɢɢ Link ɭɫɬɚɧɚɜɥɢɜɚɟɬɫɹ ɫɜɹɡɶ ɫ ɧɨɜɵɦ ɷɥɟɦɟɧɬɨɦ, ɚ ɫɜɹɡɶ ɫ ɩɪɟɠɧɢɦ ɷɥɟɦɟɧɬɨɦ ɪɚɡɪɵɜɚɟɬɫɹ. Ⱦɥɹ ɜɤɥɸɱɟɧɢɹ ɦɟɬɤɢ ɜ ɨɤɧɨ ɢɥɢ ɞɢɚɥɨɝ, ɧɟɨɛɯɨɞɢɦɨ ɜɵɡɜɚɬɶ ɮɭɧɤɰɢɸ Insert, ɫ ɨɤɧɨɦ ɢɥɢ ɞɢɚɥɨɝɨɦ ɜ ɤɚɱɟɫɬɜɟ ɩɟɪɜɨɝɨ ɩɚɪɚɦɟɬɪɚ ɢ ɦɟɬɤɨɣ ɜ ɤɚɱɟɫɬɜɟ ɜɬɨɪɨɝɨ ɩɚɪɚɦɟɬɪɚ. Ⱦɥɹ ɭɧɢɱɬɨɠɟɧɢɹ ɦɟɬɤɢ ɧɟɨɛɯɨɞɢɦɨ ɜɵɡɜɚɬɶ ɮɭɧɤɰɢɸ Erase, ɫ ɞɚɧɧɨɣ ɦɟɬɤɨɣ ɜ ɤɚɱɟɫɬɜɟ ɩɚɪɚɦɟɬɪɚ. ɇɚɡɜɚɧɢɟ ɷɥɟɦɟɧɬɚ: StringVisible (ɫɬɪɨɤɨɜɵɣ ɷɥɟɦɟɧɬ). ɉɚɪɚɦɟɬɪɵ ɩɪɢ ɫɨɡɞɚɧɢɢ: BeginX, BeginY – Ʉɨɨɪɞɢɧɚɬɵ ɜɟɪɯɧɟɝɨ ɥɟɜɨɝɨ ɭɝɥɚ ɷɥɟɦɟɧɬɚ ɨɬɧɨɫɢɬɟɥɶɧɨ ɜɥɚɞɟɥɶɰɚ. SizeX, SizeY – Ƚɨɪɢɡɨɧɬɚɥɶɧɵɣ ɢ ɜɟɪɬɢɤɚɥɶɧɵɣ ɪɚɡɦɟɪɵ ɷɥɟɦɟɧɬɚ. Size – ɪɚɡɦɟɪ ɩɨɥɹ. Ɉɩɢɫɚɧɢɟ ɷɥɟɦɟɧɬɚ. ɗɥɟɦɟɧɬ StringVisible ɧɟ ɦɨɠɟɬ ɹɜɥɹɬɶɫɹ ɜɥɚɞɟɥɶɰɟɦ ɞɪɭɝɢɯ ɨɬɨɛɪɚɠɚɟɦɵɯ ɷɥɟɦɟɧɬɨɜ. ɗɬɨɬ ɷɥɟɦɟɧɬ ɞɨɥɠɟɧ ɛɵɬɶ ɫɜɹɡɚɧ ɫ ɩɟɪɟɦɟɧɧɨɣ ɬɢɩɚ String. Ⱦɥɹ ɭɫɬɚɧɨɜɥɟɧɢɹ ɫɜɹɡɢ ɢɫɩɨɥɶɡɭɟɬɫɹ ɮɭɧɤɰɢɹ Data ɫɨ ɫɬɪɨɤɨɜɵɦ ɷɥɟɦɟɧɬɨɦ ɜ ɤɚɱɟɫɬɜɟ ɩɟɪɜɨɝɨ ɩɚɪɚɦɟɬɪɚ ɢ ɚɞɪɟɫɨɦ ɩɟɪɟɦɟɧɧɨɣ ɜ ɤɚɱɟɫɬɜɟ ɜɬɨɪɨɝɨ ɩɚɪɚɦɟɬɪɚ. Ʉɚɤ ɩɪɚɜɢɥɨ, ɫɬɪɨɤɨɜɵɣ ɷɥɟɦɟɧɬ ɫɜɹɡɵɜɚɸɬ ɫ ɦɟɬɤɨɣ. Ⱦɥɹ ɨɪɝɚɧɢɡɚɰɢɢ ɫɜɹɡɢ ɦɟɬɤɢ ɫɨ ɫɬɪɨɤɨɜɵɦ ɷɥɟɦɟɧɬɨɦ ɧɟɨɛɯɨɞɢɦɨ ɜɵɡɜɚɬɶ ɮɭɧɤɰɢɸ Link, ɩɟɪɟɞɚɜ ɟɣ ɜ ɤɚɱɟɫɬɜɟ ɩɟɪɜɨɝɨ ɩɚɪɚɦɟɬɪɚ ɦɟɬɤɭ, ɚ ɜɬɨɪɵɦ ɩɚɪɚɦɟɬɪɨɦ ɫɬɪɨɤɨɜɵɣ ɷɥɟɦɟɧɬ. Ⱦɥɹ ɜɤɥɸɱɟɧɢɹ ɫɬɪɨɤɨɜɨɝɨ ɷɥɟɦɟɧɬɚ ɜ ɨɤɧɨ ɢɥɢ ɞɢɚɥɨɝ, ɧɟɨɛɯɨɞɢɦɨ ɜɵɡɜɚɬɶ ɮɭɧɤɰɢɸ Insert, ɫ ɨɤɧɨɦ ɢɥɢ ɞɢɚɥɨɝɨɦ ɜ ɤɚɱɟɫɬɜɟ ɩɟɪɜɨɝɨ ɩɚɪɚɦɟɬɪɚ ɢ ɫɬɪɨɤɨɜɵɦ ɷɥɟɦɟɧɬɨɦ ɜ ɤɚɱɟɫɬɜɟ ɜɬɨɪɨɝɨ ɩɚɪɚɦɟɬɪɚ. Ⱦɥɹ ɭɧɢɱɬɨɠɟɧɢɹ ɫɬɪɨɤɢ ɧɟɨɛɯɨɞɢɦɨ ɜɵɡɜɚɬɶ ɮɭɧɤɰɢɸ Erase, ɫ ɞɚɧɧɵɦ ɫɬɪɨɤɨɜɵɦ ɷɥɟɦɟɧɬɨɦ ɜ ɤɚɱɟɫɬɜɟ ɩɚɪɚɦɟɬɪɚ. ɉɚɪɚɦɟɬɪ Size ɡɚɞɚɟɬ ɦɚɤɫɢɦɚɥɶɧɵɣ ɪɚɡɦɟɪ ɜɜɨɞɢɦɨɣ ɫɬɪɨɤɢ ɜ ɫɢɦɜɨɥɚɯ. ɇɚɡɜɚɧɢɟ ɷɥɟɦɟɧɬɚ: RealVisible (LongVisible) (ɱɢɫɥɨɜɨɣ ɷɥɟɦɟɧɬ). ɉɚɪɚɦɟɬɪɵ ɩɪɢ ɫɨɡɞɚɧɢɢ: BeginX, BeginY – Ʉɨɨɪɞɢɧɚɬɵ ɜɟɪɯɧɟɝɨ ɥɟɜɨɝɨ ɭɝɥɚ ɷɥɟɦɟɧɬɚ ɨɬɧɨɫɢɬɟɥɶɧɨ ɜɥɚɞɟɥɶɰɚ. SizeX, SizeY – Ƚɨɪɢɡɨɧɬɚɥɶɧɵɣ ɢ ɜɟɪɬɢɤɚɥɶɧɵɣ ɪɚɡɦɟɪɵ ɷɥɟɦɟɧɬɚ. Min, Max – ɦɢɧɢɦɚɥɶɧɨɟ ɢ ɦɚɤɫɢɦɚɥɶɧɨɟ ɞɨɩɭɫɬɢɦɵɟ ɡɧɚɱɟɧɢɹ. Size – ɪɚɡɦɟɪ ɩɨɥɹ. Ɉɩɢɫɚɧɢɟ ɷɥɟɦɟɧɬɚ. ɗɥɟɦɟɧɬɵ RealVisible ɢ LongVisible ɫɥɭɠɚɬ ɞɥɹ ɜɜɨɞɚ ɞɟɣɫɬɜɢɬɟɥɶɧɵɯ ɢ ɞɥɢɧɧɵɯ ɰɟɥɵɯ ɱɢɫɟɥ, ɫɨɨɬɜɟɬɫɬɜɟɧɧɨ. Ʌɸɛɨɣ ɬɚɤɨɣ ɷɥɟɦɟɧɬ ɞɨɥɠɟɧ ɛɵɬɶ ɫɜɹɡɚɧ ɫ ɩɟɪɟɦɟɧɧɨɣ ɫɨɨɬɜɟɬɫɬɜɭɸɳɟɝɨ ɬɢɩɚ (Real ɢɥɢ Long) ɢ ɧɟ ɦɨɠɟɬ ɹɜɥɹɬɶɫɹ ɜɥɚɞɟɥɶɰɟɦ ɞɪɭɝɢɯ ɨɬɨɛɪɚɠɚɟɦɵɯ ɷɥɟɦɟɧɬɨɜ. Ⱦɥɹ ɭɫɬɚɧɨɜɥɟɧɢɹ ɫɜɹɡɢ ɢɫɩɨɥɶɡɭɟɬɫɹ ɮɭɧɤɰɢɹ Data ɫ ɱɢɫɥɨɜɵɦ ɷɥɟɦɟɧɬɨɦ ɜ ɤɚɱɟɫɬɜɟ ɩɟɪɜɨɝɨ ɩɚɪɚɦɟɬɪɚ ɢ ɚɞɪɟɫɨɦ ɩɟɪɟɦɟɧɧɨɣ ɜ ɤɚɱɟɫɬɜɟ ɜɬɨɪɨɝɨ ɩɚɪɚɦɟɬɪɚ. Ʉɚɤ ɩɪɚɜɢɥɨ, ɱɢɫɥɨɜɨɣ ɷɥɟɦɟɧɬ ɫɜɹɡɵɜɚɸɬ ɫ ɦɟɬɤɨɣ. Ⱦɥɹ ɨɪɝɚɧɢɡɚɰɢɢ ɫɜɹɡɢ ɦɟɬɤɢ ɫ ɱɢɫɥɨɜɵɦ ɷɥɟɦɟɧɬɨɦ ɧɟɨɛɯɨɞɢɦɨ ɜɵɡɜɚɬɶ ɮɭɧɤɰɢɸ Link, ɩɟɪɟɞɚɜ ɟɣ ɜ ɤɚɱɟɫɬɜɟ ɩɟɪɜɨɝɨ ɩɚɪɚɦɟɬɪɚ ɦɟɬɤɭ, ɚ ɜɬɨɪɵɦ ɩɚɪɚɦɟɬɪɨɦ ɱɢɫɥɨɜɨɣ ɷɥɟɦɟɧɬ. Ⱦɥɹ ɜɤɥɸɱɟɧɢɹ ɱɢɫɥɨɜɨɝɨ ɷɥɟɦɟɧɬɚ ɜ ɨɤɧɨ ɢɥɢ ɞɢɚɥɨɝ, ɧɟɨɛɯɨɞɢɦɨ ɜɵɡɜɚɬɶ ɮɭɧɤɰɢɸ Insert, ɫ ɨɤɧɨɦ ɢɥɢ ɞɢɚɥɨɝɨɦ ɜ ɤɚɱɟɫɬɜɟ ɩɟɪɜɨɝɨ ɩɚɪɚɦɟɬɪɚ ɢ ɱɢɫɥɨɜɵɦ ɷɥɟɦɟɧɬɨɦ ɜ ɤɚɱɟɫɬɜɟ ɜɬɨɪɨɝɨ ɩɚɪɚɦɟɬɪɚ. Ⱦɥɹ ɭɧɢɱɬɨɠɟɧɢɹ ɱɢɫɥɨɜɨɝɨ ɷɥɟɦɟɧɬɚ ɧɟɨɛɯɨɞɢɦɨ ɜɵɡɜɚɬɶ ɮɭɧɤɰɢɸ Erase, ɫ ɞɚɧɧɵɦ ɱɢɫɥɨɜɵɦ ɷɥɟɦɟɧɬɨɦ ɜ ɤɚɱɟɫɬɜɟ ɩɚɪɚɦɟɬɪɚ. ɇɚɡɜɚɧɢɟ ɷɥɟɦɟɧɬɚ: RadioButtons (ɩɟɪɟɤɥɸɱɚɬɟɥɢ). ɉɚɪɚɦɟɬɪɵ ɩɪɢ ɫɨɡɞɚɧɢɢ: BeginX, BeginY – Ʉɨɨɪɞɢɧɚɬɵ ɜɟɪɯɧɟɝɨ ɥɟɜɨɝɨ ɭɝɥɚ ɷɥɟɦɟɧɬɚ ɨɬɧɨɫɢɬɟɥɶɧɨ ɜɥɚɞɟɥɶɰɚ. SizeX, SizeY – Ƚɨɪɢɡɨɧɬɚɥɶɧɵɣ ɢ ɜɟɪɬɢɤɚɥɶɧɵɣ ɪɚɡɦɟɪɵ ɷɥɟɦɟɧɬɚ. Ɉɩɢɫɚɧɢɟ ɷɥɟɦɟɧɬɚ. ɗɥɟɦɟɧɬ RadioButtons ɫɥɭɠɢɬ ɞɥɹ ɡɚɞɚɧɢɹ ɡɧɚɱɟɧɢɹ ɩɚɪɚɦɟɬɪɚɦ, ɤɨɬɨɪɵɟ ɦɨɝɭɬ ɩɪɢɧɢɦɚɬɶ ɬɨɥɶɤɨ ɧɟɫɤɨɥɶɤɨ ɡɧɚɱɟɧɢɣ (ɧɚɩɪɢɦɟɪ, ɞɜɚ ɡɧɚɱɟɧɢɹ – ɢɫɬɢɧɚ ɢɥɢ ɥɨɠɶ). ɉɪɨɰɟɞɭɪɚ ɫɨɡɞɚɧɢɹ ɷɥɟɦɟɧɬɚ RadioButtons ɫɥɨɠɧɟɟ, ɱɟɦ ɞɥɹ ɪɚɧɟɟ ɪɚɫɫɦɨɬɪɟɧɧɵɯ ɩɪɨɰɟɞɭɪ. Ʉɪɨɦɟ ɜɵɡɨɜɚ ɮɭɧɤɰɢɢ RadioButtons, ɫɨɡɞɚɸɳɟɣ ɷɥɟɦɟɧɬ, ɧɟɨɛɯɨɞɢɦɨ ɧɟɫɤɨɥɶɤɨ ɪɚɡ ɜɵɡɜɚɬɶ ɮɭɧɤɰɢɸ AddItem, ɫ ɷɥɟɦɟɧɬɨɦ RadioButtons ɜ ɤɚɱɟɫɬɜɟ ɩɟɪɜɨɝɨ ɚɪɝɭɦɟɧɬɚ ɢ ɩɨɞɩɢɫɶɸ ɩɟɪɟɤɥɸɱɚɬɟɥɹ ɜ ɤɚɱɟɫɬɜɟ ɜɬɨɪɨɝɨ ɚɪɝɭɦɟɧɬɚ. ɗɥɟɦɟɧɬ RadioButtons ɞɨɥɠɟɧ ɛɵɬɶ ɫɜɹɡɚɧ ɫ ɩɟɪɟɦɟɧɧɨɣ ɬɢɩɚ Long ɢ ɧɟ ɦɨɠɟɬ ɹɜɥɹɬɶɫɹ ɜɥɚɞɟɥɶɰɟɦ ɞɪɭɝɢɯ ɨɬɨɛɪɚɠɚɟɦɵɯ ɷɥɟɦɟɧɬɨɜ. Ⱦɥɹ ɭɫɬɚɧɨɜɥɟɧɢɹ ɫɜɹɡɢ ɢɫɩɨɥɶɡɭɟɬɫɹ ɮɭɧɤɰɢɹ Data ɫ ɷɥɟɦɟɧɬɨɦ RadioButtons ɜ ɤɚɱɟɫɬɜɟ ɩɟɪɜɨɝɨ ɩɚɪɚɦɟɬɪɚ ɢ ɚɞɪɟɫɨɦ ɩɟɪɟɦɟɧɧɨɣ ɜ ɤɚɱɟɫɬɜɟ ɜɬɨɪɨɝɨ ɩɚɪɚɦɟɬɪɚ. ɗɥɟɦɟɧɬ RadioButtons ɢɧɬɟɪɩɪɟɬɢɪɭɟɬ ɞɚɧɧɵɟ, ɫɨɞɟɪɠɚɳɢɟɫɹ ɜ ɩɟɪɟɦɟɧɧɨɣ, ɫɥɟɞɭɸɳɢɦ ɨɛɪɚɡɨɦ: ɩɟɪɜɨɦɭ ɮɥɚɝɭ ɫɨɨɬɜɟɬɫɬɜɭɟɬ ɦɥɚɞɲɢɣ ɛɢɬ ɩɟɪɟɦɟɧɧɨɣ, ɜɬɨɪɨɦɭ ɫɥɟɞɭɸɳɢɣ ɩɨ ɫɬɚɪɲɢɧɫɬɜɭ ɢ ɬ. ɞ. ɗɥɟɦɟɧɬ ɧɟ ɦɨɠɟɬ ɜɤɥɸɱɚɬɶ ɛɨɥɟɟ 32 ɮɥɚɝɨɜ. Ȼɢɬɵ ɫ ɧɨɦɟɪɚɦɢ ɛɨɥɶɲɢɦɢ ɱɢɫɥɚ ɮɥɚɝɨɜ ɨɱɢɳɚɸɬɫɹ (ɡɚɦɟɧɹɸɬɫɹ ɧɭɥɹɦɢ). Ʉɚɤ ɩɪɚɜɢɥɨ, ɷɥɟɦɟɧɬ RadioButtons ɫɜɹɡɵɜɚɸɬ ɫ ɦɟɬɤɨɣ. Ⱦɥɹ ɨɪɝɚɧɢɡɚɰɢɢ ɫɜɹɡɢ ɦɟɬɤɢ ɫ ɷɥɟɦɟɧɬɨɦ RadioButtons ɧɟɨɛɯɨɞɢɦɨ ɜɵɡɜɚɬɶ ɮɭɧɤɰɢɸ Link, ɩɟɪɟɞɚɜ ɟɣ ɜ ɤɚɱɟɫɬɜɟ ɩɟɪɜɨɝɨ ɩɚɪɚɦɟɬɪɚ ɦɟɬɤɭ, ɚ ɜɬɨɪɵɦ ɩɚɪɚɦɟɬɪɨɦ ɷɥɟɦɟɧɬ RadioButtons. Ⱦɥɹ ɜɤɥɸɱɟɧɢɹ ɷɥɟɦɟɧɬɚ RadioButtons ɜ ɨɤɧɨ ɢɥɢ ɞɢɚɥɨɝ, ɧɟɨɛɯɨɞɢɦɨ ɜɵɡɜɚɬɶ ɮɭɧɤɰɢɸ Insert, ɫ ɨɤɧɨɦ ɢɥɢ ɞɢɚɥɨɝɨɦ ɜ ɤɚɱɟɫɬɜɟ ɩɟɪɜɨɝɨ ɩɚɪɚɦɟɬɪɚ ɢ ɷɥɟɦɟɧɬɨɦ
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RadioButtons ɜ ɤɚɱɟɫɬɜɟ ɜɬɨɪɨɝɨ ɩɚɪɚɦɟɬɪɚ. Ⱦɥɹ ɭɧɢɱɬɨɠɟɧɢɹ ɷɥɟɦɟɧɬɚ RadioButtons ɧɟɨɛɯɨɞɢɦɨ ɜɵɡɜɚɬɶ ɮɭɧɤɰɢɸ Erase, ɫ ɞɚɧɧɵɦ ɷɥɟɦɟɧɬɨɦ RadioButtons ɜ ɤɚɱɟɫɬɜɟ ɩɚɪɚɦɟɬɪɚ. ɇɚɡɜɚɧɢɟ ɷɥɟɦɟɧɬɚ: CheckBoxes (ɝɪɭɩɩɚ ɮɥɚɝɨɜ). ɉɚɪɚɦɟɬɪɵ ɩɪɢ ɫɨɡɞɚɧɢɢ: BeginX, BeginY – Ʉɨɨɪɞɢɧɚɬɵ ɜɟɪɯɧɟɝɨ ɥɟɜɨɝɨ ɭɝɥɚ ɷɥɟɦɟɧɬɚ ɨɬɧɨɫɢɬɟɥɶɧɨ ɜɥɚɞɟɥɶɰɚ. SizeX, SizeY – Ƚɨɪɢɡɨɧɬɚɥɶɧɵɣ ɢ ɜɟɪɬɢɤɚɥɶɧɵɣ ɪɚɡɦɟɪɵ ɷɥɟɦɟɧɬɚ. Ɉɩɢɫɚɧɢɟ ɷɥɟɦɟɧɬɚ. ɗɥɟɦɟɧɬ CheckBoxes ɫɥɭɠɢɬ ɞɥɹ ɡɚɞɚɧɢɹ ɡɧɚɱɟɧɢɹ ɩɚɪɚɦɟɬɪɚɦ, ɤɨɬɨɪɵɟ ɹɜɥɹɸɬɫɹ ɫɨɜɨɤɭɩɧɨɫɬɶɸ ɛɢɬɨɜɵɯ ɮɥɚɝɨɜ. ɉɪɨɰɟɞɭɪɚ ɫɨɡɞɚɧɢɹ ɝɪɭɩɩɵ ɮɥɚɝɨɜ ɚɧɚɥɨɝɢɱɧɚ ɫɨɡɞɚɧɢɸ ɷɥɟɦɟɧɬɚ RadioButtons. Ʉɪɨɦɟ ɜɵɡɨɜɚ ɮɭɧɤɰɢɢ CheckBoxes, ɫɨɡɞɚɸɳɟɣ ɷɥɟɦɟɧɬ, ɧɟɨɛɯɨɞɢɦɨ ɧɟɫɤɨɥɶɤɨ ɪɚɡ ɜɵɡɜɚɬɶ ɮɭɧɤɰɢɸ AddItem, ɫ ɷɥɟɦɟɧɬɨɦ CheckBoxes ɜ ɤɚɱɟɫɬɜɟ ɩɟɪɜɨɝɨ ɚɪɝɭɦɟɧɬɚ ɢ ɧɚɡɜɚɧɢɟɦ ɮɥɚɝɚ ɜ ɤɚɱɟɫɬɜɟ ɜɬɨɪɨɝɨ ɚɪɝɭɦɟɧɬɚ. ɗɥɟɦɟɧɬ CheckBoxes ɞɨɥɠɟɧ ɛɵɬɶ ɫɜɹɡɚɧ ɫ ɩɟɪɟɦɟɧɧɨɣ ɬɢɩɚ Long ɢ ɧɟ ɦɨɠɟɬ ɹɜɥɹɬɶɫɹ ɜɥɚɞɟɥɶɰɟɦ ɞɪɭɝɢɯ ɨɬɨɛɪɚɠɚɟɦɵɯ ɷɥɟɦɟɧɬɨɜ. Ⱦɥɹ ɭɫɬɚɧɨɜɥɟɧɢɹ ɫɜɹɡɢ ɢɫɩɨɥɶɡɭɟɬɫɹ ɮɭɧɤɰɢɹ Data ɫ ɷɥɟɦɟɧɬɨɦ CheckBoxes ɜ ɤɚɱɟɫɬɜɟ ɩɟɪɜɨɝɨ ɩɚɪɚɦɟɬɪɚ ɢ ɚɞɪɟɫɨɦ ɩɟɪɟɦɟɧɧɨɣ ɜ ɤɚɱɟɫɬɜɟ ɜɬɨɪɨɝɨ ɩɚɪɚɦɟɬɪɚ. ɗɥɟɦɟɧɬ CheckBoxes ɢɧɬɟɪɩɪɟɬɢɪɭɟɬ ɞɚɧɧɵɟ, ɫɨɞɟɪɠɚɳɢɟɫɹ ɜ ɩɟɪɟɦɟɧɧɨɣ, ɫɥɟɞɭɸɳɢɦ ɨɛɪɚɡɨɦ: ɟɫɥɢ ɡɧɚɱɟɧɢɟ ɩɟɪɟɦɟɧɧɨɣ ɪɚɜɧɨ ɟɞɢɧɢɰɟ, ɬɨ ɜɤɥɸɱɟɧ ɩɟɪɜɵɣ ɩɟɪɟɤɥɸɱɚɬɟɥɶ, ɟɫɥɢ ɞɜɭɦ – ɬɨ ɜɬɨɪɨɣ, ɬɪɟɦ – ɩɟɪɜɵɟ ɞɜɚ ɢ ɬ. ɞ. ȿɫɥɢ ɡɧɚɱɟɧɢɟ ɩɟɪɟɦɟɧɧɨɣ ɦɟɧɶɲɟ ɥɢɛɨ ɪɚɜɧɨ ɧɭɥɸ ɢɥɢ ɛɨɥɶɲɟ ɥɢɛɨ ɪɚɜɧɨ ɞɜɚ ɜ ɫɬɟɩɟɧɢ ɱɢɫɥɚ ɩɟɪɟɤɥɸɱɚɬɟɥɟɣ, ɬɨ ɨɧɨ ɡɚɦɟɧɹɟɬɫɹ ɧɚ ɟɞɢɧɢɰɭ. Ʉɚɤ ɩɪɚɜɢɥɨ, ɷɥɟɦɟɧɬ CheckBoxes ɫɜɹɡɵɜɚɸɬ ɫ ɦɟɬɤɨɣ. Ⱦɥɹ ɨɪɝɚɧɢɡɚɰɢɢ ɫɜɹɡɢ ɦɟɬɤɢ ɫ ɷɥɟɦɟɧɬɨɦ CheckBoxes ɧɟɨɛɯɨɞɢɦɨ ɜɵɡɜɚɬɶ ɮɭɧɤɰɢɸ Link, ɩɟɪɟɞɚɜ ɟɣ ɜ ɤɚɱɟɫɬɜɟ ɩɟɪɜɨɝɨ ɩɚɪɚɦɟɬɪɚ ɦɟɬɤɭ, ɚ ɜɬɨɪɵɦ ɩɚɪɚɦɟɬɪɨɦ ɷɥɟɦɟɧɬ CheckBoxes. Ⱦɥɹ ɜɤɥɸɱɟɧɢɹ ɝɪɭɩɩɵ ɮɥɚɝɨɜ ɜ ɨɤɧɨ ɢɥɢ ɞɢɚɥɨɝ, ɧɟɨɛɯɨɞɢɦɨ ɜɵɡɜɚɬɶ ɮɭɧɤɰɢɸ Insert, ɫ ɨɤɧɨɦ ɢɥɢ ɞɢɚɥɨɝɨɦ ɜ ɤɚɱɟɫɬɜɟ ɩɟɪɜɨɝɨ ɩɚɪɚɦɟɬɪɚ ɢ ɷɥɟɦɟɧɬɨɦ CheckBoxes ɜ ɤɚɱɟɫɬɜɟ ɜɬɨɪɨɝɨ ɩɚɪɚɦɟɬɪɚ. Ⱦɥɹ ɭɧɢɱɬɨɠɟɧɢɹ ɷɥɟɦɟɧɬɚ CheckBoxes ɧɟɨɛɯɨɞɢɦɨ ɜɵɡɜɚɬɶ ɮɭɧɤɰɢɸ Erase, ɫ ɞɚɧɧɵɦ ɷɥɟɦɟɧɬɨɦ CheckBoxes ɜ ɤɚɱɟɫɬɜɟ ɩɚɪɚɦɟɬɪɚ. ɇɚɡɜɚɧɢɟ ɷɥɟɦɟɧɬɚ: Button(ɤɧɨɩɤɚ). ɉɚɪɚɦɟɬɪɵ ɩɪɢ ɫɨɡɞɚɧɢɢ: BeginX, BeginY – Ʉɨɨɪɞɢɧɚɬɵ ɜɟɪɯɧɟɝɨ ɥɟɜɨɝɨ ɭɝɥɚ ɷɥɟɦɟɧɬɚ ɨɬɧɨɫɢɬɟɥɶɧɨ ɜɥɚɞɟɥɶɰɚ. SizeX, SizeY – Ƚɨɪɢɡɨɧɬɚɥɶɧɵɣ ɢ ɜɟɪɬɢɤɚɥɶɧɵɣ ɪɚɡɦɟɪɵ ɷɥɟɦɟɧɬɚ. Macro – Ⱥɞɪɟɫ ɮɭɧɤɰɢɢ, ɜɵɡɵɜɚɟɦɨɣ ɩɪɢ ɧɚɠɚɬɢɢ ɤɧɨɩɤɢ. ȼ ɡɚɜɢɫɢɦɨɫɬɢ ɨɬ ɪɟɚɥɢɡɚɰɢɢ ɩɨ ɷɬɨɦɭ ɚɞɪɟɫɭ ɦɨɠɟɬ ɥɟɠɚɬɶ ɥɢɛɨ ɧɚɱɚɥɨ ɦɚɲɢɧɧɨɝɨ ɤɨɞɚ ɮɭɧɤɰɢɢ, ɥɢɛɨ ɧɚɱɚɥɨ ɬɟɤɫɬɚ ɮɭɧɤɰɢɢ. ȼ ɫɥɭɱɚɟ ɩɟɪɟɞɚɱɢ ɬɟɤɫɬɚ ɮɭɧɤɰɢɢ ɩɟɪɜɵɟ ɜɨɫɟɦɶ ɛɚɣɬ ɩɨ ɩɟɪɟɞɚɧɧɨɦɭ ɚɞɪɟɫɭ ɫɨɞɟɪɠɚɬ ɫɥɨɜɨ «Function». Ɉɩɢɫɚɧɢɟ ɷɥɟɦɟɧɬɚ. Ʉɧɨɩɤɚ ɫɥɭɠɢɬ ɞɥɹ ɡɚɩɭɫɤɚ ɦɚɤɪɨɫɚ, ɤɨɬɨɪɵɣ ɜɵɩɨɥɧɹɟɬ ɧɟɤɨɬɨɪɵɟ ɞɟɣɫɬɜɢɹ, ɹɜɥɹɸɳɢɟɫɹ ɪɟɚɤɰɢɟɣ ɧɚ ɧɚɠɚɬɢɟ ɤɧɨɩɤɢ. Ʉɧɨɩɤɚ ɧɟ ɦɨɠɟɬ ɛɵɬɶ ɫɜɹɡɚɧɚ ɫ ɩɟɪɟɦɟɧɧɵɦɢ ɢ ɧɟ ɦɨɠɟɬ ɹɜɥɹɬɶɫɹ ɜɥɚɞɟɥɶɰɟɦ ɞɪɭɝɢɯ ɨɬɨɛɪɚɠɚɟɦɵɯ ɷɥɟɦɟɧɬɨɜ. Ⱦɥɹ ɜɤɥɸɱɟɧɢɹ ɤɧɨɩɤɢ ɷɥɟɦɟɧɬɚ ɜ ɨɤɧɨ ɢɥɢ ɞɢɚɥɨɝ, ɧɟɨɛɯɨɞɢɦɨ ɜɵɡɜɚɬɶ ɮɭɧɤɰɢɸ Insert, ɫ ɨɤɧɨɦ ɢɥɢ ɞɢɚɥɨɝɨɦ ɜ ɤɚɱɟɫɬɜɟ ɩɟɪɜɨɝɨ ɩɚɪɚɦɟɬɪɚ ɢ ɤɧɨɩɤɨɣ ɜ ɤɚɱɟɫɬɜɟ ɜɬɨɪɨɝɨ ɩɚɪɚɦɟɬɪɚ. Ⱦɥɹ ɭɧɢɱɬɨɠɟɧɢɹ ɤɧɨɩɤɢ ɧɟɨɛɯɨɞɢɦɨ ɜɵɡɜɚɬɶ ɮɭɧɤɰɢɸ Erase, ɫ ɞɚɧɧɨɣ ɤɧɨɩɤɨɣ ɜ ɤɚɱɟɫɬɜɟ ɩɚɪɚɦɟɬɪɚ.
2.6.4 ɉɟɪɟɱɟɧɶ ɢɧɬɟɪɮɟɣɫɧɵɯ ɮɭɧɤɰɢɣ ȼ ɞɚɧɧɨɦ ɪɚɡɞɟɥɟ ɞɚɧɨ ɨɩɢɫɚɧɢɟ ɜɫɟɯ ɢɧɬɟɪɮɟɣɫɧɵɯ ɮɭɧɤɰɢɣ. ɉɪɢɜɨɞɢɬɫɹ ɫɢɧɬɚɤɫɢɫ ɨɩɢɫɚɧɢɹ ɧɚ ɨɛɳɟɦ ɩɨɞɦɧɨɠɟɫɬɜɟ ɹɡɵɤɨɜ ɨɩɢɫɚɧɢɹ ɤɨɦɩɨɧɟɧɬɨɜ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚ. Ɏɭɧɤɰɢɢ ɩɪɢɜɟɞɟɧɵ ɜ ɚɥɮɚɜɢɬɧɨɦ ɩɨɪɹɞɤɟ. ɋɥɟɞɭɟɬ ɨɬɦɟɬɢɬɶ, ɱɬɨ, ɤɚɤ ɢ ɜ ɹɡɵɤɚɯ ɨɩɢɫɚɧɢɹ ɜɫɟɯ ɤɨɦɩɨɧɟɧɬɨɜ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚ ɜɫɟ ɚɪɝɭɦɟɧɬɵ ɩɟɪɟɞɚɸɬɫɹ ɮɭɧɤɰɢɹɦ ɩɨ ɫɫɵɥɤɟ (ɩɟɪɟɞɚɟɬɫɹ ɧɟ ɡɧɚɱɟɧɢɟ ɚɪɝɭɦɟɧɬɚ, ɚ ɟɝɨ ɚɞɪɟɫ). AddItem Function AddItem( Elem : Visible; Text : String ) : Logic; Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: Elem – Ɉɬɨɛɪɚɠɚɟɦɵɣ ɷɥɟɦɟɧɬ ɬɢɩɚ CheckBoxes ɢɥɢ RadioButtons. Text – ɇɚɡɜɚɧɢɟ ɩɟɪɟɤɥɸɱɚɬɟɥɹ ɢɥɢ ɮɥɚɝɚ. Ɉɩɢɫɚɧɢɟ ɮɭɧɤɰɢɢ: ɗɬɚ ɮɭɧɤɰɢɹ ɞɨɛɚɜɥɹɟɬ ɧɚɡɜɚɧɢɟ ɩɟɪɟɤɥɸɱɚɬɟɥɹ (ɟɫɥɢ ɩɟɪɜɵɣ ɚɪɝɭɦɟɧɬ ɬɢɩɚ RadioButtons) ɢɥɢ ɮɥɚɝɚ (CheckBoxes) ɤ ɫɩɢɫɤɭ ɷɥɟɦɟɧɬɚ, ɩɟɪɟɞɚɜɚɟɦɨɝɨ ɮɭɧɤɰɢɢ ɩɟɪɜɵɦ ɚɪɝɭɦɟɧɬɨɦ. ȿɫɥɢ ɩɟɪɜɵɣ ɷɥɟɦɟɧɬ ɧɟ ɹɜɥɹɟɬɫɹ ɷɥɟɦɟɧɬɨɦ ɬɢɩɚ CheckBoxes ɢɥɢ RadioButtons, ɬɨ ɮɭɧɤɰɢɹ ɜɨɡɜɪɚɳɚɟɬ ɡɧɚɱɟɧɢɟ ɥɨɠɶ (False). ȼ ɫɥɭɱɚɟ ɭɫɩɟɲɧɨɝɨ ɡɚɜɟɪɲɟɧɢɹ ɨɩɟɪɚɰɢɢ ɞɨɛɚɜɥɟɧɢɹ ɜ ɫɩɢɫɨɤ ɮɭɧɤɰɢɹ ɜɨɡɜɪɚɳɚɟɬ ɡɧɚɱɟɧɢɟ ɢɫɬɢɧɚ (True). ȼ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ ɜɨɡɜɪɚɳɚɟɬɫɹ ɡɧɚɱɟɧɢɟ ɥɨɠɶ (False).
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Button Function Button( BeginX, BeginY, SizeX, SizeY : Long; Macro : PString ) : Visible; Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: BeginX, BeginY – Ʉɨɨɪɞɢɧɚɬɵ ɜɟɪɯɧɟɝɨ ɥɟɜɨɝɨ ɭɝɥɚ ɷɥɟɦɟɧɬɚ ɨɬɧɨɫɢɬɟɥɶɧɨ ɜɥɚɞɟɥɶɰɚ. SizeX, SizeY – Ƚɨɪɢɡɨɧɬɚɥɶɧɵɣ ɢ ɜɟɪɬɢɤɚɥɶɧɵɣ ɪɚɡɦɟɪɵ ɷɥɟɦɟɧɬɚ. Macro – Ⱥɞɪɟɫ ɮɭɧɤɰɢɢ, ɜɵɡɵɜɚɟɦɨɣ ɩɪɢ ɧɚɠɚɬɢɢ ɤɧɨɩɤɢ. Ɉɩɢɫɚɧɢɟ ɮɭɧɤɰɢɢ: ɗɬɚ ɮɭɧɤɰɢɹ ɫɨɡɞɚɟɬ ɨɬɨɛɪɚɠɚɟɦɵɣ ɷɥɟɦɟɧɬ ɬɢɩɚ Button. ȿɫɥɢ ɫɨɡɞɚɧɢɟ ɩɪɨɲɥɨ ɭɫɩɟɲɧɨ, ɬɨ ɜɨɡɜɪɚɳɚɟɬɫɹ ɡɧɚɱɟɧɢɟ ɷɬɨɝɨ ɷɥɟɦɟɧɬɚ (ɬɢɩɵ ɡɧɚɱɟɧɢɣ ɧɟ ɨɝɨɜɚɪɢɜɚɸɬɫɹ ɫɬɚɧɞɚɪɬɨɦ, ɧɨ, ɤɚɤ ɩɪɚɜɢɥɨ, ɷɬɨ ɚɞɪɟɫ ɫɨɨɬɜɟɬɫɬɜɭɸɳɟɣ ɫɬɪɭɤɬɭɪɵ). ȿɫɥɢ ɫɨɡɞɚɧɢɟ ɷɥɟɦɟɧɬɚ ɡɚɜɟɪɲɢɥɨɫɶ ɧɟ ɭɞɚɱɧɨ, ɬɨ ɜɨɡɜɪɚɳɚɟɬɫɹ ɡɧɚɱɟɧɢɟ Null. CheckBoxes Function CheckBoxes( BeginX, BeginY, SizeX, SizeY : Long ) : Visible; Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: BeginX, BeginY – Ʉɨɨɪɞɢɧɚɬɵ ɜɟɪɯɧɟɝɨ ɥɟɜɨɝɨ ɭɝɥɚ ɷɥɟɦɟɧɬɚ ɨɬɧɨɫɢɬɟɥɶɧɨ ɜɥɚɞɟɥɶɰɚ. SizeX, SizeY – Ƚɨɪɢɡɨɧɬɚɥɶɧɵɣ ɢ ɜɟɪɬɢɤɚɥɶɧɵɣ ɪɚɡɦɟɪɵ ɷɥɟɦɟɧɬɚ. Ɉɩɢɫɚɧɢɟ ɮɭɧɤɰɢɢ: ɗɬɚ ɮɭɧɤɰɢɹ ɫɨɡɞɚɟɬ ɨɬɨɛɪɚɠɚɟɦɵɣ ɷɥɟɦɟɧɬ ɬɢɩɚ CheckBoxes ɫ ɩɭɫɬɵɦ ɫɩɢɫɤɨɦ ɩɟɪɟɤɥɸɱɚɬɟɥɟɣ. Ⱦɥɹ ɞɨɛɚɜɥɟɧɢɹ ɩɟɪɟɤɥɸɱɚɬɟɥɟɣ ɫɥɟɞɭɟɬ ɜɨɫɩɨɥɶɡɨɜɚɬɶɫɹ ɮɭɧɤɰɢɟɣ AddItem. ȿɫɥɢ ɫɨɡɞɚɧɢɟ ɩɪɨɲɥɨ ɭɫɩɟɲɧɨ, ɬɨ ɜɨɡɜɪɚɳɚɟɬɫɹ ɡɧɚɱɟɧɢɟ ɷɬɨɝɨ ɷɥɟɦɟɧɬɚ (ɬɢɩɵ ɡɧɚɱɟɧɢɣ ɧɟ ɨɝɨɜɚɪɢɜɚɸɬɫɹ ɫɬɚɧɞɚɪɬɨɦ, ɧɨ, ɤɚɤ ɩɪɚɜɢɥɨ, ɷɬɨ ɚɞɪɟɫ ɫɨɨɬɜɟɬɫɬɜɭɸɳɟɣ ɫɬɪɭɤɬɭɪɵ). ȿɫɥɢ ɫɨɡɞɚɧɢɟ ɷɥɟɦɟɧɬɚ ɡɚɜɟɪɲɢɥɨɫɶ ɧɟ ɭɞɚɱɧɨ, ɬɨ ɜɨɡɜɪɚɳɚɟɬɫɹ ɡɧɚɱɟɧɢɟ Null. Data Function Data( Element : Visible; Var Datum ) : Logic; Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: Element – Ɉɬɨɛɪɚɠɚɟɦɵɣ ɷɥɟɦɟɧɬ, ɤɨɬɨɪɵɣ ɫɜɹɡɵɜɚɟɬɫɹ ɫ ɩɟɪɟɦɟɧɧɨɣ. Datum – Ⱥɞɪɟɫ ɩɟɪɟɦɟɧɧɨɣ. Ɉɩɢɫɚɧɢɟ ɮɭɧɤɰɢɢ: ɗɬɚ ɮɭɧɤɰɢɹ ɫɜɹɡɵɜɚɟɬ ɨɬɨɛɪɚɠɚɟɦɵɣ ɷɥɟɦɟɧɬ (Element) ɫ ɩɟɪɟɦɧɧɨɣ Datum. ȿɫɥɢ ɷɥɟɦɟɧɬ Element ɧɟ ɞɨɩɭɫɤɚɟɬ ɭɫɬɚɧɨɜɥɟɧɢɹ ɫɜɹɡɢ ɫ ɩɟɪɟɦɟɧɧɨɣ, ɬɨ ɮɭɧɤɰɢɹ ɜɨɡɜɪɚɳɚɟɬ ɡɧɚɱɟɧɢɟ ɥɨɠɶ (False). ȼ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ ɨɧɚ ɭɫɬɚɧɚɜɥɢɜɚɟɬ ɫɜɹɡɶ ɦɟɠɞɭ ɷɥɟɦɟɧɬɨɦ ɢ ɩɟɪɟɦɟɧɧɨɣ ɢ ɜɨɡɜɪɚɳɚɟɬ ɡɧɚɱɟɧɢɟ ɢɫɬɢɧɚ (True). Ɉɬɦɟɬɢɦ, ɱɬɨ ɮɭɧɤɰɢɹ ɧɟ ɩɪɨɜɟɪɹɟɬ ɬɢɩɚ ɩɟɪɟɦɟɧɧɨɣ. ȿɫɥɢ ɜɦɟɫɬɨ ɚɞɪɟɫɚ ɩɟɪɟɦɟɧɧɨɣ ɬɢɩɚ ɞɥɢɧɧɨɟ ɰɟɥɨɟ ɛɵɥ ɞɚɧ ɚɞɪɟɫ ɩɟɪɟɦɟɧɧɨɣ ɞɟɣɫɬɜɢɬɟɥɶɧɨɝɨ ɬɢɩɚ, ɬɨ ɷɬɚ ɩɟɪɟɦɟɧɧɚɹ ɛɭɞɟɬ ɢɧɬɟɪɩɪɟɬɢɪɨɜɚɬɶɫɹ ɤɚɤ ɞɥɢɧɧɨɟ ɰɟɥɨɟ (ɫɦ. ɪɚɡɞ. «Ɏɭɧɤɰɢɢ ɩɪɢɜɟɞɟɧɢɹ ɬɢɩɨɜ»). ȼɚɠɧɨ ɨɬɦɟɬɢɬɶ, ɱɬɨ ɩɪɨɢɡɜɨɞɢɬɫɹ ɩɪɢɜɟɞɟɧɢɟ ɩɟɪɟɦɟɧɧɨɣ, ɚ ɧɟ ɩɪɟɨɛɪɚɡɨɜɚɧɢɟ ɟɟ ɡɧɚɱɟɧɢɹ. Delete Function Delete( Owner, Element : Visible) : Logic; Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: Owner – Ɉɬɨɛɪɚɠɚɟɦɵɣ ɷɥɟɦɟɧɬ ɬɢɩɚ ɨɤɧɨ ɢɥɢ ɞɢɚɥɨɝ, ɢɡ ɤɨɬɨɪɨɝɨ ɩɪɨɢɫɯɨɞɢɬ ɭɞɚɥɟɧɢɟ. Element – ɍɞɚɥɹɟɦɵɣ ɷɥɟɦɟɧɬ. Ɉɩɢɫɚɧɢɟ ɮɭɧɤɰɢɢ: ɗɬɚ ɮɭɧɤɰɢɹ ɭɞɚɥɹɟɬ ɨɬɨɛɪɚɠɚɟɦɵɣ ɷɥɟɦɟɧɬ (Element) ɢɡ ɟɝɨ ɜɥɚɞɟɥɶɰɚ (Owner). ȿɫɥɢ ɷɥɟɦɟɧɬ Owner ɧɟ ɹɜɥɹɟɬɫɹ ɨɤɧɨɦ ɢɥɢ ɞɢɚɥɨɝɨɦ, ɢɥɢ ɟɫɥɢ ɨɧ ɧɟ ɹɜɥɹɟɬɫɹ ɜɥɚɞɟɥɶɰɟɦ ɷɥɟɦɟɧɬɚ Element, ɬɨ ɮɭɧɤɰɢɹ ɜɨɡɜɪɚɳɚɟɬ ɡɧɚɱɟɧɢɟ ɥɨɠɶ (False). ȼ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ ɨɧɚ ɭɞɚɥɹɟɬ ɷɥɟɦɟɧɬ ɢɡ ɜɥɚɞɟɥɶɰɚ ɢ ɜɨɡɜɪɚɳɚɟɬ ɡɧɚɱɟɧɢɟ ɢɫɬɢɧɚ (True). Ɉɬɦɟɬɢɦ, ɱɬɨ ɷɥɟɦɟɧɬ ɭɞɚɥɹɟɬɫɹ, ɧɨ ɧɟ ɭɧɢɱɬɨɠɚɟɬɫɹ. ȿɫɥɢ ɧɟɬ ɩɟɪɟɦɟɧɧɨɣ, ɫɨɞɟɪɠɚɳɟɣ ɭɞɚɥɹɟɦɵɣ ɷɥɟɦɟɧɬ, ɬɨ ɷɥɟɦɟɧɬ «ɩɨɬɟɪɹɟɬɫɹ», ɬɨ ɟɫɬɶ ɨɧ ɫɬɚɧɟɬ ɧɟɞɨɫɬɭɩɧɵɦ ɢɡ ɩɪɨɝɪɚɦɦɵ, ɧɨ ɛɭɞɟɬ ɡɚɧɢɦɚɬɶ ɩɚɦɹɬɶ. Dialog Function Dialog( BeginX, BeginY, SizeX, SizeY, ScrollX, ScrollY : Long; Text : String ) : Visible; Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: BeginX, BeginY – Ʉɨɨɪɞɢɧɚɬɵ ɜɟɪɯɧɟɝɨ ɥɟɜɨɝɨ ɭɝɥɚ ɷɥɟɦɟɧɬɚ ɨɬɧɨɫɢɬɟɥɶɧɨ ɜɥɚɞɟɥɶɰɚ. SizeX, SizeY – Ƚɨɪɢɡɨɧɬɚɥɶɧɵɣ ɢ ɜɟɪɬɢɤɚɥɶɧɵɣ ɪɚɡɦɟɪɵ ɷɥɟɦɟɧɬɚ. ScrollX, ScrollY – ɐɟɥɨɱɢɫɥɟɧɧɵɟ ɩɚɪɚɦɟɬɪɵ, ɡɚɞɚɸɳɢɟ ɧɚɥɢɱɢɟ ɭ ɨɤɧɚ ɝɨɪɢɡɨɧɬɚɥɶɧɨɣ ɢ ɜɟɪɬɢɤɚɥɶɧɨɣ ɩɨɥɨɫɵ ɩɪɨɤɪɭɬɤɢ. ȿɫɥɢ ɡɧɚɱɟɧɢɟ ɩɚɪɚɦɟɬɪɚ ɪɚɜɧɨ ɧɭɥɸ, ɬɨ ɫɨɨɬɜɟɬɫɬɜɭɸɳɚɹ ɩɨɥɨɫɚ
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ɩɪɨɤɪɭɬɤɢ ɨɬɫɭɬɫɬɜɭɟɬ, ɩɪɢ ɥɸɛɨɦ ɞɪɭɝɨɦ ɡɧɚɱɟɧɢɢ ɩɚɪɚɦɟɬɪɚ ɜ ɨɤɧɨ ɜɤɥɸɱɚɟɬɫɹ ɫɨɨɬɜɟɬɫɬɜɭɸɳɚɹ ɩɨɥɨɫɚ ɩɪɨɤɪɭɬɤɢ. Text – ɇɚɡɜɚɧɢɟ ɨɤɧɚ. Ɉɩɢɫɚɧɢɟ ɮɭɧɤɰɢɢ: ɗɬɚ ɮɭɧɤɰɢɹ ɫɨɡɞɚɟɬ ɨɬɨɛɪɚɠɚɟɦɵɣ ɷɥɟɦɟɧɬ ɬɢɩɚ ɞɢɚɥɨɝ. ȿɫɥɢ ɫɨɡɞɚɧɢɟ ɩɪɨɲɥɨ ɭɫɩɟɲɧɨ, ɬɨ ɜɨɡɜɪɚɳɚɟɬɫɹ ɡɧɚɱɟɧɢɟ ɷɬɨɝɨ ɷɥɟɦɟɧɬɚ (ɬɢɩɵ ɡɧɚɱɟɧɢɣ ɧɟ ɨɝɨɜɚɪɢɜɚɸɬɫɹ ɫɬɚɧɞɚɪɬɨɦ, ɧɨ, ɤɚɤ ɩɪɚɜɢɥɨ, ɷɬɨ ɚɞɪɟɫ ɫɨɨɬɜɟɬɫɬɜɭɸɳɟɣ ɫɬɪɭɤɬɭɪɵ). ȿɫɥɢ ɫɨɡɞɚɧɢɟ ɷɥɟɦɟɧɬɚ ɡɚɜɟɪɲɢɥɨɫɶ ɧɟ ɭɞɚɱɧɨ, ɬɨ ɜɨɡɜɪɚɳɚɟɬɫɹ ɡɧɚɱɟɧɢɟ Null. ɉɨɫɥɟ ɫɨɡɞɚɧɢɹ ɞɢɚɥɨɝ ɹɜɥɹɟɬɫɹ ɩɭɫɬɵɦ. Erase Function Erase( Element : Visible ) : Logic; Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: Element – ɍɧɢɱɬɨɠɚɟɦɵɣ ɷɥɟɦɟɧɬ. Ɉɩɢɫɚɧɢɟ ɮɭɧɤɰɢɢ: ɗɬɚ ɮɭɧɤɰɢɹ ɭɧɢɱɬɨɠɚɟɬ ɨɬɨɛɪɚɠɚɟɦɵɣ ɷɥɟɦɟɧɬ (Element). ȿɫɥɢ ɚɪɝɭɦɟɧɬ Element ɹɜɥɹɟɬɫɹ ɨɤɧɨɦ ɢɥɢ ɞɢɚɥɨɝɨɦ, ɬɨ ɭɧɢɱɬɨɠɚɸɬɫɹ ɬɚɤ ɠɟ ɜɫɟ ɨɬɨɛɪɚɠɚɟɦɵɟ ɷɥɟɦɟɧɬɵ, ɞɥɹ ɤɨɬɨɪɵɯ ɷɥɟɦɟɧɬ Element ɹɜɥɹɟɬɫɹ ɜɥɚɞɟɥɶɰɟɦ. ȿɫɥɢ ɨɩɟɪɚɰɢɹ ɡɚɜɟɪɲɟɧɚ ɭɫɩɟɲɧɨ, ɬɨ ɮɭɧɤɰɢɹ ɜɨɡɜɪɚɳɚɟɬ ɡɧɚɱɟɧɢɟ ɢɫɬɢɧɚ (True). ȼ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ – ɡɧɚɱɟɧɢɟ ɥɨɠɶ (False). ȿɫɥɢ ɜɵɩɨɥɧɟɧɢɟ ɮɭɧɤɰɢɢ ɡɚɜɟɪɲɢɥɨɫɶ ɧɟɭɫɩɟɲɧɨ (ɮɭɧɤɰɢɹ ɜɟɪɧɭɥɚ ɡɧɚɱɟɧɢɟ ɥɨɠɶ), ɬɨ ɷɥɟɦɟɧɬ ɦɨɠɟɬ ɛɵɬɶ ɩɨɜɪɟɠɞɟɧ ɢ ɟɝɨ ɞɚɥɶɧɟɣɲɟɟ ɢɫɩɨɥɶɡɨɜɚɧɢɟ ɧɟ ɝɚɪɚɧɬɢɪɭɟɬ ɤɨɪɪɟɤɬɧɨɣ ɪɚɛɨɬɵ. Insert Function Insert( Owner, Element : Visible) : Logic; Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: Owner – Ɉɬɨɛɪɚɠɚɟɦɵɣ ɷɥɟɦɟɧɬ ɬɢɩɚ ɨɤɧɨ ɢɥɢ ɞɢɚɥɨɝ, ɜ ɤɨɬɨɪɵɣ ɩɪɨɢɡɜɨɞɢɬɫɹ ɜɫɬɚɜɤɚ. Element – ȼɫɬɚɜɥɹɟɦɵɣ ɷɥɟɦɟɧɬ. Ɉɩɢɫɚɧɢɟ ɮɭɧɤɰɢɢ: ɗɬɚ ɮɭɧɤɰɢɹ ɜɫɬɚɜɥɹɟɬ ɨɬɨɛɪɚɠɚɟɦɵɣ ɷɥɟɦɟɧɬ (Element) ɜ ɷɥɟɦɟɧɬ (Owner). ȿɫɥɢ ɷɥɟɦɟɧɬ Owner ɧɟ ɹɜɥɹɟɬɫɹ ɨɤɧɨɦ ɢɥɢ ɞɢɚɥɨɝɨɦ, ɢɥɢ ɟɫɥɢ Element ɹɜɥɹɟɬɫɹ ɞɢɚɥɨɝɨɦ, ɬɨ ɮɭɧɤɰɢɹ ɜɨɡɜɪɚɳɚɟɬ ɡɧɚɱɟɧɢɟ ɥɨɠɶ (False). Ɍɚɤɢɟ ɠɟ ɞɟɣɫɬɜɢɹ ɩɪɨɢɡɜɨɞɹɬɫɹ, ɜ ɫɥɭɱɚɟ, ɟɫɥɢ ɚɪɝɭɦɟɧɬ Owner ɫɨɜɩɚɞɚɟɬ ɫ MainVisible, ɚ Element ɧɟ ɹɜɥɹɟɬɫɹ ɨɤɧɨɦ ɢɥɢ ɞɢɚɥɨɝɨɦ. ȼ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ ɨɧɚ ɜɫɬɚɜɥɹɟɬ ɷɥɟɦɟɧɬ ɜ Owner ɢ ɜɨɡɜɪɚɳɚɟɬ ɡɧɚɱɟɧɢɟ ɢɫɬɢɧɚ (True). ȼɫɬɚɜɤɚ ɨɤɧɚ ɢɥɢ ɞɢɚɥɨɝɚ ɜ MainVisible ɜɵɡɵɜɚɟɬ ɨɬɨɛɪɚɠɟɧɢɟ ɟɝɨ ɧɚ ɷɤɪɚɧɟ, ɚ ɜ ɫɥɭɱɚɟ, ɟɫɥɢ ɜɫɬɚɜɥɹɟɬɫɹ ɞɢɚɥɨɝ, ɬɨ ɟɦɭ ɩɟɪɟɞɚɟɬɫɹ ɭɩɪɚɜɥɟɧɢɟ. Label Function Label( BeginX, BeginY, SizeX, SizeY : Long; Text : String ) : Visible; Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: BeginX, BeginY – Ʉɨɨɪɞɢɧɚɬɵ ɜɟɪɯɧɟɝɨ ɥɟɜɨɝɨ ɭɝɥɚ ɷɥɟɦɟɧɬɚ ɨɬɧɨɫɢɬɟɥɶɧɨ ɜɥɚɞɟɥɶɰɚ. SizeX, SizeY – Ƚɨɪɢɡɨɧɬɚɥɶɧɵɣ ɢ ɜɟɪɬɢɤɚɥɶɧɵɣ ɪɚɡɦɟɪɵ ɷɥɟɦɟɧɬɚ. Text – ɬɟɤɫɬ ɦɟɬɤɢ. Ɉɩɢɫɚɧɢɟ ɮɭɧɤɰɢɢ: ɗɬɚ ɮɭɧɤɰɢɹ ɫɨɡɞɚɟɬ ɨɬɨɛɪɚɠɚɟɦɵɣ ɷɥɟɦɟɧɬ ɬɢɩɚ Label. ȿɫɥɢ ɫɨɡɞɚɧɢɟ ɩɪɨɲɥɨ ɭɫɩɟɲɧɨ, ɬɨ ɜɨɡɜɪɚɳɚɟɬɫɹ ɡɧɚɱɟɧɢɟ ɷɬɨɝɨ ɷɥɟɦɟɧɬɚ (ɬɢɩɵ ɡɧɚɱɟɧɢɣ ɧɟ ɨɝɨɜɚɪɢɜɚɸɬɫɹ ɫɬɚɧɞɚɪɬɨɦ, ɧɨ, ɤɚɤ ɩɪɚɜɢɥɨ, ɷɬɨ ɚɞɪɟɫ ɫɨɨɬɜɟɬɫɬɜɭɸɳɟɣ ɫɬɪɭɤɬɭɪɵ). ȿɫɥɢ ɫɨɡɞɚɧɢɟ ɷɥɟɦɟɧɬɚ ɡɚɜɟɪɲɢɥɨɫɶ ɧɟ ɭɞɚɱɧɨ, ɬɨ ɜɨɡɜɪɚɳɚɟɬɫɹ ɡɧɚɱɟɧɢɟ Null. Link Function Link( Element, Labels : Visible) : Logic; Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: Owner – Ɉɬɨɛɪɚɠɚɟɦɵɣ ɷɥɟɦɟɧɬ, ɫɜɹɡɵɜɚɟɦɵɣ ɫ ɦɟɬɤɨɣ. Element – Ɉɬɨɛɪɚɠɚɟɦɵɣ ɷɥɟɦɟɧɬ – ɦɟɬɤɚ. Ɉɩɢɫɚɧɢɟ ɮɭɧɤɰɢɢ: ɗɬɚ ɮɭɧɤɰɢɹ ɭɫɬɚɧɚɜɥɢɜɚɟɬ ɫɜɹɡɶ ɦɟɠɞɭ ɦɟɬɤɨɣ Labels ɢ ɨɬɨɛɪɚɠɚɟɦɵɦ ɷɥɟɦɟɧɬɨɦ Element. ȿɫɥɢ ɷɥɟɦɟɧɬ Labels ɧɟ ɹɜɥɹɟɬɫɹ ɦɟɬɤɨɣ, ɬɨ ɮɭɧɤɰɢɹ ɜɨɡɜɪɚɳɚɟɬ ɡɧɚɱɟɧɢɟ ɥɨɠɶ (False). ȼ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ ɨɧɚ ɭɫɬɚɧɚɜɥɢɜɚɟɬ ɫɜɹɡɶ ɢ ɜɨɡɜɪɚɳɚɟɬ ɡɧɚɱɟɧɢɟ ɢɫɬɢɧɚ (True). LongVisible Function LongVisible( BeginX, BeginY, SizeX, SizeY, Min, Max, Size: Long ) : Visible; Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ:
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BeginX, BeginY – Ʉɨɨɪɞɢɧɚɬɵ ɜɟɪɯɧɟɝɨ ɥɟɜɨɝɨ ɭɝɥɚ ɷɥɟɦɟɧɬɚ ɨɬɧɨɫɢɬɟɥɶɧɨ ɜɥɚɞɟɥɶɰɚ. SizeX, SizeY – Ƚɨɪɢɡɨɧɬɚɥɶɧɵɣ ɢ ɜɟɪɬɢɤɚɥɶɧɵɣ ɪɚɡɦɟɪɵ ɷɥɟɦɟɧɬɚ. Min, Max – ɦɢɧɢɦɚɥɶɧɨɟ ɢ ɦɚɤɫɢɦɚɥɶɧɨɟ ɞɨɩɭɫɬɢɦɵɟ ɡɧɚɱɟɧɢɹ. Size – ɪɚɡɦɟɪ ɩɨɥɹ ɜ ɫɢɦɜɨɥɚɯ. Ɉɩɢɫɚɧɢɟ ɮɭɧɤɰɢɢ: ɗɬɚ ɮɭɧɤɰɢɹ ɫɨɡɞɚɟɬ ɨɬɨɛɪɚɠɚɟɦɵɣ ɷɥɟɦɟɧɬ ɬɢɩɚ LongVisible ɞɥɹ ɪɟɞɚɤɬɢɪɨɜɚɧɢɹ ɢ ɜɜɨɞɚ ɡɧɚɱɟɧɢɣ ɬɢɩɚ Long. ȿɫɥɢ ɫɨɡɞɚɧɢɟ ɩɪɨɲɥɨ ɭɫɩɟɲɧɨ, ɬɨ ɜɨɡɜɪɚɳɚɟɬɫɹ ɡɧɚɱɟɧɢɟ ɷɬɨɝɨ ɷɥɟɦɟɧɬɚ (ɬɢɩɵ ɡɧɚɱɟɧɢɣ ɧɟ ɨɝɨɜɚɪɢɜɚɸɬɫɹ ɫɬɚɧɞɚɪɬɨɦ, ɧɨ, ɤɚɤ ɩɪɚɜɢɥɨ, ɷɬɨ ɚɞɪɟɫ ɫɨɨɬɜɟɬɫɬɜɭɸɳɟɣ ɫɬɪɭɤɬɭɪɵ). ȿɫɥɢ ɫɨɡɞɚɧɢɟ ɷɥɟɦɟɧɬɚ ɡɚɜɟɪɲɢɥɨɫɶ ɧɟ ɭɞɚɱɧɨ, ɬɨ ɜɨɡɜɪɚɳɚɟɬɫɹ ɡɧɚɱɟɧɢɟ Null. RadioButtons Function RadioButtons( BeginX, BeginY, SizeX, SizeY : Long ) : Visible; Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: BeginX, BeginY – Ʉɨɨɪɞɢɧɚɬɵ ɜɟɪɯɧɟɝɨ ɥɟɜɨɝɨ ɭɝɥɚ ɷɥɟɦɟɧɬɚ ɨɬɧɨɫɢɬɟɥɶɧɨ ɜɥɚɞɟɥɶɰɚ. SizeX, SizeY – Ƚɨɪɢɡɨɧɬɚɥɶɧɵɣ ɢ ɜɟɪɬɢɤɚɥɶɧɵɣ ɪɚɡɦɟɪɵ ɷɥɟɦɟɧɬɚ. Ɉɩɢɫɚɧɢɟ ɮɭɧɤɰɢɢ: ɗɬɚ ɮɭɧɤɰɢɹ ɫɨɡɞɚɟɬ ɨɬɨɛɪɚɠɚɟɦɵɣ ɷɥɟɦɟɧɬ ɬɢɩɚ RadioButtons ɫ ɩɭɫɬɵɦ ɫɩɢɫɤɨɦ ɮɥɚɝɨɜ. Ⱦɥɹ ɞɨɛɚɜɥɟɧɢɹ ɩɟɪɟɤɥɸɱɚɬɟɥɟɣ ɫɥɟɞɭɟɬ ɜɨɫɩɨɥɶɡɨɜɚɬɶɫɹ ɮɭɧɤɰɢɟɣ AddItem. ȿɫɥɢ ɫɨɡɞɚɧɢɟ ɩɪɨɲɥɨ ɭɫɩɟɲɧɨ, ɬɨ ɜɨɡɜɪɚɳɚɟɬɫɹ ɡɧɚɱɟɧɢɟ ɷɬɨɝɨ ɷɥɟɦɟɧɬɚ (ɬɢɩɵ ɡɧɚɱɟɧɢɣ ɧɟ ɨɝɨɜɚɪɢɜɚɸɬɫɹ ɫɬɚɧɞɚɪɬɨɦ, ɧɨ, ɤɚɤ ɩɪɚɜɢɥɨ, ɷɬɨ ɚɞɪɟɫ ɫɨɨɬɜɟɬɫɬɜɭɸɳɟɣ ɫɬɪɭɤɬɭɪɵ). ȿɫɥɢ ɫɨɡɞɚɧɢɟ ɷɥɟɦɟɧɬɚ ɡɚɜɟɪɲɢɥɨɫɶ ɧɟ ɭɞɚɱɧɨ, ɬɨ ɜɨɡɜɪɚɳɚɟɬɫɹ ɡɧɚɱɟɧɢɟ Null. RealVisible Function RealVisible ( BeginX, BeginY, SizeX, SizeY : Long; Min, Max : Real; Size: Long ) : Visible; Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: BeginX, BeginY – Ʉɨɨɪɞɢɧɚɬɵ ɜɟɪɯɧɟɝɨ ɥɟɜɨɝɨ ɭɝɥɚ ɷɥɟɦɟɧɬɚ ɨɬɧɨɫɢɬɟɥɶɧɨ ɜɥɚɞɟɥɶɰɚ. SizeX, SizeY – Ƚɨɪɢɡɨɧɬɚɥɶɧɵɣ ɢ ɜɟɪɬɢɤɚɥɶɧɵɣ ɪɚɡɦɟɪɵ ɷɥɟɦɟɧɬɚ. Min, Max – ɦɢɧɢɦɚɥɶɧɨɟ ɢ ɦɚɤɫɢɦɚɥɶɧɨɟ ɞɨɩɭɫɬɢɦɵɟ ɡɧɚɱɟɧɢɹ. Size – ɪɚɡɦɟɪ ɩɨɥɹ ɜ ɫɢɦɜɨɥɚɯ. Ɉɩɢɫɚɧɢɟ ɮɭɧɤɰɢɢ: ɗɬɚ ɮɭɧɤɰɢɹ ɫɨɡɞɚɟɬ ɨɬɨɛɪɚɠɚɟɦɵɣ ɷɥɟɦɟɧɬ ɬɢɩɚ RealVisible ɞɥɹ ɪɟɞɚɤɬɢɪɨɜɚɧɢɹ ɢ ɜɜɨɞɚ ɡɧɚɱɟɧɢɣ ɬɢɩɚ Real. ȿɫɥɢ ɫɨɡɞɚɧɢɟ ɩɪɨɲɥɨ ɭɫɩɟɲɧɨ, ɬɨ ɜɨɡɜɪɚɳɚɟɬɫɹ ɡɧɚɱɟɧɢɟ ɷɬɨɝɨ ɷɥɟɦɟɧɬɚ (ɬɢɩɵ ɡɧɚɱɟɧɢɣ ɧɟ ɨɝɨɜɚɪɢɜɚɸɬɫɹ ɫɬɚɧɞɚɪɬɨɦ, ɧɨ, ɤɚɤ ɩɪɚɜɢɥɨ, ɷɬɨ ɚɞɪɟɫ ɫɨɨɬɜɟɬɫɬɜɭɸɳɟɣ ɫɬɪɭɤɬɭɪɵ). ȿɫɥɢ ɫɨɡɞɚɧɢɟ ɷɥɟɦɟɧɬɚ ɡɚɜɟɪɲɢɥɨɫɶ ɧɟ ɭɞɚɱɧɨ, ɬɨ ɜɨɡɜɪɚɳɚɟɬɫɹ ɡɧɚɱɟɧɢɟ Null. Refresh Function Refresh( Element : Visible ) : Logic; Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: Element – Ɉɬɨɛɪɚɠɚɟɦɵɣ ɷɥɟɦɟɧɬ. Ɉɩɢɫɚɧɢɟ ɮɭɧɤɰɢɢ: ɗɬɚ ɮɭɧɤɰɢɹ ɨɛɧɨɜɥɹɟɬ ɢɡɨɛɪɚɠɟɧɢɟ ɷɥɟɦɟɧɬɚ Element ɧɚ ɷɤɪɚɧɟ. ȿɫɥɢ ɨɩɟɪɚɰɢɹ ɩɪɨɲɥɚ ɭɫɩɟɲɧɨ, ɬɨ ɮɭɧɤɰɢɹ ɜɨɡɜɪɚɳɚɟɬ ɡɧɚɱɟɧɢɟ ɢɫɬɢɧɚ (True). ȼ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ ɨɧɚ ɜɨɡɜɪɚɳɚɟɬ ɡɧɚɱɟɧɢɟ ɥɨɠɶ (False). StringVisible Function StringVisible ( BeginX, BeginY, SizeX, SizeY, Size: Long ) : Visible; Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: BeginX, BeginY – Ʉɨɨɪɞɢɧɚɬɵ ɜɟɪɯɧɟɝɨ ɥɟɜɨɝɨ ɭɝɥɚ ɷɥɟɦɟɧɬɚ ɨɬɧɨɫɢɬɟɥɶɧɨ ɜɥɚɞɟɥɶɰɚ. SizeX, SizeY – Ƚɨɪɢɡɨɧɬɚɥɶɧɵɣ ɢ ɜɟɪɬɢɤɚɥɶɧɵɣ ɪɚɡɦɟɪɵ ɷɥɟɦɟɧɬɚ. Size – ɪɚɡɦɟɪ ɩɨɥɹ ɜ ɫɢɦɜɨɥɚɯ. Ɉɩɢɫɚɧɢɟ ɮɭɧɤɰɢɢ: ɗɬɚ ɮɭɧɤɰɢɹ ɫɨɡɞɚɟɬ ɨɬɨɛɪɚɠɚɟɦɵɣ ɷɥɟɦɟɧɬ ɬɢɩɚ StringVisible ɞɥɹ ɪɟɞɚɤɬɢɪɨɜɚɧɢɹ ɢ ɜɜɨɞɚ ɫɢɦɜɨɥɶɧɵɯ ɫɬɪɨɤ. ȿɫɥɢ ɫɨɡɞɚɧɢɟ ɩɪɨɲɥɨ ɭɫɩɟɲɧɨ, ɬɨ ɜɨɡɜɪɚɳɚɟɬɫɹ ɡɧɚɱɟɧɢɟ ɷɬɨɝɨ ɷɥɟɦɟɧɬɚ (ɬɢɩɵ ɡɧɚɱɟɧɢɣ ɧɟ ɨɝɨɜɚɪɢɜɚɸɬɫɹ ɫɬɚɧɞɚɪɬɨɦ, ɧɨ, ɤɚɤ ɩɪɚɜɢɥɨ, ɷɬɨ ɚɞɪɟɫ ɫɨɨɬɜɟɬɫɬɜɭɸɳɟɣ ɫɬɪɭɤɬɭɪɵ). ȿɫɥɢ ɫɨɡɞɚɧɢɟ ɷɥɟɦɟɧɬɚ ɡɚɜɟɪɲɢɥɨɫɶ ɧɟ ɭɞɚɱɧɨ, ɬɨ ɜɨɡɜɪɚɳɚɟɬɫɹ ɡɧɚɱɟɧɢɟ Null. Window Function Window( BeginX, BeginY, SizeX, SizeY, ScrollX, ScrollY : Long; Text : String ) : Visible; Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ:
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BeginX, BeginY – Ʉɨɨɪɞɢɧɚɬɵ ɜɟɪɯɧɟɝɨ ɥɟɜɨɝɨ ɭɝɥɚ ɷɥɟɦɟɧɬɚ ɨɬɧɨɫɢɬɟɥɶɧɨ ɜɥɚɞɟɥɶɰɚ. SizeX, SizeY – Ƚɨɪɢɡɨɧɬɚɥɶɧɵɣ ɢ ɜɟɪɬɢɤɚɥɶɧɵɣ ɪɚɡɦɟɪɵ ɷɥɟɦɟɧɬɚ. ScrollX, ScrollY – ɐɟɥɨɱɢɫɥɟɧɧɵɟ ɩɚɪɚɦɟɬɪɵ, ɡɚɞɚɸɳɢɟ ɧɚɥɢɱɢɟ ɭ ɨɤɧɚ ɝɨɪɢɡɨɧɬɚɥɶɧɨɣ ɢ ɜɟɪɬɢɤɚɥɶɧɨɣ ɩɨɥɨɫɵ ɩɪɨɤɪɭɬɤɢ. ȿɫɥɢ ɡɧɚɱɟɧɢɟ ɩɚɪɚɦɟɬɪɚ ɪɚɜɧɨ ɧɭɥɸ, ɬɨ ɫɨɨɬɜɟɬɫɬɜɭɸɳɚɹ ɩɨɥɨɫɚ ɩɪɨɤɪɭɬɤɢ ɨɬɫɭɬɫɬɜɭɟɬ, ɩɪɢ ɥɸɛɨɦ ɞɪɭɝɨɦ ɡɧɚɱɟɧɢɢ ɩɚɪɚɦɟɬɪɚ ɜ ɨɤɧɨ ɜɤɥɸɱɚɟɬɫɹ ɫɨɨɬɜɟɬɫɬɜɭɸɳɚɹ ɩɨɥɨɫɚ ɩɪɨɤɪɭɬɤɢ. Text – ɇɚɡɜɚɧɢɟ ɨɤɧɚ. Ɉɩɢɫɚɧɢɟ ɮɭɧɤɰɢɢ: ɗɬɚ ɮɭɧɤɰɢɹ ɫɨɡɞɚɟɬ ɨɬɨɛɪɚɠɚɟɦɵɣ ɷɥɟɦɟɧɬ ɬɢɩɚ ɨɤɧɨ. ȿɫɥɢ ɫɨɡɞɚɧɢɟ ɩɪɨɲɥɨ ɭɫɩɟɲɧɨ, ɬɨ ɜɨɡɜɪɚɳɚɟɬɫɹ ɡɧɚɱɟɧɢɟ ɷɬɨɝɨ ɷɥɟɦɟɧɬɚ (ɬɢɩɵ ɡɧɚɱɟɧɢɣ ɧɟ ɨɝɨɜɚɪɢɜɚɸɬɫɹ ɫɬɚɧɞɚɪɬɨɦ, ɧɨ, ɤɚɤ ɩɪɚɜɢɥɨ, ɷɬɨ ɚɞɪɟɫ ɫɨɨɬɜɟɬɫɬɜɭɸɳɟɣ ɫɬɪɭɤɬɭɪɵ). ȿɫɥɢ ɫɨɡɞɚɧɢɟ ɷɥɟɦɟɧɬɚ ɡɚɜɟɪɲɢɥɨɫɶ ɧɟ ɭɞɚɱɧɨ, ɬɨ ɜɨɡɜɪɚɳɚɟɬɫɹ ɡɧɚɱɟɧɢɟ Null. ɉɨɫɥɟ ɫɨɡɞɚɧɢɹ ɨɤɧɨ ɹɜɥɹɟɬɫɹ ɩɭɫɬɵɦ.
2.7 ɋɬɪɨɤɨɜɵɟ ɮɭɧɤɰɢɢ ȼ ɷɬɨɦ ɪɚɡɞɟɥɟ ɨɩɢɫɚɧ ɧɚɛɨɪ ɮɭɧɤɰɢɣ ɞɥɹ ɪɚɛɨɬɵ ɫɨ ɫɬɪɨɤɚɦɢ, ɤɨɬɨɪɵɟ ɦɨɝɭɬ ɢɫɩɨɥɶɡɨɜɚɬɶɫɹ ɜ ɹɡɵɤɚɯ ɨɩɢɫɚɧɢɹ ɜɫɟɯ ɤɨɦɩɨɧɟɧɬɨɜ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚ. Function SubStr( S : String; Origin, Leng : Integer ) : String; Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ S – ɫɬɪɨɤɚ, ɢɡ ɤɨɬɨɪɨɣ ɜɵɞɟɥɹɟɬɫɹ ɮɪɚɝɦɟɧɬ. Origin – ɧɚɱɚɥɶɧɚɹ ɩɨɡɢɰɢɹ ɜɵɞɟɥɹɟɦɨɝɨ ɮɪɚɝɦɟɧɬɚ ɜ ɫɬɪɨɤɟ S Leng – ɞɥɢɧɚ ɜɵɞɟɥɹɟɦɨɝɨ ɮɪɚɝɦɟɧɬɚ. ȼɵɞɟɥɹɟɬ ɢɡ ɫɬɪɨɤɢ S ɮɪɚɝɦɟɧɬ, ɧɚɱɢɧɚɸɳɢɣɫɹ ɫ ɩɨɡɢɰɢɢ Origin ɢ ɞɥɢɧɨɣ Leng ɫɢɦɜɨɥɨɜ. ȿɫɥɢ ɫɬɪɨɤɚ ɤɨɪɨɱɟ ɱɟɦ Origin, ɬɨ ɪɟɡɭɥɶɬɚɬɨɦ ɹɜɥɹɟɬɫɹ ɩɭɫɬɚɹ ɫɬɪɨɤɚ. ȿɫɥɢ ɫɬɪɨɤɚ ɞɥɢɧɧɟɟ ɱɟɦ Origin ɫɢɦɜɨɥɨɜ, ɧɨ ɤɨɪɨɱɟ ɱɟɦ Origin+Leng ɫɢɦɜɨɥɨɜ, ɬɨ ɪɟɡɭɥɶɬɚɬɨɦ ɹɜɥɹɟɬɫɹ ɮɪɚɝɦɟɧɬ ɫɬɪɨɤɢ S ɫ ɫɢɦɜɨɥɚ Origin ɢ ɞɨ ɤɨɧɰɚ ɫɬɪɨɤɢ S. Function Pos( S1, S2 : String ) : Integer Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ S1 – ɫɬɪɨɤɚ, ɜ ɤɨɬɨɪɨɣ ɢɳɟɬɫɹ ɜɯɨɠɞɟɧɢɟ ɫɬɪɨɤɢ S2. S2 – ɫɬɪɨɤɚ, ɜɯɨɠɞɟɧɢɟ ɤɨɬɨɪɨɣ ɢɳɟɬɫɹ. Ɏɭɧɤɰɢɹ Pos ɜɨɡɜɪɚɳɚɟɬ ɧɨɦɟɪ ɩɟɪɜɨɝɨ ɫɢɦɜɨɥɚ ɜ ɫɬɪɨɤɟ S1, ɧɚɱɢɧɚɹ ɫ ɤɨɬɨɪɨɝɨ, ɜ ɫɬɪɨɤɟ S1 ɩɨɥɧɨɫɬɶɸ ɫɨɞɟɪɠɢɬɫɹ ɫɬɪɨɤɚ S2. ȿɫɥɢ ɫɬɪɨɤɚ S2 ɧɢ ɪɚɡɭ ɧɟ ɜɫɬɪɟɬɢɥɚɫɶ ɜ ɫɬɪɨɤɟ S1, ɬɨ ɪɟɡɭɥɶɬɚɬ ɪɚɜɟɧ ɧɭɥɸ. Function Len( S : String ) : Integer Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ S – ɫɬɪɨɤɚ, ɞɥɢɧɚ ɤɨɬɨɪɨɣ ɜɵɱɢɫɥɹɟɬɫɹ. Ɏɭɧɤɰɢɹ Len ɜɨɡɜɪɚɳɚɟɬ ɞɥɢɧɭ (ɱɢɫɥɨ ɫɢɦɜɨɥɨɜ) ɫɬɪɨɤɢ S
2.8 Ɉɩɢɫɚɧɢɟ ɹɡɵɤɚ ɨɩɢɫɚɧɢɹ ɤɨɦɩɨɧɟɧɬɨɜ ȼ ɬɚɛɥ. 12 ɩɪɢɜɟɞɟɧ ɫɩɢɫɨɤ ɤɥɸɱɟɜɵɯ ɫɥɨɜ, ɨɛɳɢɯ ɞɥɹ ɜɫɟɯ ɹɡɵɤɨɜ ɨɩɢɫɚɧɢɹ ɤɨɦɩɨɧɟɧɬɨɜ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚ. Ʉɪɨɦɟ ɬɨɝɨ, ɤ ɤɥɸɱɟɜɵɦ ɫɥɨɜɚɦ ɨɬɧɨɫɹɬɫɹ ɬɢɩɵ ɞɚɧɧɵɯ, ɩɪɢɜɟɞɟɧɧɵɟ ɜ ɬɚɛɥ. 1; ɨɛɨɡɧɚɱɟɧɢɹ ɨɩɟɪɚɰɢɣ, ɩɪɢɜɟɞɟɧɧɵɟ ɜ ɬɚɛɥ. 3, 7, 8, 9, 10; ɧɚɡɜɚɧɢɹ ɮɭɧɤɰɢɣ ɩɪɟɨɛɪɚɡɨɜɚɧɢɹ (ɬɚɛɥ. 5) ɢ ɩɪɢɜɟɞɟɧɢɹ ɬɢɩɨɜ (ɬɚɛɥ. 6); ɢɞɟɧɬɢɮɢɤɚɬɨɪɵ ɩɪɟɞɨɩɪɟɞɟɥɟɧɧɵɯ ɤɨɧɫɬɚɧɬ, ɩɪɢɜɟɞɟɧɧɵɟ ɜ ɬɚɛɥ. 11; ɢɦɟɧɚ ɢɧɬɟɪɮɟɣɫɧɵɯ ɮɭɧɤɰɢɣ, ɩɪɢɜɟɞɟɧɧɵɯ ɜ ɪɚɡɞɟɥɟ «ɉɟɪɟɱɟɧɶ ɢɧɬɟɪɮɟɣɫɧɵɯ ɮɭɧɤɰɢɣ»; ɨɛɨɡɧɚɱɟɧɢɹ ɷɥɟɦɟɧɬɚɪɧɵɯ ɮɭɧɤɰɢɣ, ɩɪɢɜɟɞɟɧɧɵɯ ɜ ɬɚɛɥ.13; ɨɛɨɡɧɚɱɟɧɢɹ ɫɬɪɨɤɨɜɵɯ ɮɭɧɤɰɢɣ, ɩɪɢɜɟɞɟɧɧɵɯ ɜ ɪɚɡɞɟɥɟ «ɋɬɪɨɤɨɜɵɟ ɮɭɧɤɰɢɢ» ɢ ɨɛɨɡɧɚɱɟɧɢɹ ɮɭɧɤɰɢɣ ɭɩɪɚɜɥɟɧɢɹ ɩɚɦɹɬɶɸ ɢɡ ɪɚɡɞɟɥɚ «ɮɭɧɤɰɢɢ ɭɩɪɚɜɥɟɧɢɹ ɩɚɦɹɬɶɸ».
2.8.1 ɉɟɪɟɞɚɱɚ ɚɪɝɭɦɟɧɬɨɜ ɮɭɧɤɰɢɹɦ ȼɨ ɜɫɟɯ ɹɡɵɤɚɯ ɨɩɢɫɚɧɢɹ ɤɨɦɩɨɧɟɧɬɨɜ ɜɫɟ ɩɚɪɚɦɟɬɪɵ ɩɟɪɟɞɚɸɬɫɹ ɩɨ ɫɫɵɥɤɟ (ɩɟɪɟɞɚɟɬɫɹ ɧɟ ɡɧɚɱɟɧɢɟ ɚɪɝɭɦɟɧɬɚ, ɚ ɟɝɨ ɚɞɪɟɫ). ȿɫɥɢ ɜ ɤɚɱɟɫɬɜɟ ɮɚɤɬɢɱɟɫɤɨɝɨ ɚɪɝɭɦɟɧɬɚ ɭɤɚɡɚɧɨ ɜɵɪɚɠɟɧɢɟ, ɬɨ ɡɧɚɱɟɧɢɟ ɜɵɪɚɠɟɧɢɹ ɩɨɦɟɳɚɟɬɫɹ ɢɧɬɟɪɩɪɟɬɚɬɨɪɨɦ (ɢɥɢ ɤɨɦɩɢɥɹɬɨɪɨɦ) ɜɨ ɜɪɟɦɟɧɧɭɸ ɩɟɪɟɦɟɧɧɭɸ, ɢɦɟɸɳɭɸ ɬɢɩ, ɫɨɜɩɚɞɚɸɳɢɣ ɫ ɬɢɩɨɦ ɮɨɪɦɚɥɶɧɨɝɨ ɚɪɝɭɦɟɧɬɚ, ɚ ɚɞɪɟɫ ɜɪɟɦɟɧɧɨɣ ɩɟɪɟɦɟɧɧɨɣ ɩɟɪɟɞɚɟɬɫɹ ɜ ɤɚɱɟɫɬɜɟ ɮɚɤɬɢɱɟɫɤɨɝɨ ɚɪɝɭɦɟɧɬɚ.
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Ɍɚɛɥɢɰɚ 12. Ʉɥɸɱɟɜɵɟ ɫɥɨɜɚ, ɨɛɳɢɟ ɞɥɹ ɜɫɟɯ ɹɡɵɤɨɜ ɨɩɢɫɚɧɢɹ ɤɨɦɩɨɧɟɧɬ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚ. Ʉɥɸɱɟɜɨɟ ɫɥɨɜɨɄɪɚɬɤɨɟ ɨɩɢɫɚɧɢɟ Begin ɇɚɱɚɥɨ ɨɩɢɫɚɧɢɹ ɬɟɥɚ ɩɪɨɰɟɞɭɪɵ, ɢɥɢ ɨɩɟɪɚɬɨɪɧɵɯ ɫɤɨɛɨɤ. By ɑɚɫɬɶ ɨɩɟɪɚɬɨɪɚ ɰɢɤɥɚ ɫ ɲɚɝɨɦ. ɉɪɟɞɲɟɫɬɜɭɟɬ ɲɚɝɭ ɰɢɤɥɚ. Do Ɂɚɜɟɪɲɚɸɳɚɹ ɱɚɫɬɶ ɨɩɟɪɚɬɨɪɨɜ ɰɢɤɥɚ. Else ɑɚɫɬɶ ɭɫɥɨɜɧɨɝɨ ɨɩɟɪɚɬɨɪɚ. ɉɪɟɞɲɟɫɬɜɭɟɬ ɨɩɟɪɚɬɨɪɭ, ɜɵɩɨɥɧɹɟɦɨɦɭ, ɟɫɥɢ ɭɫɥɨɜɢɟ ɥɨɠɧɨ. End Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɬɟɥɚ ɩɪɨɰɟɞɭɪɵ ɢɥɢ ɨɩɟɪɚɬɨɪɧɵɯ ɫɤɨɛɨɤ. For Ɂɚɝɨɥɨɜɨɤ ɨɩɟɪɚɬɨɪɚ ɰɢɤɥɚ ɫ ɲɚɝɨɦ. Function Ɂɚɝɨɥɨɜɨɤ ɨɩɢɫɚɧɢɹ ɮɭɧɤɰɢɢ. Global ɇɚɱɥɨ ɛɥɨɤɚ ɨɩɢɫɚɧɢɹ ɝɥɨɛɚɥɶɧɵɯ ɩɟɪɟɦɟɧɧɵɯ. GoTo ɇɚɱɚɥɨ ɨɩɟɪɚɬɨɪɚ ɩɟɪɟɯɨɞɚ. If ɇɚɱɚɥɨ ɭɫɥɨɜɧɨɝɨ ɨɩɟɪɚɬɨɪɚ. Include ɉɪɟɞɲɟɫɬɜɭɟɬ ɢɦɟɧɢ ɮɚɣɥɚ, ɰɟɥɢɤɨɦ ɜɫɬɚɜɥɹɟɦɨɝɨ ɜ ɷɬɨ ɦɟɫɬɨ ɨɩɢɫɚɧɢɹ. Label ɇɚɱɚɥɨ ɨɩɢɫɚɧɢɹ ɦɟɬɨɤ Name ɉɪɟɞɲɟɫɬɜɭɟɬ ɢɦɟɧɢ ɫɬɚɬɢɱɟɫɤɨɣ ɩɟɪɟɦɟɧɧɨɣ. SetParameters ɉɪɢɡɧɚɤ ɪɚɡɞɟɥɚ ɭɫɬɚɧɨɜɥɟɧɢɹ ɡɧɚɱɟɧɢɣ ɩɚɪɚɦɟɬɪɨɜ. Static ɇɚɱɥɨ ɛɥɨɤɚ ɨɩɢɫɚɧɢɹ ɫɬɚɬɢɱɟɫɤɢɯ ɩɟɪɟɦɟɧɧɵɯ. Then ɑɚɫɬɶ ɭɫɥɨɜɧɨɝɨ ɨɩɟɪɚɬɨɪɚ, ɩɪɟɞɲɟɫɬɜɭɸɳɚɹ ɨɩɟɪɚɬɨɪɭ, ɜɵɩɨɥɧɹɟɦɨɦɭ, ɟɫɥɢ ɭɫɥɨɜɢɟ ɢɫɬɢɧɧɨ. To ɑɚɫɬɶ ɨɩɟɪɚɬɨɪɚ ɰɢɤɥɚ ɫ ɲɚɝɨɦ. ɉɪɟɞɲɟɫɬɜɭɟɬ ɜɟɪɯɧɟɣ ɝɪɚɧɢɰɟ ɰɢɤɥɚ. Var ɇɚɱɥɨ ɛɥɨɤɚ ɨɩɢɫɚɧɢɹ ɩɟɪɟɦɟɧɧɵɯ. While Ɂɚɝɨɥɨɜɨɤ ɨɩɟɪɚɬɨɪɚ ɰɢɤɥɚ ɩɨ ɭɫɥɨɜɢɸ. Ɍɚɛɥɢɰɚ 13 ɗɥɟɦɟɧɬɚɪɧɵɟ ɮɭɧɤɰɢɢ, ɞɨɩɭɫɬɢɦɵɟ ɜ ɹɡɵɤɚɯ ɨɩɢɫɚɧɢɹ ɤɨɦɩɨɧɟɧɬ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚ ɈɛɨɡɧɚɱɟɧɢɟɁɧɚɱɟɧɢɟɈɛɨɡɧɚɱɟɧɢɟɁɧɚɱɟɧɢɟ Sin ɋɢɧɭɫ Cos Ʉɨɫɢɧɭɫ Tan Ɍɚɧɝɟɧɫ Atan Ⱥɪɤɬɚɧɝɟɧɫ Sh Ƚɢɩɟɪɛɨɥɢɱɟɫɤɢɣ ɫɢɧɭɫ Ch Ƚɢɩɟɪɛɨɥɢɱɟɫɤɢɣ ɤɨɫɢɧɭɫ Th Ƚɢɩɟɪɛɨɥɢɱɟɫɤɢɣ ɬɚɧɝɟɧɫ Lg Ʌɨɝɚɪɢɮɦ ɞɜɨɢɱɧɵɣ Ln Ʌɨɝɚɪɢɮɦ ɧɚɬɭɪɚɥɶɧɵɣ Exp ɗɤɫɩɨɧɟɧɬɚ Sqrt Ʉɜɚɞɪɚɬɧɵɣ ɤɨɪɟɧɶ Sqr Ʉɜɚɞɪɚɬ Abs Ⱥɛɫɨɥɸɟɧɨɟ ɡɧɚɱɟɧɢɟ Sign Ɂɧɚɤ ɚɪɝɭɦɟɧɬɚ (0 – ɦɢɧɭɫ)
2.8.2 ɂɦɟɧɚ ɫɬɪɭɤɬɭɪɧɵɯ ɟɞɢɧɢɰ ɤɨɦɩɨɧɟɧɬɨɜ Ʉɨɦɩɨɧɟɧɬɵ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ, ɫɟɬɶ, ɨɰɟɧɤɚ ɢ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɨɬɜɟɬɚ ɢɦɟɸɬ ɢɟɪɚɪɯɢɱɟɫɤɭɸ ɫɬɪɭɤɬɭɪɭ. ɑɚɫɬɶ ɡɚɩɪɨɫɨɜ ɦɨɠɟɬ ɛɵɬɶ ɚɞɪɟɫɨɜɚɧɚ ɧɟ ɜɫɟɦɭ ɤɨɦɩɨɧɟɧɬɭ, ɚ ɟɝɨ ɫɬɪɭɤɬɭɪɧɨɣ ɟɞɢɧɢɰɟ ɥɸɛɨɝɨ ɭɪɨɜɧɹ. Ⱦɥɹ ɬɨɱɧɨɝɨ ɭɤɚɡɚɧɢɹ ɚɞɪɟɫɚɬɚ ɡɚɩɪɨɫɚ ɢɫɩɨɥɶɡɭɟɬɫɹ ɩɨɥɧɨɟ ɢɦɹ ɫɬɪɭɤɬɭɪɧɨɣ ɟɞɢɧɢɰɵ, ɤɨɬɨɪɨɟ ɫɬɪɨɢɬɫɹ ɩɨ ɫɥɟɞɭɸɳɟɦɭ ɩɪɚɜɢɥɭ: 1. ɂɦɹ ɤɨɦɩɨɧɟɧɬɚ ɹɜɥɹɟɬɫɹ ɩɨɥɧɵɦ ɢɦɟɧɟɦ ɤɨɦɩɨɧɟɧɬɚ. 2. ɉɨɥɧɨɟ ɢɦɹ ɦɥɚɞɲɟɣ ɫɬɪɭɤɬɭɪɧɨɣ ɟɞɢɧɢɰɵ ɫɬɪɨɢɬɫɹ ɩɭɬɟɦ ɞɨɛɚɜɥɟɧɢɹ ɫɩɪɚɜɚ ɤ ɢɦɟɧɢ ɫɬɚɪɲɟɣ ɫɬɪɭɤɬɭɪɧɨɣ ɟɞɢɧɢɰɵ ɬɨɱɤɢ, ɩɫɟɜɞɨɧɢɦɚ ɦɥɚɞɲɟɣ ɫɬɪɭɤɬɭɪɧɨɣ ɟɞɢɧɢɰɵ ɢ ɧɨɦɟɪɚ ɷɤɡɟɦɩɥɹɪɚ ɦɥɚɞɲɟɣ ɫɬɪɭɤɬɭɪɧɨɣ ɟɞɢɧɢɰɵ, ɟɫɥɢ ɦɥɚɞɲɢɯ ɫɬɪɭɤɬɭɪɧɵɯ ɟɞɢɧɢɰ ɫ ɬɚɤɢɦ ɩɫɟɜɞɨɧɢɦɨɦ ɧɟɫɤɨɥɶɤɨ. ɂɧɨɝɞɚ ɩɪɢ ɩɨɫɬɪɨɟɧɢɢ ɨɩɢɫɚɧɢɹ ɤɨɦɩɨɧɟɧɬɚ ɬɪɟɛɭɟɬɫɹ ɨɞɧɨɡɧɚɱɧɨɟ ɢɦɹ ɫɬɪɭɤɬɭɪɧɨɣ ɟɞɢɧɢɰɵ. ȼ ɤɚɱɟɫɬɜɟ ɨɞɧɨɡɧɚɱɧɨɝɨ ɢɦɟɧɢ ɦɨɠɧɨ ɢɫɩɨɥɶɡɨɜɚɬɶ ɩɨɥɧɨɟ ɢɦɹ, ɧɨ ɬɚɤɨɣ ɩɨɞɯɨɞ ɥɢɲɚɟɬ ɜɨɡɦɨɠɧɨɫɬɢ ɜɫɬɚɜɥɹɬɶ ɩɨɞɝɨɬɨɜɥɟɧɧɵɟ ɫɬɪɭɤɬɭɪɧɵɟ ɟɞɢɧɢɰɵ ɜ ɫɬɪɭɤɬɭɪɵ ɛɨɥɟɟ ɜɵɫɨɤɨɝɨ ɭɪɨɜɧɹ. Ⱦɥɹ ɷɬɨɝɨ ɜɜɨɞɢɬɫɹ ɩɨɧɹɬɢɟ ɨɞɧɨɡɧɚɱɧɨɝɨ ɢɦɟɧɢ ɫɬɪɭɤɬɭɪɧɨɣ ɟɞɢɧɢɰɵ: ɜ ɨɩɢɫɚɧɢɢ ɫɬɪɭɤɬɭɪɧɨɣ ɟɞɢɧɢɰɵ A ɨɞɧɨɡɧɚɱɧɵɦ ɢɦɟɧɟɦ ɫɬɪɭɤɬɭɪɧɨɣ ɟɞɢɧɢɰɵ B, ɹɜɥɹɸɳɟɣɫɹ ɱɚɫɬɶɸ ɫɬɪɭɤɬɭɪɧɨɣ ɟɞɢɧɢɰɵ A, ɹɜɥɹɟɬɫɹ ɩɨɥɧɨɟ ɢɦɹ ɫɬɪɭɤɬɭɪɧɨɣ ɟɞɢɧɢɰɵ B, ɢɡ ɤɨɬɨɪɨɝɨ ɢɫɤɥɸɱɟɧɨ ɩɨɥɧɨɟ ɢɦɹ ɫɬɪɭɤɬɭɪɧɨɣ ɟɞɢɧɢɰɵ A.
2.8.3 Ɉɩɢɫɚɧɢɟ ɫɢɧɬɚɤɫɢɱɟɫɤɢɯ ɤɨɧɫɬɪɭɤɰɢɣ Ⱦɥɹ ɨɩɢɫɚɧɢɹ ɫɢɧɬɚɤɫɢɫɚ ɹɡɵɤɨɜ ɨɩɢɫɚɧɢɣ ɤɨɦɩɨɧɟɧɬɨɜ ɢɫɩɨɥɶɡɭɟɬɫɹ ɪɚɫɲɢɪɟɧɧɚɹ Ȼɷɤɭɫɨɜɚ ɧɨɪɦɚɥɶɧɚɹ ɮɨɪɦɚ. ȼ ɨɩɢɫɚɧɢɢ ȻɇɎ ɜɨ ɜɫɟɣ ɤɧɢɝɟ ɩɪɢɧɹɬɵ ɫɥɟɞɭɸɳɢɟ ɨɛɨɡɧɚɱɟɧɢɹ: · <ɂɦɹ> – ɧɟɬɟɪɦɢɧɚɥɶɧɵɣ ɫɢɦɜɨɥ – ɩɨɧɹɬɢɟ ɤɨɬɨɪɨɟ ɛɵɥɨ ɪɚɫɤɪɵɬɨ ɢɥɢ ɛɭɞɟɬ ɪɚɫɤɪɵɬɨ ɞɚɥɟɟ;
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ɂɦɹ – ɬɟɪɦɢɧɚɥɶɧɵɣ ɫɢɦɜɨɥ – ɩɨɧɹɬɢɟ, ɧɟ ɬɪɟɛɭɸɳɟɟ ɪɚɫɤɪɵɬɢɹ; ɂɦɹ – ɤɥɸɱɟɜɨɟ ɫɥɨɜɨ – ɩɨɞɦɧɨɠɟɫɬɜɨ ɬɟɪɦɢɧɚɥɶɧɵɯ ɫɢɦɜɨɥɨɜ; [ɂɦɹ] – ɧɟɨɛɹɡɚɬɟɥɶɧɵɣ ɷɥɟɦɟɧɬ – ɫɢɧɬɚɤɫɢɱɟɫɤɚɹ ɤɨɧɫɬɪɭɤɰɢɹ, ɡɚɤɥɸɱɟɧɧɚɹ ɜ ɫɤɨɛɤɢ ɦɨɠɟɬ ɨɬɫɭɬɫɬɜɨɜɚɬɶ; · {ɂɦɹ1 ½ ɂɦɹ2 ½ ɂɦɹ3}– ɨɞɧɨ ɢɡ ɡɧɚɱɟɧɢɣ – ɦɨɠɟɬ ɩɪɢɫɭɬɫɬɜɨɜɚɬɶ ɨɞɧɨ ɢ ɬɨɥɶɤɨ ɨɞɧɨ ɢɡ ɪɚɡɞɟɥɟɧɧɵɯ ɫɢɦɜɨɥɨɦ ½ ɡɧɚɱɟɧɢɣ. ȼ ɞɚɧɧɨɦ ɪɚɡɞɟɥɟ ɩɪɢɜɟɞɟɧɨ ɨɩɢɫɚɧɢɟ ɨɛɳɟɝɨ ɩɨɞɦɧɨɠɟɫɬɜɚ ɹɡɵɤɨɜ ɨɩɢɫɚɧɢɹ ɤɨɦɩɨɧɟɧɬɨɜ. ȼ ɧɟɤɨɬɨɪɵɯ ɫɥɭɱɚɹɯ, ɤɨɝɞɚ ȻɇɎ ɨɩɢɫɚɧɢɟ ɩɨɧɹɬɢɹ ɫɥɨɠɧɨ, ɚ ɧɟɮɨɪɦɚɥɶɧɨɟ ɨɩɢɫɚɧɢɟ ɩɪɨɫɬɨ ɢ ɨɞɧɨɡɧɚɱɧɨ, ɜ ȻɇɎ ɨɩɢɫɚɧɢɟ ɜɤɥɸɱɚɸɬɫɹ ɮɪɚɝɦɟɧɬɵ ɧɟɮɨɪɦɚɥɶɧɨɝɨ ɨɩɢɫɚɧɢɹ ɬɚɤɢɯ ɩɨɧɹɬɢɣ. ɋɩɢɫɨɤ ɫɢɧɬɚɤɫɢɱɟɫɤɢɯ ɤɨɧɫɬɪɭɤɰɢɣ ɨɛɳɟɝɨ ɧɚɡɧɚɱɟɧɢɹ: <ɂɞɟɧɬɢɮɢɤɚɬɨɪ> ::= <Ȼɭɤɜɚ> [<ɋɢɦɜɨɥɶɧɚɹ ɫɬɪɨɤɚ>] <Ȼɭɤɜɚ> ::= {a ½ b ½ c ½ d ½ e ½ f ½ g ½ h ½ i ½ j ½ k ½ l ½ m ½ n ½ o ½ p ½ q ½ r ½ s ½ t ½ u ½ v ½ w ½ x ½ y ½ z ½A½B½C½D½E½F½G½H½I½J½K½L½M½N½O½P½Q½R½S½T½U½V½W½X ½ Y ½ Z} <ɋɢɦɜɨɥɶɧɚɹ ɫɬɪɨɤɚ> ::= {<Ȼɭɤɜɚ> ½ <ɐɢɮɪɚ> ½ _ } [<ɋɢɦɜɨɥɶɧɚɹ ɫɬɨɤɚ>] <ɐɢɮɪɚ> ::= {0 ½ 1 ½ 2 ½ 3 ½ 4 ½ 5 ½ 6 ½ 7 ½ 8 ½ 9} <ɑɢɫɥɨ> ::= {<ɐɟɥɨɟ ɱɢɫɥɨ> ½ <Ⱦɟɣɫɬɜɢɬɟɥɶɧɨɟ ɱɢɫɥɨ>} <ɐɟɥɨɟ ɱɢɫɥɨ> ::= [–] <ɉɨɥɨɠɢɬɟɥɶɧɨɟ ɰɟɥɨɟ ɱɢɫɥɨ> <ɉɨɥɨɠɢɬɟɥɶɧɨɟ ɰɟɥɨɟ ɱɢɫɥɨ> ::= <ɐɢɮɪɚ> [<ɉɨɥɨɠɢɬɟɥɶɧɨɟ ɰɟɥɨɟ ɱɢɫɥɨ>] <Ⱦɟɣɫɬɜɢɬɟɥɶɧɨɟ ɱɢɫɥɨ> ::= <ɐɟɥɨɟ ɱɢɫɥɨ>[.<ɉɨɥɨɠɢɬɟɥɶɧɨɟ ɰɟɥɨɟ ɱɢɫɥɨ>][e<ɐɟɥɨɟ ɱɢɫɥɨ>] <ɐɟɥɨɱɢɫɥɟɧɧɚɹ ɤɨɧɫɬɚɧɬɚ> ::= {<ɉɪɟɞɨɩɪɟɞɟɥɟɧɧɚɹ ɤɨɧɫɬɚɧɬɚ ɬɢɩɚ Integer> ½ < ɉɪɟɞɨɩɪɟɞɟɥɟɧɧɚɹ ɤɨɧɫɬɚɧɬɚ ɬɢɩɚ Long> ½ <ɐɟɥɨɟ ɱɢɫɥɨ>} <ɐɜɟɬɨɜɚɹ ɤɨɧɫɬɚɧɬɚ> ::= H <ɒɟɫɬɧɚɞɰɚɬɟɪɢɱɧɚɹ ɰɢɮɪɚ> <ɒɟɫɬɧɚɞɰɚɬɟɪɢɱɧɚɹ ɰɢɮɪɚ> <ɒɟɫɬɧɚɞɰɚɬɟɪɢɱɧɚɹ ɰɢɮɪɚ> <ɒɟɫɬɧɚɞɰɚɬɟɪɢɱɧɚɹ ɰɢɮɪɚ> <ɒɟɫɬɧɚɞɰɚɬɟɪɢɱɧɚɹ ɰɢɮɪɚ> ::= {0 ½ 1 ½ 2 ½ 3 ½ 4 ½ 5 ½ 6 ½ 7 ½ 8 ½ 9 ½ A ½ B ½ C ½ D ½ E ½ F } <ɋɬɪɨɤɨɜɚɹ ɤɨɧɫɬɚɧɬɚ> ::= “<ɋɬɪɨɤɚ ɩɪɨɢɡɜɨɥɶɧɵɯ ɫɢɦɜɨɥɨɜ>” <Ʌɨɝɢɱɟɫɤɚɹ ɤɨɧɫɬɚɧɬɚ> ::= {True ½ False} <ɋɬɪɨɤɚ ɩɪɨɢɡɜɨɥɶɧɵɯ ɫɢɦɜɨɥɨɜ> – ɉɨɫɥɟɞɨɜɚɬɟɥɶɧɨɫɬɶ ɩɪɨɢɡɜɨɥɶɧɵɯ ɫɢɦɜɨɥɨɜ ɢɡ ɧɚɛɨɪɚ ANSI. ȼ ɷɬɨɣ ɩɨɫɥɟɞɨɜɚɬɟɥɶɧɨɫɬɢ ɞɨɩɭɫɤɚɸɬɫɹ ɫɢɦɜɨɥɵ ɧɚɰɢɨɧɚɥɶɧɵɯ ɚɥɮɚɜɢɬɨɜ. ɉɪɢ ɧɟɨɛɯɨɞɢɨɫɬɢ ɜɤɥɸɱɢɬɶ ɜ ɷɬɭ ɤɨɧɫɬɪɭɤɰɢɸ ɫɢɦɜɨɥ ɤɚɜɵɱɟɤ, ɨɧ ɞɨɥɠɟɧ ɛɵɬɶ ɭɞɜɨɟɧ. <ɋɤɚɥɹɪɧɵɣ ɬɢɩ> ::= {Long ½ Real ½ Integer ½ Color ½ Logic ½ String ½ PRealArray ½ PIntegerArray ½ PLongArray ½ PLogicArray ½ PString ½ Visual ½ Pointer ½ FuncType} <Ɍɢɩ ɦɚɫɫɢɜɚ> ::= { RealArray ½ IntegerArray ½ LongArray ½ LogicArray} <Ʉɨɧɫɬɚɧɬɚ ɬɢɩɚ Ɍɢɩ> – ɤɨɧɫɬɚɧɬɚ ɢɦɟɸɳɚɹ ɬɢɩ Ɍɢɩ. ɋɩɢɫɨɤ ɫɢɧɬɚɤɫɢɱɟɫɤɢɯ ɤɨɧɫɬɪɭɤɰɢɣ ɞɥɹ ɮɨɪɦɚɥɶɧɵɯ ɚɪɝɭɦɟɧɬɨɜ: <ɋɩɢɫɨɤ ɮɨɪɦɚɥɶɧɵɯ ɚɪɝɭɦɟɧɬɨɜ> ::= <Ɏɨɪɦɚɥɶɧɵɣ ɚɪɝɭɦɟɧɬ> [; <ɋɩɢɫɨɤ ɮɨɪɦɚɥɶɧɵɯ ɚɪɝɭɦɟɧɬɨɜ>] <Ɏɨɪɦɚɥɶɧɵɣ ɚɪɝɭɦɟɧɬ> ::= <ɋɩɢɫɨɤ ɢɦɟɧ ɚɪɝɭɦɟɧɬɨɜ> : <ɋɤɚɥɹɪɧɵɣ ɬɢɩ> <ɋɩɢɫɨɤ ɢɦɟɧ ɚɪɝɭɦɟɧɬɨɜ> ::= <ɂɦɹ ɚɪɝɭɦɟɧɬɚ> [,<ɋɩɢɫɨɤ ɢɦɟɧ ɚɪɝɭɦɟɧɬɨɜ>] <ɂɦɹ ɚɪɝɭɦɟɧɬɚ> ::= <ɂɞɟɧɬɢɮɢɤɚɬɨɪ> <Ⱥɪɝɭɦɟɧɬ ɬɢɩɚ Ɍɢɩ> – ɨɞɧɨ ɢɡ ɫɥɟɞɭɸɳɢɯ ɩɨɧɹɬɢɣ: ɢɦɹ ɚɪɝɭɦɟɧɬɚ, ɤɨɬɨɪɵɣ ɩɪɢ ɨɩɢɫɚɧɢɢ ɮɨɪɦɚɥɶɧɵɯ ɚɪɝɭɦɟɧɬɨɜ ɢɦɟɥ ɬɢɩ Ɍɢɩ ɢɦɹ ɷɥɟɦɟɧɬɚ ɚɪɝɭɦɟɧɬɚ-ɦɚɫɫɢɜɚ, ɟɫɥɢ ɷɥɟɦɟɧɬɵ ɦɚɫɫɢɜɚ ɢɦɟɸɬ ɬɢɩ Ɍɢɩ ɪɟɡɭɥɶɬɚɬ ɩɪɢɜɟɞɟɧɢɹ ɩɪɨɢɡɜɨɥɶɧɨɝɨ ɚɪɝɭɦɟɧɬɚ ɢɥɢ ɷɥɟɦɟɧɬɚ ɚɪɝɭɦɟɧɬɚ-ɦɚɫɫɢɜɚ ɤ ɬɢɩɭ Ɍɢɩ. ɋɢɧɬɚɤɫɢɱɟɫɤɢɟ ɤɨɧɫɬɪɭɤɰɢɢ ɨɩɢɫɚɧɢɹ ɩɟɪɟɦɟɧɧɵɯ: <Ɉɩɢɫɚɧɢɟ ɩɟɪɟɦɟɧɧɵɯ> ::= Var <ɋɩɢɫɨɤ ɨɩɢɫɚɧɢɣ ɨɞɧɨɬɢɩɧɵɯ ɩɟɪɟɦɟɧɧɵɯ> <ɋɩɢɫɨɤ ɨɩɢɫɚɧɢɣ ɨɞɧɨɬɢɩɧɵɯ ɩɟɪɟɦɟɧɧɵɯ> ::= <Ɍɢɩ ɩɟɪɟɦɟɧɧɨɣ> <ɋɩɢɫɨɤ ɩɟɪɟɦɟɧɧɵɯ>; [<ɋɩɢɫɨɤ ɨɩɢɫɚɧɢɣ ɨɞɧɨɬɢɩɧɵɯ ɩɟɪɟɦɟɧɧɵɯ>] <ɋɩɢɫɨɤ ɩɟɪɟɦɟɧɧɵɯ> ::= <ɂɦɹ ɩɟɪɟɦɟɧɧɨɣ> [, <ɋɩɢɫɨɤ ɩɟɪɟɦɟɧɧɵɯ>] <ɂɦɹ ɩɟɪɟɦɟɧɧɨɣ> ::= <ɂɞɟɧɬɢɮɢɤɚɬɨɪ> <Ɍɢɩ ɩɟɪɟɦɟɧɧɨɣ> ::= {<ɋɤɚɥɹɪɧɵɣ ɬɢɩ> ½ <Ɍɢɩ ɦɚɫɫɢɜɚ>[<ɐɟɥɨɱɢɫɥɟɧɧɨɟ ɤɨɧɫɬɚɧɬɧɨɟ ɜɵɪɚɠɟɧɢɟ>]} <ɉɟɪɟɦɟɧɧɚɹ ɬɢɩɚ Ɍɢɩ> – ɨɞɧɨ ɢɡ ɫɥɟɞɭɸɳɢɯ ɩɨɧɹɬɢɣ: ɢɦɹ ɩɟɪɟɦɟɧɧɨɣ, ɤɨɬɨɪɚɹ ɩɪɢ ɨɩɢɫɚɧɢɢ ɩɟɪɟɦɟɧɧɵɯ ɢɦɟɥɚ ɬɢɩ Ɍɢɩ ɢɦɹ ɷɥɟɦɟɧɬɚ ɦɚɫɫɢɜɚ, ɟɫɥɢ ɷɥɟɦɟɧɬɵ ɦɚɫɫɢɜɚ ɢɦɟɸɬ ɬɢɩ Ɍɢɩ ɪɟɡɭɥɶɬɚɬ ɩɪɢɜɟɞɟɧɢɹ ɩɪɨɢɡɜɨɥɶɧɨɣ ɩɟɪɟɦɟɧɧɨɣ ɢɥɢ ɷɥɟɦɟɧɬɚ ɦɚɫɫɢɜɚ ɤ ɬɢɩɭ Ɍɢɩ.
CHAP2.DOC
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ɋɢɧɬɚɤɫɢɱɟɫɤɚɹ ɤɨɧɫɬɪɭɤɰɢɹ ɨɩɢɫɚɧɢɹ ɝɥɨɛɚɥɶɧɵɯ ɩɟɪɟɦɟɧɧɵɯ (ɞɨɫɬɭɩɧɚ ɬɨɥɶɤɨ ɜ ɹɡɵɤɚɯ ɨɩɢɫɚɧɢɹ ɤɨɦɩɨɧɟɧɬɨɜ ɭɱɢɬɟɥɶ ɢ ɤɨɧɬɪɚɫɬɟɪ): <Ɉɩɢɫɚɧɢɟ ɝɥɨɛɚɥɶɧɵɯ ɩɟɪɟɦɟɧɧɵɯ> ::= Global <ɋɩɢɫɨɤ ɨɩɢɫɚɧɢɣ ɨɞɧɨɬɢɩɧɵɯ ɩɟɪɟɦɟɧɧɵɯ> ɋɢɧɬɚɤɫɢɱɟɫɤɢɟ ɤɨɧɫɬɪɭɤɰɢɢ ɨɩɢɫɚɧɢɹ ɫɬɚɬɢɱɟɫɤɢɯ ɩɟɪɟɦɟɧɧɵɯ ɋɬɚɬɢɱɟɫɤɢɟ ɩɟɪɟɦɟɧɧɵɟ, ɤɚɤ ɩɪɚɜɢɥɨ, ɫɥɭɠɚɬ ɞɥɹ ɨɩɢɫɚɧɢɹ ɩɚɪɚɦɟɬɪɨɜ ɤɨɦɩɨɧɟɧɬɨɜ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚ. ɂɫɩɨɥɶɡɨɜɚɧɢɟ ɜ ɢɦɟɧɚɯ ɩɟɪɟɦɟɧɧɵɯ ɬɨɥɶɤɨ ɫɢɦɜɨɥɨɜ ɥɚɬɢɧɫɤɨɝɨ ɚɥɮɚɜɢɬɚ ɢ ɰɢɮɪ ɞɟɥɚɟɬ ɢɞɟɧɬɢɮɢɤɚɬɨɪɵ ɭɧɢɜɟɪɫɚɥɶɧɵɦɢ, ɧɨ ɧɟɭɞɨɛɧɵɦɢ ɞɥɹ ɜɫɟɯ ɩɨɥɶɡɨɜɚɬɟɥɟɣ, ɤɪɨɦɟ ɚɧɝɥɨ-ɝɨɜɨɪɹɳɢɯ. Ⱦɥɹ ɭɞɨɛɫɬɜɚ ɜɫɟɯ ɨɫɬɚɥɶɧɵɯ ɩɨɥɶɡɨɜɚɬɟɥɟɣ ɜ ɨɩɢɫɚɧɢɢ ɫɬɚɬɢɱɟɫɤɢɯ ɩɟɪɟɦɟɧɧɵɯ ɩɪɟɞɭɫɦɨɬɪɟɧɚ ɜɨɡɦɨɠɧɨɫɬɶ ɢɫɩɨɥɶɡɨɜɚɬɶ ɞɨɩɨɥɧɢɬɟɥɶɧɵɟ ɢɦɟɧɚ ɞɥɹ ɫɬɚɬɢɱɟɫɤɢɯ ɩɟɪɟɦɟɧɧɵɯ. Ɉɞɧɚɤɨ ɷɬɢ ɢɦɟɧɚ ɫɥɭɠɚɬ ɬɨɥɶɤɨ ɞɥɹ ɩɨɫɬɪɨɟɧɢɹ ɢɧɬɟɪɮɟɣɫɚ ɢ ɧɟ ɦɨɝɭɬ ɛɵɬɶ ɢɫɩɨɥɶɡɨɜɚɧɵ ɜ ɨɩɢɫɚɧɢɢ ɬɟɥɚ ɫɨɨɬɜɟɬɫɬɜɭɸɳɟɝɨ ɤɨɦɩɨɧɟɧɬɚ. Ʉɪɨɦɟ ɬɨɝɨ, ɫɬɚɬɢɱɟɫɤɨɣ ɩɟɪɟɦɟɧɧɨɣ ɦɨɠɧɨ ɩɪɢ ɨɩɢɫɚɧɢɢ ɡɚɞɚɬɶ ɡɧɚɱɟɧɢɟ ɩɨ ɭɦɨɥɱɚɧɢɸ. <Ɉɩɢɫɚɧɢɟ ɫɬɚɬɢɱɟɫɤɢɯ ɩɟɪɟɦɟɧɧɵɯ> ::= Static <ɋɩɢɫɨɤ ɨɩɢɫɚɧɢɣ ɫɬɚɬɢɱɟɫɤɢɯ ɩɟɪɟɦɟɧɧɵɯ> <ɋɩɢɫɨɤ ɨɩɢɫɚɧɢɣ ɫɬɚɬɢɱɟɫɤɢɯ ɩɟɪɟɦɟɧɧɵɯ> ::= <Ɉɩɢɫɚɧɢɟ ɫɬɚɬɢɱɟɫɤɨɣ ɩɟɪɟɦɟɧɧɨɣ>; [<ɋɩɢɫɨɤ ɨɩɢɫɚɧɢɣ ɫɬɚɬɢɱɟɫɤɢɯ ɩɟɪɟɦɟɧɧɵɯ>] <Ɉɩɢɫɚɧɢɟ ɫɬɚɬɢɱɟɫɤɨɣ ɩɟɪɟɦɟɧɧɨɣ> ::= <Ɍɢɩ ɩɟɪɟɦɟɧɧɨɣ> <ɂɦɹ ɩɟɪɟɦɟɧɧɨɣ> [Name <ɂɦɹ ɫɬɚɬɢɱɟɫɤɨɣ ɩɟɪɟɦɟɧɧɨɣ>] [Default <Ɂɧɚɱɟɧɢɟ ɩɨ ɭɦɨɥɱɚɧɢɸ>] <ɂɦɹ ɫɬɚɬɢɱɟɫɤɨɣ ɩɟɪɟɦɟɧɧɨɣ> ::= <ɋɬɪɨɤɨɜɚɹ ɤɨɧɫɬɚɧɬɚ> <Ɂɧɚɱɟɧɢɟ ɩɨ ɭɦɨɥɱɚɧɢɸ> ::= <Ʉɨɧɫɬɚɧɬɧɨɟ ɜɵɪɚɠɟɧɢɟ ɬɢɩɚ <Ɍɢɩ ɩɟɪɟɦɟɧɧɨɣ>> ɋɢɧɬɚɤɫɢɱɟɫɤɢɟ ɤɨɧɫɬɪɭɤɰɢɢ ɨɩɢɫɚɧɢɹ ɮɭɧɤɰɢɣ <Ɉɩɢɫɚɧɢɟ ɮɭɧɤɰɢɣ> ::= <Ɉɩɢɫɚɧɢɟ ɮɭɧɤɰɢɢ> [<Ɉɩɢɫɚɧɢɟ ɮɭɧɤɰɢɣ>] <Ɉɩɢɫɚɧɢɟ ɮɭɧɤɰɢɢ> ::= <Ɂɚɝɨɥɨɜɨɤ ɮɭɧɤɰɢɢ> <Ɉɩɢɫɚɧɢɟ ɩɟɪɟɦɟɧɧɵɯ> <Ɉɩɢɫɚɧɢɟ ɦɟɬɨɤ> <Ɍɟɥɨ ɮɭɧɤɰɢɢ> <Ɂɚɝɨɥɨɜɨɤ ɮɭɧɤɰɢɢ> ::= Function <ɂɦɹ ɮɭɧɤɰɢɢ>[(<ɋɩɢɫɨɤ ɮɨɪɦɚɥɶɧɵɯ ɚɪɝɭɦɟɧɬɨɜ>)] : <ɋɤɚɥɹɪɧɵɣ ɬɢɩ>; <Ɉɩɢɫɚɧɢɟ ɦɟɬɨɤ> ::= Label <ɋɩɢɫɨɤ ɦɟɬɨɤ>; <ɋɩɢɫɨɤ ɦɟɬɨɤ> ::= <ɂɦɹ ɦɟɬɤɢ> [, <ɋɩɢɫɨɤ ɦɟɬɨɤ>] <ɂɦɹ ɦɟɬɤɢ> ::= <ɂɞɟɧɬɢɮɢɤɚɬɨɪ> <Ɍɟɥɨ ɮɭɧɤɰɢɢ> ::= Begin <ɋɨɫɬɚɜɧɨɣ ɨɩɟɪɚɬɨɪ> End; <ɋɨɫɬɚɜɧɨɣ ɨɩɟɪɚɬɨɪ> ::= [<ɂɦɹ ɦɟɬɤɢ>:] <Ɉɩɟɪɚɬɨɪ> [; <ɋɨɫɬɚɜɧɨɣ ɨɩɟɪɚɬɨɪ>] <Ɉɩɟɪɚɬɨɪ> ::= {<Ɉɩɟɪɚɬɨɪ ɩɪɢɫɜɚɢɜɚɧɢɹ> ½ <Ɉɩɟɪɚɬɨɪ ɜɟɬɜɥɟɧɢɹ> ½ <Ɉɩɟɪɚɬɨɪ ɰɢɤɥɚ> ½ <Ɉɩɟɪɚɬɨɪ ɩɟɪɟɯɨɞɚ> ½ <Ɉɩɟɪɚɬɨɪɧɵɟ ɫɤɨɛɤɢ>} <Ɉɩɟɪɚɬɨɪ ɩɪɢɫɜɚɢɜɚɧɢɹ> ::= <Ⱦɨɩɭɫɬɢɦɨɟ ɢɦɹ ɩɟɪɟɦɟɧɧɨɣ> = <ȼɵɪɚɠɟɧɢɟ> <Ɉɩɟɪɚɬɨɪ ɜɟɬɜɥɟɧɢɹ> ::= If <Ʌɨɝɢɱɟɫɤɨɟ ɜɵɪɚɠɟɧɢɟ> Then <Ɉɩɟɪɚɬɨɪ> [Else <Ɉɩɟɪɚɬɨɪ>] <Ɉɩɟɪɚɬɨɪ ɰɢɤɥɚ> ::= { <ɐɢɤɥ For> ½ <ɐɢɤɥ While> } <ɐɢɤɥ For> ::= For <ɂɦɹ ɩɟɪɟɦɟɧɧɨɣ> = <ɐɟɥɨɱɢɫɥɟɧɧɨɟ ɜɵɪɚɠɟɧɢɟ> To <ɐɟɥɨɱɢɫɥɟɧɧɨɟ ɜɵɪɚɠɟɧɢɟ> [By <ɐɟɥɨɱɢɫɥɟɧɧɨɟ ɜɵɪɚɠɟɧɢɟ>] Do <Ɉɩɟɪɚɬɨɪ> <ɐɢɤɥ While> ::= While <Ʌɨɝɢɱɟɫɤɨɟ ɜɵɪɚɠɟɧɢɟ> Do <Ɉɩɟɪɚɬɨɪ> <Ɉɩɟɪɚɬɨɪ ɩɟɪɟɯɨɞɚ> ::= GoTo <ɂɦɹ ɦɟɬɤɢ> <Ɉɩɟɪɚɬɨɪɧɵɟ ɫɤɨɛɤɢ> ::= Begin <ɋɨɫɬɚɜɧɨɣ ɨɩɟɪɚɬɨɪ> End <Ɏɭɧɤɰɢɹ ɬɢɩɚ Ɍɢɩ> – ɮɭɧɤɰɢɹ, ɜɨɡɜɪɚɳɚɸɳɚɹ ɜɟɥɢɱɢɧɭ ɬɢɩɚ Ɍɢɩ. <Ⱦɨɩɭɫɬɢɦɨɟ ɢɦɹ ɩɟɪɟɦɟɧɧɨɣ> – ɞɨɩɭɫɬɢɦɨɣ ɩɟɪɟɦɟɧɧɨɣ ɹɜɥɹɸɬɫɹ ɜɫɟ ɩɟɪɟɦɟɧɧɵɟ, ɨɩɢɫɚɧɧɵɟ ɜ ɞɚɧɧɨɣ ɮɭɧɤɰɢɢ ɢɥɢ ɜ ɞɚɧɧɨɦ ɩɪɨɰɟɞɭɪɧɨɦ ɛɥɨɤɟ, ɝɥɨɛɚɥɶɧɵɟ ɩɟɪɟɦɟɧɧɵɟ ɞɚɧɧɨɝɨ ɤɨɦɩɨɧɟɧɬɚ. Ⱦɥɹ ɜɨɡɜɪɚɳɟɧɢɹ ɡɧɚɱɟɧɢɹ ɮɭɧɤɰɢɢ, ɜ ɥɟɜɨɣ ɱɚɫɬɢ ɨɩɟɪɚɬɨɪɚ ɩɪɢɫɜɚɢɜɚɧɢɹ ɞɨɥɠɧɨ ɫɬɨɹɬɶ ɢɦɹ ɮɭɧɤɰɢɢ. ɋɢɧɬɚɤɫɢɱɟɫɤɢɟ ɤɨɧɫɬɪɭɤɰɢɢ ɨɩɢɫɚɧɢɹ ɜɵɪɚɠɟɧɢɣ: <ȼɵɪɚɠɟɧɢɟ> ::= { <ȼɵɪɚɠɟɧɢɟ ɬɢɩɚ Long> ½ <ȼɵɪɚɠɟɧɢɟ ɬɢɩɚ Real> ½ <ȼɵɪɚɠɟɧɢɟ ɬɢɩɚ Integer> ½ <ȼɵɪɚɠɟɧɢɟ ɬɢɩɚ Color> ½ <ȼɵɪɚɠɟɧɢɟ ɬɢɩɚ Logic> ½ <ȼɵɪɚɠɟɧɢɟ ɬɢɩɚ String> ½ <ȼɵɪɚɠɟɧɢɟ ɬɢɩɚ Pointer>} <ɐɟɥɨɱɢɫɥɟɧɧɨɟ ɜɵɪɚɠɟɧɢɟ> ::= { <ȼɵɪɚɠɟɧɢɟ ɬɢɩɚ Long> ½ <ȼɵɪɚɠɟɧɢɟ ɬɢɩɚ Integer>} <ȼɵɪɚɠɟɧɢɟ ɬɢɩɚ Ɍɢɩ> ::= [<ɉɪɟɮɢɤɫɧɚɹ ɨɩɟɪɚɰɢɹ ɬɢɩɚ Ɍɢɩ>] <Ɉɩɟɪɚɧɞ ɬɢɩɚ Ɍɢɩ> [<Ɉɩɟɪɚɰɢɹ ɬɢɩɚ Ɍɢɩ> <Ɉɩɟɪɚɧɞ ɬɢɩɚ Ɍɢɩ>] <Ɉɩɟɪɚɰɢɹ ɬɢɩɚ Long> ::= {+ ½ – ½ * ½ Div ½ Mod ½ And ½ Or ½ Xor} <Ɉɩɟɪɚɰɢɹ ɬɢɩɚ Real>::= {+ ½ – ½ * ½ / ½ RMod } <Ɉɩɟɪɚɰɢɹ ɬɢɩɚ Integer> ::= {+ ½ – ½ * ½ Div ½ Mod ½ And ½ Or ½ Xor} <Ɉɩɟɪɚɰɢɹ ɬɢɩɚ Color> ::= {COr ½ CAnd ½ CXor}
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<Ɉɩɟɪɚɰɢɹ ɬɢɩɚ Logic> ::= {And ½ Or ½ Xor} <Ɉɩɟɪɚɰɢɹ ɬɢɩɚ String> ::= + <ɉɪɟɮɢɤɫɧɚɹ ɨɩɟɪɚɰɢɹ ɬɢɩɚ Long> ::= { – ½ Not } <ɉɪɟɮɢɤɫɧɚɹ ɨɩɟɪɚɰɢɹ ɬɢɩɚ Real>::= – <ɉɪɟɮɢɤɫɧɚɹ ɨɩɟɪɚɰɢɹ ɬɢɩɚ Integer> ::= { – ½ Not } <ɉɪɟɮɢɤɫɧɚɹ ɨɩɟɪɚɰɢɹ ɬɢɩɚ Color> ::= CNot <ɉɪɟɮɢɤɫɧɚɹ ɨɩɟɪɚɰɢɹ ɬɢɩɚ Logic> ::= Not <Ɉɩɟɪɚɧɞ ɬɢɩɚ Logic> ::= ::= {<Ɋɟɡɭɥɶɬɚɬ ɫɪɚɜɧɟɧɢɹ> ½ <ȼɵɪɚɠɟɧɢɟ ɬɢɩɚ Logic> ½ (<ȼɵɪɚɠɟɧɢɟ ɬɢɩɚ Logic>) ½ <Ʉɨɧɫɬɚɧɬɚ ɬɢɩɚ Logic> ½ <ɉɟɪɟɦɟɧɧɚɹ ɬɢɩɚ Logic> ½ <Ⱥɪɝɭɦɟɧɬ ɬɢɩɚ Logic> ½ <ȼɵɡɨɜ ɮɭɧɤɰɢɢ ɬɢɩɚ Logic>} <Ɋɟɡɭɥɶɬɚɬ ɫɪɚɜɧɟɧɢɹ ɬɢɩɨɜ Long, Integer, Real> ::= (<ȼɵɪɚɠɟɧɢɟ ɬɢɩɚ Long, Integer, Real> {> ½ < ½ >= ½ <= ½ = ½ <>} <ȼɵɪɚɠɟɧɢɟ ɬɢɩɚ Long, Integer, Real> ) <Ɋɟɡɭɥɶɬɚɬ ɫɪɚɜɧɟɧɢɹ ɬɢɩɚ Color> ::= (<ȼɵɪɚɠɟɧɢɟ ɬɢɩɚ Color> {CEqual ½ CIn ½ CInclude ½ CExclude ½ CIntersect} <ȼɵɪɚɠɟɧɢɟ ɬɢɩɚ Color> ) <Ɋɟɡɭɥɶɬɚɬ ɫɪɚɜɧɟɧɢɹ ɬɢɩɚ String> ::= (<ȼɵɪɚɠɟɧɢɟ ɬɢɩɚ String> {= ½ <>} <ȼɵɪɚɠɟɧɢɟ ɬɢɩɚ String> ) <Ɉɩɟɪɚɧɞ ɬɢɩɚ Ɍɢɩ> ::= {<ȼɵɪɚɠɟɧɢɟ ɬɢɩɚ Ɍɢɩ> ½ (<ȼɵɪɚɠɟɧɢɟ ɬɢɩɚ Ɍɢɩ>) ½ <Ʉɨɧɫɬɚɧɬɚ ɬɢɩɚ Ɍɢɩ> ½ <ɉɟɪɟɦɟɧɧɚɹ ɬɢɩɚ Ɍɢɩ> ½ <Ⱥɪɝɭɦɟɧɬ ɬɢɩɚ Ɍɢɩ> ½ <ȼɵɡɨɜ ɮɭɧɤɰɢɢ ɬɢɩɚ Ɍɢɩ>} <ȼɵɡɨɜ ɮɭɧɤɰɢɢ ɬɢɩɚ Ɍɢɩ> ::= <ɂɦɹ ɮɭɧɤɰɢɢ ɬɢɩɚ Ɍɢɩ> [(<ɋɩɢɫɨɤ ɮɚɤɬɢɱɟɫɤɢɯ ɚɪɝɭɦɟɧɬɨɜ>)] <ɋɩɢɫɨɤ ɮɚɤɬɢɱɟɫɤɢɯ ɚɪɝɭɦɟɧɬɨɜ> ::= <ȼɵɪɚɠɟɧɢɟ> [,<ɋɩɢɫɨɤ ɮɚɤɬɢɱɟɫɤɢɯ ɚɪɝɭɦɟɧɬɨɜ>] <Ʉɨɧɫɬɚɧɬɧɨɟ ɜɵɪɚɠɟɧɢɟ ɬɢɩɚ Ɍɢɩ> – <ȼɵɪɚɠɟɧɢɟ ɬɢɩɚ Ɍɢɩ> ɜ ɨɩɟɪɚɧɞɚɯ ɤɨɬɨɪɨɝɨ ɧɟ ɦɨɝɭɬ ɮɢɝɭɪɢɪɨɜɚɬɶ ɩɟɪɟɦɟɧɧɵɟ ɢ ɮɭɧɤɰɢɢ, ɨɩɢɫɚɧɧɵɟ ɩɨɥɶɡɨɜɚɬɟɥɟɦ. <ɑɢɫɥɨɜɨɟ ɜɵɪɚɠɟɧɢɟ> ::= { <ȼɵɪɚɠɟɧɢɟ ɬɢɩɚ Long> ½ <ȼɵɪɚɠɟɧɢɟ ɬɢɩɚ Real> ½ <ȼɵɪɚɠɟɧɢɟ ɬɢɩɚ Integer>} ɋɢɧɬɚɤɫɢɱɟɫɤɢɟ ɤɨɧɫɬɪɭɤɰɢɢ ɡɚɞɚɧɢɹ ɡɧɚɱɟɧɢɣ ɫɬɚɬɢɱɟɫɤɢɦ ɩɟɪɟɦɟɧɧɵɦ ɗɬɚ ɤɨɧɫɬɪɭɤɰɢɹ ɫɥɭɠɢɬ ɞɥɹ ɡɚɞɚɧɢɹ ɡɧɚɱɟɧɢɣ ɩɚɪɚɦɟɬɪɚɦ (ɫɬɚɬɢɱɟɫɤɢɦ ɩɟɪɟɦɟɧɧɵɦ) ɤɨɦɩɨɧɟɧɬɨɜ. Ⱦɥɹ ɤɨɦɩɨɧɟɧɬɚ ɫɟɬɶ ɨɧɚ ɦɨɠɟɬ ɜɫɬɪɟɱɚɬɶɫɹ ɧɟ ɬɨɥɶɤɨ ɩɪɢ ɨɩɢɫɚɧɢɢ ɝɥɚɜɧɨɣ ɫɟɬɢ, ɧɨ ɢ ɩɪɢ ɨɩɢɫɚɧɢɢ ɥɸɛɨɣ ɫɨɫɬɚɜɧɨɣ ɩɨɞɫɟɬɢ. ȼ ɫɩɟɰɢɚɥɶɧɵɯ ɜɵɪɚɠɟɧɢɹɯ ɬɢɩɚ Ɍɢɩ ɦɨɝɭɬ ɭɱɚɫɬɜɨɜɚɬɶ ɬɨɥɶɤɨ ɫɬɚɧɞɚɪɬɧɵɟ ɮɭɧɤɰɢɢ ɢ ɚɪɝɭɦɟɧɬɵ ɬɨɣ ɫɬɪɭɤɬɭɪɧɨɣ ɟɞɢɧɢɰɵ, ɜ ɤɨɬɨɪɨɣ ɧɚɯɨɞɢɬɫɹ ɛɥɨɤ ɡɚɞɚɧɢɹ ɡɧɚɱɟɧɢɣ ɫɬɚɬɢɱɟɫɤɢɦ ɩɟɪɟɦɟɧɧɵɦ. ɉɪɢ ɷɬɨɦ ɫɩɟɰɢɚɥɶɧɨɟ ɜɵɪɚɠɟɧɢɟ, ɡɚɞɚɸɳɟɟ ɡɧɚɱɟɧɢɟ ɩɚɪɚɦɟɬɪɚ ɞɨɥɠɧɨ ɢɦɟɬɶ ɬɢɩ, ɫɨɜɦɟɫɬɢɦɵɣ ɫ ɬɢɩɨɦ ɫɬɚɬɢɱɟɫɤɨɣ ɩɟɪɟɦɟɧɧɨɣ, ɤɨɬɨɪɨɣ ɩɪɢɫɜɚɢɜɚɟɬɫɹ ɷɬɨ ɡɧɚɱɟɧɢɟ. <ɍɫɬɚɧɨɜɥɟɧɢɟ ɩɚɪɚɦɟɬɪɨɜ ɋɬɪɭɤɬɭɪɧɨɣ ɟɞɢɧɢɰɵ> ::= <Ɉɞɧɨɡɧɚɱɧɨɟ ɢɦɹ ɋɬɪɭɤɬɭɪɧɨɣ ɟɞɢɧɢɰɵ> [[[<ɉɟɪɟɦɟɧɧɚɹ ɰɢɤɥɚ>:] <ɇɚɱɚɥɶɧɵɣ ɧɨɦɟɪ> [..<Ʉɨɧɟɱɧɵɣ ɧɨɦɟɪ> [:<ɒɚɝ>] ]]] SetParameters <ɋɩɢɫɨɤ ɡɧɚɱɟɧɢɣ ɩɚɪɚɦɟɬɪɨɜ> <ɉɟɪɟɦɟɧɧɚɹ ɰɢɤɥɚ> ::= <ɂɞɟɧɬɢɮɢɤɚɬɨɪ> <ɇɚɱɚɥɶɧɵɣ ɧɨɦɟɪ> ::= <Ʉɨɧɫɬɚɧɬɧɨɟ ɜɵɪɚɠɟɧɢɟ ɬɢɩɚ Long> <Ʉɨɧɟɱɧɵɣ ɧɨɦɟɪ> ::= <Ʉɨɧɫɬɚɧɬɧɨɟ ɜɵɪɚɠɟɧɢɟ ɬɢɩɚ Long> <ɒɚɝ> ::= <Ʉɨɧɫɬɚɧɬɧɨɟ ɜɵɪɚɠɟɧɢɟ ɬɢɩɚ Long> <ɋɩɢɫɨɤ ɡɧɚɱɟɧɢɣ ɩɚɪɚɦɟɬɪɨɜ> ::= <Ɂɧɚɱɟɧɢɟ ɩɚɪɚɦɟɬɪɚ> [,<ɋɩɢɫɨɤ ɡɧɚɱɟɧɢɣ ɩɚɪɚɦɟɬɪɨɜ>] <Ɂɧɚɱɟɧɢɟ ɩɚɪɚɦɟɬɪɚ> ::= <ɋɩɟɰɢɚɥɶɧɨɟ ɜɵɪɚɠɟɧɢɟ ɬɢɩɚ Ɍɢɩ> <ɋɩɟɰɢɚɥɶɧɨɟ ɜɵɪɚɠɟɧɢɟ ɬɢɩɚ Ɍɢɩ> ::= [<ɉɪɟɮɢɤɫɧɚɹ ɨɩɟɪɚɰɢɹ ɬɢɩɚ Ɍɢɩ>] <ɋɩɟɰɢɚɥɶɧɵɣ ɨɩɟɪɚɧɞ ɬɢɩɚ Ɍɢɩ> [<Ɉɩɟɪɚɰɢɹ ɬɢɩɚ Ɍɢɩ> <ɋɩɟɰɢɚɥɶɧɵɣ ɨɩɟɪɚɧɞ ɬɢɩɚ Ɍɢɩ>] <ɋɩɟɰɢɚɥɶɧɵɣ ɨɩɟɪɚɧɞ ɬɢɩɚ Ɍɢɩ > ::= {<ɋɩɟɰɢɚɥɶɧɨɟ ɜɵɪɚɠɟɧɢɟ ɬɢɩɚ Ɍɢɩ > ½ <Ʉɨɧɫɬɚɧɬɚ ɬɢɩɚ Ɍɢɩ> ½ <ɉɟɪɟɦɟɧɧɚɹ ɰɢɤɥɚ> ½ (<ɋɩɟɰɢɚɥɶɧɨɟ ɜɵɪɚɠɟɧɢɟ ɬɢɩɚ Ɍɢɩ > ½ <Ⱥɪɝɭɦɟɧɬ ɬɢɩɚ Ɍɢɩ> ½ <ȼɵɡɨɜ ɮɭɧɤɰɢɢ ɬɢɩɚ Ɍɢɩ>)> ɋɢɧɬɚɤɫɢɱɟɫɤɢɟ ɤɨɧɫɬɪɭɤɰɢɢ ɨɩɢɫɚɧɢɹ ɪɚɫɩɪɟɞɟɥɟɧɢɹ ɫɢɝɧɚɥɨɜ ɢɥɢ ɩɚɪɚɦɟɬɪɨɜ: Ⱦɚɧɧɚɹ ɤɨɧɫɬɪɭɤɰɢɹ ɢɦɟɟɬ ɱɟɬɵɪɟ ɚɪɝɭɦɟɧɬɚ, ɢɦɟɸɳɢɯ ɫɥɟɞɭɸɳɢɣ ɫɦɵɫɥ: Ⱦɚɧɧɨɟ – ɫɢɝɧɚɥ ɢɥɢ ɩɚɪɚɦɟɬɪ. Ɉɛɴɟɤɬ – ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ, ɢɧɬɟɪɩɪɟɬɚɬɨɪ, ɨɰɟɧɤɚ, ɫɟɬɶ. ɉɨɞɨɛɴɟɤɬ – ɱɚɫɬɧɵɣ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ, ɱɚɫɬɧɵɣ ɢɧɬɟɪɩɪɟɬɚɬɨɪ, ɱɚɫɬɧɚɹ ɨɰɟɧɤɚ, ɩɨɞɫɟɬɶ. <ɂɞɟɧɬɢɮɢɤɚɬɨɪ ɞɚɧɧɵɯ> – ɨɞɧɨ ɢɡ ɤɥɸɱɟɜɵɯ ɫɥɨɜ Signals, Parameters, Data, InSignals, OutSignals. <Ɉɩɢɫɚɧɢɟ ɪɚɫɩɪɟɞɟɥɟɧɢɹ Ⱦɚɧɧɵɯ, Ɉɛɴɟɤɬɚ, ɉɨɞɨɛɴɟɤɬɚ, <ɂɞɟɧɬɢɮɢɤɚɬɨɪ ɞɚɧɧɵɯ>> ::= Connections <Ɉɩɢɫɚɧɢɟ ɝɪɭɩɩ ɫɨɨɬɜɟɬɫɬɜɢɣ Ⱦɚɧɧɵɯ> <Ɉɩɢɫɚɧɢɟ ɝɪɭɩɩ ɫɨɨɬɜɟɬɫɬɜɢɣ Ⱦɚɧɧɵɯ> ::= <Ɉɩɢɫɚɧɢɟ ɝɪɭɩɩɵ ɫɨɨɬɜɟɬɫɬɜɢɣ Ⱦɚɧɧɵɯ> [;<Ɉɩɢɫɚɧɢɟ ɝɪɭɩɩ ɫɨɨɬɜɟɬɫɬɜɢɣ Ⱦɚɧɧɵɯ>] <Ɉɩɢɫɚɧɢɟ ɝɪɭɩɩɵ ɫɨɨɬɜɟɬɫɬɜɢɣ Ⱦɚɧɧɵɯ> ::= <Ȼɥɨɤ ɫɢɝɧɚɥɨɜ ɉɨɞɨɛɴɟɤɬɚ> <=> {<Ȼɥɨɤ ɫɢɝɧɚɥɨɜ Ɉɛɴɟɤɬɚ> ½ <Ȼɥɨɤ ɫɢɝɧɚɥɨɜ ɉɨɞɨɛɴɟɤɬɚ>}
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<Ȼɥɨɤ ɫɢɝɧɚɥɨɜ ɉɨɞɨɛɴɟɤɬɚ> ::= <Ɉɩɢɫɚɬɟɥɶ ɫɢɝɧɚɥɨɜ ɉɨɞɨɛɴɟɤɬɚ> [;<Ȼɥɨɤ ɫɢɝɧɚɥɨɜ ɉɨɞɨɛɴɟɤɬɚ>] <Ɉɩɢɫɚɬɟɥɶ ɫɢɝɧɚɥɨɜ ɉɨɞɨɛɴɟɤɬɚ> ::= { For <ɉɟɪɟɦɟɧɧɚɹ ɰɢɤɥɚ> = <ɇɚɱɚɥɶɧɵɣ ɧɨɦɟɪ> To <Ʉɨɧɟɱɧɵɣ ɧɨɦɟɪ> [Step <ɒɚɝ>] Do <Ȼɥɨɤ ɫɢɝɧɚɥɨɜ ɉɨɞɨɛɴɟɤɬɚ> End ½ <ɋɩɢɫɨɤ Ⱦɚɧɧɵɯ ɉɨɞɨɛɴɟɤɬɚ>} <ɉɟɪɟɦɟɧɧɚɹ ɰɢɤɥɚ> ::= <ɂɞɟɧɬɢɮɢɤɚɬɨɪ> <ɋɩɢɫɨɤ Ⱦɚɧɧɵɯ ɉɨɞɨɛɴɟɤɬɚ> ::= <Ⱦɚɧɧɨɟ ɉɨɞɨɛɴɟɤɬɚ>[; <ɋɩɢɫɨɤ Ⱦɚɧɧɵɯ ɉɨɞɨɛɴɟɤɬɚ>] <Ⱦɚɧɧɨɟ ɉɨɞɨɛɴɟɤɬɚ> ::= <ɉɫɟɜɞɨɧɢɦ>[[<ɇɨɦɟɪ ɷɤɡɟɦɩɥɹɪɚ>]].<ɂɞɟɧɬɢɮɢɤɚɬɨɪ ɞɚɧɧɵɯ> [[<ɇɨɦɟɪ Ⱦɚɧɧɨɝɨ>]] <ɇɨɦɟɪ ɷɤɡɟɦɩɥɹɪɚ> ::= {<ɋɩɟɰɢɚɥɶɧɨɟ ɜɵɪɚɠɟɧɢɟ ɬɢɩɚ Long> ½ [+:]<ɇɚɱɚɥɶɧɵɣ ɧɨɦɟɪ> [..<Ʉɨɧɟɱɧɵɣ ɧɨɦɟɪ> [:<ɒɚɝ>]]} <ɇɨɦɟɪ Ⱦɚɧɧɨɝɨ> {<ɋɩɟɰɢɚɥɶɧɨɟ ɜɵɪɚɠɟɧɢɟ ɬɢɩɚ Long> ½ [+:]<ɇɚɱɚɥɶɧɵɣ ɧɨɦɟɪ> [..<Ʉɨɧɟɱɧɵɣ ɧɨɦɟɪ> [:<ɒɚɝ>]]} <Ȼɥɨɤ Ⱦɚɧɧɵɯ Ɉɛɴɟɤɬɚ> ::= <Ɉɩɢɫɚɬɟɥɶ Ⱦɚɧɧɵɯ Ɉɛɴɟɤɬɚ> [; <Ȼɥɨɤ Ⱦɚɧɧɵɯ Ɉɛɴɟɤɬɚ>] <Ɉɩɢɫɚɬɟɥɶ Ⱦɚɧɧɵɯ Ɉɛɴɟɤɬɚ> ::= { For <ɉɟɪɟɦɟɧɧɚɹ ɰɢɤɥɚ> = <ɇɚɱɚɥɶɧɵɣ ɧɨɦɟɪ> To <Ʉɨɧɟɱɧɵɣ ɧɨɦɟɪ> [Step <ɒɚɝ>] Do <Ȼɥɨɤ Ⱦɚɧɧɵɯ Ɉɛɴɟɤɬɚ> End ½ <ɋɩɢɫɨɤ Ⱦɚɧɧɵɯ Ɉɛɴɟɤɬɚ> } <ɋɩɢɫɨɤ Ⱦɚɧɧɵɯ Ɉɛɴɟɤɬɚ> ::= <Ⱦɚɧɧɨɟ Ɉɛɴɟɤɬɚ>[; <ɋɩɢɫɨɤ Ⱦɚɧɧɵɯ Ɉɛɴɟɤɬɚ>] <Ⱦɚɧɧɨɟ Ɉɛɴɟɤɬɚ> ::= <ɂɞɟɧɬɢɮɢɤɚɬɨɪ ɞɚɧɧɵɯ> [[<ɇɨɦɟɪ Ⱦɚɧɧɨɝɨ>]]
2.8.4 Ʉɨɦɦɟɧɬɚɪɢɢ Ⱦɥɹ ɩɨɧɹɬɧɨɫɬɢ ɨɩɢɫɚɧɢɣ ɤɨɦɩɨɧɟɧɬɨɜ ɜ ɧɢɯ ɧɟɨɛɯɨɞɢɦɨ ɜɤɥɸɱɚɬɶ ɤɨɦɦɟɧɬɚɪɢɢ. Ʉɨɦɦɟɧɬɚɪɢɟɦ ɹɜɥɹɟɬɫɹ ɥɸɛɚɹ ɫɬɪɨɤɚ (ɢɥɢ ɧɟɫɤɨɥɶɤɨ ɫɬɪɨɤ) ɫɢɦɜɨɥɨɜ, ɡɚɤɥɸɱɟɧɧɵɯ ɜ ɮɢɝɭɪɧɵɟ ɫɤɨɛɤɢ. Ʉɨɦɦɟɧɬɚɪɢɣ ɦɨɠɟɬ ɧɚɯɨɞɢɬɶɫɹ ɜ ɥɸɛɨɦ ɦɟɫɬɟ ɨɩɢɫɚɧɢɹ ɤɨɦɩɨɧɟɧɬɚ. ɉɪɢ ɢɧɬɟɪɩɪɟɬɚɰɢɢ ɢɥɢ ɤɨɦɩɢɥɹɰɢɢ ɨɩɢɫɚɧɢɹ ɤɨɦɦɟɧɬɚɪɢɢ ɢɝɧɨɪɢɪɭɸɬɫɹ (ɢɫɤɥɸɱɚɸɬɫɹ ɢɡ ɬɟɤɫɬɚ).
2.8.5 Ɉɛɥɚɫɬɶ ɞɟɣɫɬɜɢɹ ɩɟɪɟɦɟɧɧɵɯ ȼɫɟ ɢɞɟɧɬɢɮɢɤɚɬɨɪɵ ɫɨɫɬɨɹɬ ɢɡ ɩɪɨɢɡɜɨɥɶɧɵɯ ɤɨɦɛɢɧɚɰɢɣ ɥɚɬɢɧɫɤɢɯ ɛɭɤɜ, ɰɢɮɪ ɢ ɩɨɞɱɟɪɤɨɜ. ɉɟɪɜɵɦ ɫɢɦɜɨɥɨɦ ɢɦɟɧɢ ɨɛɹɡɚɬɟɥɶɧɨ ɹɜɥɹɟɬɫɹ ɛɭɤɜɚ. ɂɫɩɨɥɶɡɨɜɚɧɢɟ ɛɭɤɜ ɬɨɥɶɤɨ ɥɚɬɢɧɫɤɨɝɨ ɚɥɮɚɜɢɬɚ ɫɜɹɡɚɧɨ ɫ ɬɟɦ, ɱɬɨ ɤɨɞɵ, ɢɫɩɨɥɶɡɭɟɦɵɟ ɛɨɥɶɲɢɧɫɬɜɨɦ ɤɨɦɩɶɸɬɟɪɨɜ, ɢɦɟɸɬ ɨɞɢɧɚɤɨɜɭɸ ɤɨɞɢɪɨɜɤɭ ɞɥɹ ɛɭɤɜ ɥɚɬɢɧɫɤɨɝɨ ɚɥɮɚɜɢɬɚ, ɬɨɝɞɚ ɤɚɤ ɞɥɹ ɛɭɤɜ ɧɚɰɢɨɧɚɥɶɧɵɯ ɚɥɮɚɜɢɬɨɜ ɞɪɭɝɢɯ ɫɬɪɚɧ ɤɨɞɢɪɨɜɤɚ ɪɚɡɥɢɱɧɚ ɧɟ ɬɨɥɶɤɨ ɨɬ ɤɨɦɩɶɸɬɟɪɚ ɤ ɤɨɦɩɶɸɬɟɪɭ ɧɨ ɢ ɨɬ ɨɞɧɨɣ ɨɩɟɪɚɰɢɨɧɧɨɣ ɫɢɫɬɟɦɵ ɤ ɞɪɭɝɨɣ. Ɂɚɝɥɚɜɧɵɟ ɢ ɩɪɨɩɢɫɧɵɟ ɛɭɤɜɵ ɧɟ ɪɚɡɥɢɱɚɸɬɫɹ ɧɢ ɜ ɢɦɟɧɚɯ, ɧɢ ɜ ɤɥɸɱɟɜɵɯ ɫɥɨɜɚɯ. əɡɵɤɢ ɨɩɢɫɚɧɢɹ ɧɟɤɨɬɨɪɵɯ ɤɨɦɩɨɧɟɧɬɨɜ ɩɨɡɜɨɥɹɸɬ ɨɩɢɫɵɜɚɬɶ ɝɥɨɛɚɥɶɧɵɟ ɩɟɪɟɦɟɧɧɵɟ. ɗɬɢ ɩɟɪɟɦɟɧɧɵɟ ɞɨɫɬɭɩɧɵ ɜɨ ɜɫɟɯ ɮɭɧɤɰɢɹɯ ɢ ɩɪɨɰɟɞɭɪɧɵɯ ɛɥɨɤɚɯ ɞɚɧɧɨɝɨ ɤɨɦɩɨɧɟɧɬɚ. Ɏɭɧɤɰɢɹɦ ɢ ɩɪɨɰɟɞɭɪɧɵɦ ɛɥɨɤɚɦ ɞɪɭɝɢɯ ɤɨɦɩɨɧɟɧɬɨɜ ɷɬɢ ɩɟɪɟɦɟɧɧɵɟ ɧɟɞɨɫɬɭɩɧɵ. ȼɫɟ ɨɫɬɚɥɶɧɵɟ ɩɟɪɟɦɟɧɧɵɟ (ɨɩɢɫɚɧɧɵɟ ɜ ɛɥɨɤɚɯ Var ɢ Static) ɹɜɥɹɸɬɫɹ ɥɨɤɚɥɶɧɵɦɢ ɢ ɞɨɫɬɭɩɧɵ ɬɨɥɶɤɨ ɜ ɩɪɟɞɟɥɚɯ ɬɨɣ ɮɭɧɤɰɢɢ ɢɥɢ ɩɪɨɰɟɞɭɪɧɨɝɨ ɛɥɨɤɚ, ɜ ɤɨɬɨɪɨɦ ɨɧɢ ɨɩɢɫɚɧɵ. ɋɬɚɬɢɱɟɫɤɢɟ ɩɟɪɟɦɟɧɧɵɟ ɫɨɯɪɚɧɹɸɬ ɫɜɨɟ ɡɧɚɱɟɧɢɟ ɦɟɠɞɭ ɜɵɡɨɜɚɦɢ ɮɭɧɤɰɢɣ ɢɥɢ ɩɪɨɰɟɞɭɪɧɵɯ ɛɥɨɤɨɜ, ɬɨɝɞɚ ɤɚɤ ɩɟɪɟɦɟɧɧɵɟ, ɨɩɢɫɚɧɧɵɟ ɜ ɛɥɨɤɚɯ Var ɧɟ ɫɨɯɪɚɧɹɸɬ. ȼ ɧɟɤɨɬɨɪɵɯ ɤɨɦɩɨɧɟɧɬɚɯ ɨɩɪɟɞɟɥɟɧɵ ɫɬɚɧɞɚɪɬɧɵɟ ɩɟɪɟɦɟɧɧɵɟ ɢ ɦɚɫɫɢɜɵ (ɫɦ. ɧɚɩɪɢɦɟɪ ɨɩɢɫɚɧɢɟ ɹɡɵɤɚ ɨɩɢɫɚɧɢɹ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ). ȼ ɬɚɤɢɯ ɪɚɡɞɟɥɚɯ ɨɛɥɚɫɬɶ ɞɨɫɬɭɩɧɨɫɬɢ ɩɪɟɞɨɩɪɟɞɟɥɟɧɧɵɯ ɩɟɪɟɦɟɧɧɵɯ ɨɝɨɜɚɪɢɜɚɟɬɫɹ ɨɬɞɟɥɶɧɨ. ɉɟɪɟɦɟɧɧɚɹ Error ɹɜɥɹɟɬɫɹ ɝɥɨɛɚɥɶɧɨɣ ɞɥɹ ɜɫɟɯ ɤɨɦɩɨɧɟɧɬɨɜ. Ƚɥɨɛɚɥɶɧɨɣ ɹɜɥɹɟɬɫɹ ɬɚɤɠɟ ɩɟɪɟɦɟɧɧɚɹ ErrorManager. Ɉɞɧɚɤɨ ɧɟ ɪɟɤɨɦɟɧɞɭɟɬɫɹ ɢɫɩɨɥɶɡɨɜɚɧɢɟ ɷɬɢɯ ɩɟɪɟɦɟɧɧɵɯ ɩɭɬɟɦ ɩɪɹɦɨɝɨ ɨɛɪɚɳɟɧɢɹ ɤ ɧɢɦ. Ⱦɥɹ ɩɨɥɭɱɟɧɢɹ ɡɧɚɱɟɧɢɹ ɩɟɪɟɦɟɧɧɨɣ Error ɫɥɭɠɢɬ ɡɚɩɪɨɫ GetError, ɢɫɩɨɥɧɹɟɦɵɣ ɦɚɤɪɨɤɨɦɩɨɧɟɧɬɨɦ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪ.
2.8.6 Ɉɫɧɨɜɧɵɟ ɨɩɟɪɚɬɨɪɵ Ɉɩɟɪɚɬɨɪ ɩɪɢɫɜɚɢɜɚɧɢɹ ɫɨɫɬɨɢɬ ɢɡ ɞɜɭɯ ɱɚɫɬɟɣ, ɪɚɡɞɟɥɟɧɧɵɯ ɡɧɚɤɨɦ “=“. ȼ ɥɟɜɨɣ ɱɚɫɬɢ ɨɩɟɪɚɬɨɪɚ ɩɪɢɫɜɚɢɜɚɧɢɹ ɦɨɝɭɬ ɭɱɚɫɬɜɨɜɚɬɶ ɢɦɟɧɚ ɥɸɛɵɯ ɩɟɪɟɦɟɧɧɵɯ. ȼ ɜɵɪɚɠɟɧɢɢ, ɫɬɨɹɳɟɦ ɜ ɩɪɚɜɨɣ ɱɚɫɬɢ ɨɩɟɪɚɬɨɪɚ ɩɪɢɫɜɚɢɜɚɧɢɹ ɦɨɝɭɬ ɭɱɚɫɬɜɨɜɚɬɶ ɥɸɛɵɟ ɩɟɪɟɦɟɧɧɵɟ, ɚɪɝɭɦɟɧɬɵ ɩɪɨɰɟɞɭɪɧɨɝɨ ɛɥɨɤɚ ɢ ɤɨɧɫɬɚɧɬɵ. ȼ ɫɥɭɱɚɟ ɧɟɫɨɨɬɜɟɬɫɬɜɢɹ ɬɢɩɚ ɜɵɪɚɠɟɧɢɹ ɜ ɩɪɚɜɨɣ ɱɚɫɬɢ ɢ ɬɢɩɚ ɩɟɪɟɦɟɧɧɨɣ ɜ ɥɟɜɨɣ ɱɚɫɬɢ ɨɩɟɪɚɬɨɪɚ ɩɪɢɫɜɚɢɜɚɧɢɹ ɩɪɨɢɡɜɨɞɢɬɫɹ ɩɪɢɜɟɞɟɧɢɟ ɬɢɩɚ. ȼɫɟ ɜɵɪɚɠɟɧɢɹ ɜɵɱɢɫɥɹɸɬɫɹ ɫɥɟɜɚ ɧɚ ɩɪɚɜɨ ɫ ɭɱɟɬɨɦ ɫɬɚɪɲɢɧɫɬɜɚ ɨɩɟɪɚɰɢɣ. Ɉɩɟɪɚɬɨɪ ɜɟɬɜɥɟɧɢɹ. Ɉɩɟɪɚɬɨɪ ɜɟɬɜɥɟɧɢɹ ɫɨɫɬɨɢɬ ɢɡ ɬɪɟɯ ɱɚɫɬɟɣ, ɤɚɠɞɚɹ ɢɡ ɤɨɬɨɪɵɯ ɧɚɱɢɧɚɟɬɫɹ ɫɨɨɬɜɟɬɫɬɜɭɸɳɢɦ ɤɥɸɱɟɜɵɦ ɫɥɨɜɨɦ. ɉɟɪɜɚɹ ɱɚɫɬɶ – ɭɫɥɨɜɢɟ, ɧɚɱɢɧɚɟɬɫɹ ɫ ɤɥɸɱɟɜɨɝɨ ɫɥɨɜɚ If ɢ ɫɨɞɟɪɠɢɬ ɥɨɝɢɱɟɫɤɨɟ ɜɵɪɚɠɟɧɢɟ. ȼ ɡɚɜɢɫɢɦɨɫɬɢ ɨɬ ɡɧɚɱɟɧɢɹ ɜɵɱɢɫɥɟɧɧɨɝɨ ɥɨɝɢɱɟɫɤɨɝɨ ɜɵɪɚɠɟɧɢɹ ɜɵɩɨɥɧɹɟɬɫɹ Then ɱɚɫɬɶ (ɢɫɬɢɧɚ) ɢɥɢ Else ɱɚɫɬɶ (ɥɨɠɶ). Ɍɪɟɬɶɹ (Else) ɱɚɫɬɶ ɨɩɟɪɚɬɨɪɚ ɦɨɠɟɬ ɛɵɬɶ ɨɩɭɳɟɧɚ. Ʉɚɠɞɚɹ ɢɡ ɜɵɩɨɥɧɹɟɦɵɯ ɱɚɫɬɟɣ ɫɨɫɬɨɢɬ ɢɡ ɤɥɸɱɟɜɨɝɨ ɫɥɨɜɚ ɢ ɨɩɟɪɚɬɨɪɚ. ɉɪɢ ɧɟɨɛɯɨɞɢɦɨɫɬɢ ɜɵɩɨɥɧɢɬɶ ɧɟɫɤɨɥɶɤɨ ɨɩɟɪɚɬɨɪɨɜ, ɧɟɨɛɯɨɞɢɦɨ ɢɫɩɨɥɶɡɨɜɚɬɶ ɨɩɟɪɚɬɨɪɧɵɟ ɫɤɨɛɤɢ Begin End. ɐɢɤɥ For ɢɦɟɟɬ ɫɥɟɞɭɸɳɢɣ ɜɢɞ: For ɉɟɪɟɦɟɧɧɚɹ_ɰɢɤɥɚ = ɇɚɱɚɥɶɧɨɟ_ɡɧɚɱɟɧɢɟ To Ʉɨɧɟɱɧɨɟ_ɡɧɚɱɟɧɢɟ [By ɒɚɝ] Do <Ɉɩɟɪɚɬɨɪ>
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ɉɟɪɟɦɟɧɧɚɹ ɰɢɤɥɚ ɞɨɥɠɧɚ ɛɵɬɶ ɨɞɧɨɝɨ ɢɡ ɰɟɥɨɱɢɫɥɟɧɧɵɯ ɬɢɩɨɜ. ȼ ɯɨɞɟ ɜɵɩɨɥɧɟɧɢɹ ɨɩɟɪɚɬɨɪɚ ɨɧɚ ɩɪɨɛɟɝɚɟɬ ɡɧɚɱɟɧɢɹ ɨɬ ɇɚɱɚɥɶɧɨɟ_ɡɧɚɱɟɧɢɟ ɞɨ Ʉɨɧɟɱɧɨɟ_ɡɧɚɱɟɧɢɟ ɫ ɲɚɝɨɦ ɒɚɝ. ȿɫɥɢ ɨɩɢɫɚɧɢɟ ɲɚɝɚ ɨɩɭɳɟɧɨ, ɬɨ ɲɚɝ ɪɚɜɟɧ ɟɞɢɧɢɰɟ. ɉɪɢ ɤɚɠɞɨɦ ɡɧɚɱɟɧɢɢ ɩɟɪɟɦɟɧɧɨɣ ɰɢɤɥɚ ɢɡ ɞɢɚɩɚɡɨɧɚ ɜɵɩɨɥɧɹɟɬɫɹ ɨɩɟɪɚɬɨɪ, ɹɜɥɹɸɳɢɣɫɹ ɬɟɥɨɦ ɰɢɤɥɚ. ȿɫɥɢ ɜ ɬɟɥɟ ɰɢɤɥɚ ɧɟɨɛɯɨɞɢɦɨ ɜɵɩɨɥɧɢɬɶ ɧɟɫɤɨɥɶɤɨ ɨɩɟɪɚɬɨɪɨɜ, ɬɨ ɧɟɨɛɯɨɞɢɦɨ ɜɨɫɩɨɥɶɡɨɜɚɬɶɫɹ ɨɩɟɪɚɬɨɪɧɵɦɢ ɫɤɨɛɤɚɦɢ. Ⱦɨɩɭɫɤɚɟɬɫɹ ɥɸɛɨɟ ɱɢɫɥɨ ɜɥɨɠɟɧɧɵɯ ɰɢɤɥɨɜ. ȼɵɩɨɥɧɟɧɢɟ ɰɢɤɥɚ ɜ ɡɚɜɢɫɢɦɨɫɬɢ ɨɬ ɫɨɨɬɧɨɲɟɧɢɹ ɦɟɠɞɭ ɡɧɚɱɟɧɢɹɦɢ ɇɚɱɚɥɶɧɨɟ_ɡɧɚɱɟɧɢɟ, Ʉɨɧɟɱɧɨɟ_ɡɧɚɱɟɧɢɟ ɢ ɒɚɝ ɩɪɢɜɟɞɟɧɨ ɜ ɬɚɛɥ. 14. Ɍɚɛɥɢɰɚ 14. ɋɩɨɫɨɛ ɜɵɩɨɥɧɟɧɢɹ ɰɢɤɥɚ ɜ ɡɚɜɢɫɢɦɨɫɬɢ ɨɬ ɡɧɚɱɟɧɢɣ ɩɚɪɚɦɟɬɪɨɜ ɰɢɤɥɚ. Ʉɨɧɟɱɧɨɟ ɡɧɚɱɟɧɢɟɒɚɝɋɩɨɫɨɛ ɜɵɩɨɥɧɟɧɢɹ >ɇɚɱɚɥɶɧɨɝɨ ɡɧɚɱɟɧɢɹ >0 ɐɢɤɥ ɜɵɩɨɥɧɹɟɬɫɹ ɩɨɤɚ ɩɟɪɟɦɟɧɧɚɹ ɰɢɤɥɚ £ Ʉɨɧɟɱɧɨɝɨ ɡɧɚɱɟɧɢɹ <ɇɚɱɚɥɶɧɨɝɨ ɡɧɚɱɟɧɢɹ >0 Ɍɟɥɨ ɰɢɤɥɚ ɧɟ ɜɵɩɨɥɧɹɟɬɫɹ =ɇɚɱɚɥɶɧɨɦɭ ɡɧɚɱɟɧɢɸ ¹0 Ɍɟɥɨ ɰɢɤɥɚ ɜɵɩɨɥɧɹɟɬɫɹ ɨɞɢɧ ɪɚɡ >ɇɚɱɚɥɶɧɨɝɨ ɡɧɚɱɟɧɢɹ <0 Ɍɟɥɨ ɰɢɤɥɚ ɧɟ ɜɵɩɨɥɧɹɟɬɫɹ <ɇɚɱɚɥɶɧɨɝɨ ɡɧɚɱɟɧɢɹ <0 ɐɢɤɥ ɜɵɩɨɥɧɹɟɬɫɹ ɩɨɤɚ ɩɟɪɟɦɟɧɧɚɹ ɰɢɤɥɚ ³ Ʉɨɧɟɱɧɨɝɨ ɡɧɚɱɟɧɢɹ =0 Ɍɟɥɨ ɰɢɤɥɚ ɧɟ ɜɵɩɨɥɧɹɟɬɫɹ ɐɢɤɥ While. Ɍɟɥɨ ɰɢɤɥɚ ɜɵɩɨɥɧɹɟɬɫɹ ɞɨ ɬɟɯ ɩɨɪ, ɩɨɤɚ ɜɟɪɧɨ ɥɨɝɢɱɟɫɤɨɟ ɜɵɪɚɠɟɧɢɟ. ɉɪɨɜɟɪɤɚ ɢɫɬɢɧɧɨɫɬɢ ɥɨɝɢɱɟɫɤɨɝɨ ɜɵɪɚɠɟɧɢɹ ɩɪɨɢɡɜɨɞɢɬɫɹ ɩɟɪɟɞ ɜɵɩɨɥɧɟɧɢɟɦ ɬɟɥɚ ɰɢɤɥɚ. ȿɫɥɢ ɬɟɥɨ ɰɢɤɥɚ ɞɨɥɠɧɨ ɫɨɞɟɪɠɚɬɶ ɛɨɥɟɟ ɨɞɧɨɝɨ ɨɩɟɪɚɬɨɪɚ, ɬɨ ɧɟɨɛɯɨɞɢɦɨ ɢɫɩɨɥɶɡɨɜɚɬɶ ɨɩɟɪɚɬɨɪɧɵɟ ɫɤɨɛɤɢ.
2.8.7 Ɉɩɢɫɚɧɢɟ ɪɚɫɩɪɟɞɟɥɟɧɢɹ ɫɢɝɧɚɥɨɜ Ɋɚɡɞɟɥ ɨɩɢɫɚɧɢɹ ɪɚɫɩɪɟɞɟɥɟɧɢɹ ɫɢɝɧɚɥɨɜ ɧɚɱɢɧɚɟɬɫɹ ɫ ɤɥɸɱɟɜɨɝɨ ɫɥɨɜɚ Connections. Ɂɚ ɤɥɸɱɟɜɵɦ ɫɥɨɜɨɦ Connections ɫɥɟɞɭɟɬ ɨɞɧɚ ɢɥɢ ɧɟɫɤɨɥɶɤɨ ɝɪɭɩɩ ɫɨɨɬɜɟɬɫɬɜɢɣ. Ʉɚɠɞɚɹ ɝɪɭɩɩɚ ɫɨɨɬɜɟɬɫɬɜɢɣ ɫɨɫɬɨɢɬ ɢɡ ɩɪɚɜɨɣ ɢ ɥɟɜɨɣ ɱɚɫɬɟɣ, ɪɚɡɞɟɥɟɧɧɵɯ ɫɢɦɜɨɥɚɦɢ «<=>« ɢ ɨɩɢɫɵɜɚɟɬ ɫɨɨɬɜɟɬɫɬɜɢɟ ɢɦɟɧ ɫɢɝɧɚɥɨɜ (ɩɚɪɚɦɟɬɪɨɜ) ɪɚɡɥɢɱɧɵɯ ɫɬɪɭɤɬɭɪɧɵɯ ɟɞɢɧɢɰ. Ʉɚɠɞɚɹ ɱɚɫɬɶ ɝɪɭɩɩɵ ɫɨɨɬɜɟɬɫɬɜɢɣ ɩɪɟɞɫɬɚɜɥɹɟɬ ɫɨɛɨɣ ɫɩɢɫɨɤ ɫɢɝɧɚɥɨɜ (ɩɚɪɚɦɟɬɪɨɜ) ɢɥɢ ɢɧɬɟɪɜɚɥɨɜ ɫɢɝɧɚɥɨɜ (ɩɚɪɚɦɟɬɪɨɜ), ɪɚɡɞɟɥɟɧɧɵɯ ɦɟɠɞɭ ɫɨɛɨɣ ɫɢɦɜɨɥɨɦ «;». ɍɤɚɡɚɧɧɵɟ ɜ ɥɟɜɨɣ ɢ ɩɪɚɜɨɣ ɱɚɫɬɹɯ ɫɢɝɧɚɥɵ (ɩɚɪɚɦɟɬɪɵ) ɨɬɨɠɞɟɫɬɜɥɹɸɬɫɹ. ȿɫɥɢ ɩɪɢ ɭɤɚɡɚɧɢɢ ɫɢɝɧɚɥɚ (ɩɚɪɚɦɟɬɪɚ) ɧɟ ɭɤɚɡɚɧɨ ɢɦɹ ɩɨɞɨɛɴɟɤɬɚ, ɬɨ ɷɬɨ ɫɢɝɧɚɥ (ɩɚɪɚɦɟɬɪ) ɨɩɢɫɵɜɚɟɦɨɝɨ ɨɛɴɟɤɬɚ. ɂɫɩɨɥɶɡɨɜɚɧɢɟ ɢɧɬɟɪɜɚɥɚ ɫɢɝɧɚɥɨɜ (ɩɚɪɚɦɟɬɪɨɜ) ɜ ɩɪɚɜɨɣ ɢɥɢ ɥɟɜɨɣ ɱɚɫɬɢ ɝɪɭɩɩɵ ɫɨɨɬɜɟɬɫɬɜɢɣ ɪɚɜɧɨɫɢɥɶɧɨ ɩɟɪɟɱɢɫɥɟɧɢɸ ɫɢɝɧɚɥɨɜ (ɩɚɪɚɦɟɬɪɨɜ), ɫ ɧɨɦɟɪɚɦɢ, ɜɯɨɞɹɳɢɦɢ ɜ ɢɧɬɟɪɜɚɥ, ɧɚɱɢɧɚɹ ɫ ɧɚɱɚɥɶɧɨɝɨ ɧɨɦɟɪɚ c ɲɚɝɨɦ, ɭɤɚɡɚɧɧɵɦ ɩɨɫɥɟ ɫɢɦɜɨɥɚ «:». ȿɫɥɢ ɲɚɝ ɧɟ ɭɤɚɡɚɧ, ɬɨ ɨɧ ɩɨɥɚɝɚɟɬɫɹ ɪɚɜɧɵɦ ɟɞɢɧɢɰɟ. ɑɢɫɥɨ ɫɢɝɧɚɥɨɜ ɜ ɩɪɚɜɨɣ ɢ ɥɟɜɨɣ ɱɚɫɬɹɯ ɝɪɭɩɩɵ ɫɨɨɬɜɟɬɫɬɜɢɣ ɞɨɥɠɧɨ ɫɨɜɩɚɞɚɬɶ. ȿɫɥɢ ɢɧɬɟɪɜɚɥ ɩɭɫɬ (ɧɚɩɪɢɦɟɪ [2..1:1]), ɬɨ ɨɩɢɫɵɜɚɟɦɚɹ ɢɦ ɝɪɭɩɩɚ ɫɢɝɧɚɥɨɜ ɫɱɢɬɚɟɬɫɹ ɨɬɫɭɬɫɬɜɭɸɳɟɣ ɢ ɩɪɨɩɭɫɤɚɟɬɫɹ. ɉɪɢ ɢɫɩɨɥɶɡɨɜɚɧɢɢ ɜ ɨɩɢɫɚɧɢɢ ɫɨɨɬɜɟɬɫɬɜɢɣ ɹɜɧɵɯ ɰɢɤɥɨɜ, ɜɨ ɜɫɟɯ ɜɵɪɚɠɟɧɢɹɯ ɜɧɭɬɪɢ ɰɢɤɥɚ ɜɨɡɦɨɠɧɨ ɢɫɩɨɥɶɡɨɜɚɧɢɟ ɩɟɪɟɦɟɧɧɨɣ ɰɢɤɥɚ. ɉɪɢ ɷɬɨɦ ɩɨɞɪɚɡɭɦɟɜɚɟɬɫɹ ɫɥɟɞɭɸɳɢɣ ɩɨɪɹɞɨɤ ɩɟɪɟɱɢɫɥɟɧɢɹ: ɋɧɚɱɚɥɚ ɢɡɦɟɧɹɟɬɫɹ ɧɨɦɟɪ ɜ ɫɚɦɨɦ ɩɪɚɜɨɦ ɢɧɬɟɪɜɚɥɟ, ɞɚɥɟɟ ɜɨ ɜɬɨɪɨɦ ɫɩɪɚɜɚ, ɢ ɬ.ɞ. ȼ ɩɨɫɥɟɞɧɸɸ ɨɱɟɪɟɞɶ ɢɡɦɟɧɹɸɬɫɹ ɡɧɚɱɟɧɢɹ ɩɟɪɟɦɟɧɧɵɯ ɰɢɤɥɚ ɹɜɧɵɯ ɰɢɤɥɨɜ ɜ ɩɨɪɹɞɤɟ ɢɯ ɜɥɨɠɟɧɧɨɫɬɢ (ɩɟɪɟɦɟɧɧɚɹ ɫɚɦɨɝɨ ɜɧɭɬɪɟɧɧɟɝɨ ɰɢɤɥɚ ɦɟɧɹɟɬɫɹ ɩɟɪɜɨɣ ɢ ɬ.ɞ.). Ɋɚɫɫɦɨɬɪɢɦ ɫɥɟɞɭɸɳɢɣ ɩɪɢɦɟɪ ɨɩɢɫɚɧɢɹ ɝɪɭɩɩɵ ɫɨɨɬɜɟɬɫɬɜɢɣ ɛɥɨɤɚ, ɫɨɞɟɪɠɚɳɟɝɨ ɞɜɟ ɫɟɬɢ Net ɫ 3 ɜɯɨɞɚɦɢ ɤɚɠɞɚɹ. ɇɢɠɟ ɩɪɢɜɟɞɟɧɨ ɞɜɟ ɪɚɡɥɢɱɧɵɯ ɫɬɪɭɤɬɭɪɵ ɫɜɹɡɟɣ ɩɨ ɧɟɫɤɨɥɶɤɨ ɷɤɜɢɜɚɥɟɧɬɧɵɯ ɜɚɪɢɚɧɬɨɜ ɨɩɢɫɚɧɢɹ. ɋɥɭɱɚɣ 1. ȿɫɬɟɫɬɜɟɧɧɵɣ ɩɨɪɹɞɨɤ ɫɜɹɡɟɣ. ȼɚɪɢɚɧɬ 1. InSignals[1] <=> Net[1].InSignals[1] InSignals[2] <=> Net[1].InSignals[2] InSignals[3] <=> Net[1].InSignals[3] InSignals[4] <=> Net[2].InSignals[1] InSignals[5] <=> Net[2].InSignals[2] InSignals[6] <=> Net[2].InSignals[3] ȼɚɪɢɚɧɬ 2. InSignals[1..6] <=> Net[1..2].InSignals[1..3] ȼɚɪɢɚɧɬ 3. InSignals[1]; InSignals[2]; InSignals[3]; InSignals[4]; InSignals[5]; InSignals[6] <=> For I=1 To 3 Do For J=1 To 2 Do Net[J].InSignals[I] End End ɋɥɭɱɚɣ 2. Ⱦɪɭɝɨɣ ɩɨɪɹɞɨɤ ɫɜɹɡɟɣ. ȼɚɪɢɚɧɬ 1. InSignals[1] <=> Net[2].InSignals[3] InSignals[2] <=> Net[1].InSignals[3]
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InSignals[3] <=> Net[2].InSignals[2] InSignals[4] <=> Net[1].InSignals[2] InSignals[5] <=> Net[2].InSignals[1] InSignals[6] <=> Net[1].InSignals[1] ȼɚɪɢɚɧɬ 2. InSignals[1..6] <=> For I=3 To 1 Step -1 Do Net[2..1:-1].InSignals[I] End ȼɚɪɢɚɧɬ 3. InSignals[6..1:-2]; InSignals[5..1:-2]<=> For I=1 To 3 Do For J=1 To 2 Do Net[J].InSignals[I] End End
2.8.8 Ɏɭɧɤɰɢɢ ɭɩɪɚɜɥɟɧɢɹ ɩɚɦɹɬɶɸ Ⱦɥɹ ɫɨɡɞɚɧɢɹ ɦɚɫɫɢɜɨɜ ɢ ɨɫɜɨɛɨɠɞɟɧɢɹ ɡɚɧɢɦɚɟɦɨɣ ɢɦɢ ɩɚɦɹɬɢ ɢɫɩɨɥɶɡɭɸɬɫɹ ɫɥɟɞɭɸɳɢɟ ɮɭɧɤɰɢɢ: ɋɨɡɞɚɧɢɟ ɦɚɫɫɢɜɚ. Function NewArray( Type : Integer; Size : Long ) : PRealArray; Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: Type – ɡɚɞɚɟɬ ɪɚɡɦɟɪ ɷɥɟɦɟɧɬɚ Ɍɚɛɥɢɰɚ 15. ɦɚɫɫɢɜɚ ɢ ɹɜɥɹɟɬɫɹ ɨɞɧɨɣ ɢɡ ɩɪɟɞɨɩɪɟɞɟɉɪɟɞɨɩɪɟɞɟɥɟɧɧɵɟ ɤɨɧɫɬɚɧɬɵ ɬɢɩɨɜ ɷɥɟɦɟɧɬɨɜ ɦɚɫɫɢɜɨɜ ɥɟɧɧɵɯ ɤɨɧɫɬɚɧɬ, ɩɪɢɜɟɞɟɧɧɵɯ ɜ ɬɚɛɥ. 15. ɂɞɟɧɬɢɮɢɤɚɬɨɪɁɧɚɱɟɧɢɟɈɩɢɫɚɧɢɟ Size – ɱɢɫɥɨ ɷɥɟɦɟɧɬɨɜ ɜ ɦɚɫɫɢɜɟ. mRealArray 4 Ɋɚɡɦɟɪ ɷɥɟɦɟɧɬɚ – 4 ɛɚɣɬɚ Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. mIntegerArray 2 Ɋɚɡɦɟɪ ɷɥɟɦɟɧɬɚ – 2 ɛɚɣɬɚ 1. ȿɫɥɢ ɚɪɝɭɦɟɧɬ Type ɧɟ ɫɨɜɩɚɞɚɟɬ mLongArray 4 Ɋɚɡɦɟɪ ɷɥɟɦɟɧɬɚ – 4 ɛɚɣɬɚ ɧɢ ɫ ɨɞɧɨɣ ɢɡ ɩɪɟɞɨɩɪɟɞɟɥɟɧɧɵɯ mLogicArray 1 Ɋɚɡɦɟɪ ɷɥɟɦɟɧɬɚ – 1 ɛɚɣɬ ɤɨɧɫɬɚɧɬ, ɩɪɢɜɟɞɟɧɧɵɯ ɜ ɬɚɛɥ. 15, ɬɨ ɜɨɡɜɪɚɳɚɟɬɫɹ ɡɧɚɱɟɧɢɟ Null, ɢɫɩɨɥɧɟɧɢɟ ɮɭɧɤɰɢɢ ɡɚɜɟɪɲɚɟɬɫɹ. 2. ɋɨɡɞɚɟɬɫɹ ɦɚɫɫɢɜ, ɡɚɧɢɦɚɸɳɢɣ Size*Type+4 ɛɚɣɬɚ. 3. Ⱥɞɪɟɫ ɦɚɫɫɢɜɚ ɜɨɡɜɪɚɳɚɟɬɫɹ ɤɚɤ ɪɟɡɭɥɶɬɚɬ. Ɉɫɜɨɛɨɠɞɟɧɢɟ ɦɚɫɫɢɜɚ. Function FreeArray( Type : Integer; Array : PRealArray ) : Logic; Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: Type – ɡɚɞɚɟɬ ɪɚɡɦɟɪ ɷɥɟɦɟɧɬɚ ɦɚɫɫɢɜɚ ɢ ɹɜɥɹɟɬɫɹ ɨɞɧɨɣ ɢɡ ɩɪɟɞɨɩɪɟɞɟɥɟɧɧɵɯ ɤɨɧɫɬɚɧɬ, ɩɪɢɜɟɞɟɧɧɵɯ ɜ ɬɚɛɥ. 14. Array – ɚɞɪɟɫ ɦɚɫɫɢɜɚ. ɉɚɦɹɬɶ, ɡɚɧɢɦɚɟɦɚɹ ɷɬɢɦ ɦɚɫɫɢɜɨɦ, ɞɨɥɠɧɚ ɛɵɬɶ ɨɫɜɨɛɨɠɞɟɧɚ. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. 1. ȿɫɥɢ ɚɪɝɭɦɟɧɬ Type ɧɟ ɫɨɜɩɚɞɚɟɬ ɧɢ ɫ ɨɞɧɨɣ ɢɡ ɩɪɟɞɨɩɪɟɞɟɥɟɧɧɵɯ ɤɨɧɫɬɚɧɬ, ɩɪɢɜɟɞɟɧɧɵɯ ɜ ɬɚɛɥ. 15, ɬɨ ɜɨɡɜɪɚɳɚɟɬɫɹ ɡɧɚɱɟɧɢɟ False, ɢɫɩɨɥɧɟɧɢɟ ɮɭɧɤɰɢɢ ɡɚɜɟɪɲɚɟɬɫɹ. 2. Ɉɫɜɨɛɨɠɞɚɟɬɫɹ ɩɚɦɹɬɶ ɪɚɡɦɟɪɨɦ TReal(Array[0])*Type+4 ɛɚɣɬɚ. 3. Ⱥɪɝɭɦɟɧɬɭ Array ɩɪɢɫɜɚɢɜɚɟɬɫɹ ɡɧɚɱɟɧɢɟ Null ɉɟɪɟɫɨɡɞɚɧɢɟ ɦɚɫɫɢɜɚ. Function ReCreateArray( Type : Integer; Array : PRealArray; Size : Long ) : Logic; Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: Type – ɡɚɞɚɟɬ ɪɚɡɦɟɪ ɷɥɟɦɟɧɬɚ ɦɚɫɫɢɜɚ ɢ ɹɜɥɹɟɬɫɹ ɨɞɧɨɣ ɢɡ ɩɪɟɞɨɩɪɟɞɟɥɟɧɧɵɯ ɤɨɧɫɬɚɧɬ, ɩɪɢɜɟɞɟɧɧɵɯ ɜ ɬɚɛɥ. 15. Array – ɚɞɪɟɫ ɦɚɫɫɢɜɚ. Size – ɱɢɫɥɨ ɷɥɟɦɟɧɬɨɜ ɜ ɦɚɫɫɢɜɟ. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. 1. ȿɫɥɢ ɚɪɝɭɦɟɧɬ Type ɧɟ ɫɨɜɩɚɞɚɟɬ ɧɢ ɫ ɨɞɧɨɣ ɢɡ ɩɪɟɞɨɩɪɟɞɟɥɟɧɧɵɯ ɤɨɧɫɬɚɧɬ, ɩɪɢɜɟɞɟɧɧɵɯ ɜ ɬɚɛɥ. 15, ɬɨ ɜɨɡɜɪɚɳɚɟɬɫɹ ɡɧɚɱɟɧɢɟ False, ɢɫɩɨɥɧɟɧɢɟ ɮɭɧɤɰɢɢ ɡɚɜɟɪɲɚɟɬɫɹ. 2. ȿɫɥɢ ɚɪɝɭɦɟɧɬ Array ɧɟ ɪɚɜɟɧ Null, ɢ TReal(Array[0]) ɪɚɜɟɧ Size, ɬɨ ɜɨɡɜɪɚɳɚɟɬɫɹ ɡɧɚɱɟɧɢɟ True, ɜɵɩɨɥɧɟɧɢɟ ɮɭɧɤɰɢɢ ɡɚɜɟɪɲɚɟɬɫɹ. 3. ȿɫɥɢ ɚɪɝɭɦɟɧɬ Array ɧɟ ɪɚɜɟɧ Null, ɢ TReal(Array[0]) ɧɟ ɪɚɜɟɧ Size, ɬɨ ɨɫɜɨɛɨɠɞɚɟɬɫɹ ɩɚɦɹɬɶ ɪɚɡɦɟɪɨɦ TReal(Array[0])*Type+4 ɛɚɣɬɚ. Ⱥɪɝɭɦɟɧɬɭ Array ɩɪɢɫɜɚɢɜɚɟɬɫɹ ɡɧɚɱɟɧɢɟ Null 4. Ⱥɪɝɭɦɟɧɬɭ Array ɩɪɢɫɜɚɢɜɚɟɬɫɹ ɡɧɚɱɟɧɢɟ NewArray(Type,Size), ɜɨɡɜɪɚɳɚɟɬɫɹ ɡɧɚɱɟɧɢɟ True, ɢɫɩɨɥɧɟɧɢɟ ɮɭɧɤɰɢɢ ɡɚɜɟɪɲɚɟɬɫɹ.
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2.9 ɂɫɩɨɥɶɡɨɜɚɧɢɟ ɩɚɦɹɬɢ Ɋɹɞ ɡɚɩɪɨɫɨɜ, ɢɫɩɨɥɧɹɟɦɵɯ ɪɚɡɥɢɱɧɵɦɢ ɤɨɦɩɨɧɟɧɬɚɦɢ, ɜɨɡɜɪɚɳɚɸɬ ɜ ɤɚɱɟɫɬɜɟ ɨɬɜɟɬɚ ɭɤɚɡɚɬɟɥɢ ɧɚ ɦɚɫɫɢɜɵ. ȼ ɷɬɢɯ ɫɥɭɱɚɹɯ ɞɟɣɫɬɜɭɸɬ ɫɥɟɞɭɸɳɢɟ ɩɪɚɜɢɥɚ: 1. ȿɫɥɢ ɤɨɦɩɨɧɟɧɬ ɩɨɥɭɱɢɥ ɩɭɫɬɨɣ ɭɤɚɡɚɬɟɥɶ (Null), ɬɨ ɨɧ ɫɚɦ ɫɨɡɞɚɟɬ ɦɚɫɫɢɜ ɧɟɨɛɯɨɞɢɦɨɣ ɞɥɢɧɵ. 2. ȿɫɥɢ ɩɟɪɟɞɚɧ ɧɟɩɭɫɬɨɣ ɭɤɚɡɚɬɟɥɶ, ɧɨ ɫɭɳɟɫɬɜɭɸɳɟɣ ɞɥɢɧɵ ɦɚɫɫɢɜɚ ɧɟɞɨɫɬɚɬɨɱɧɨ, ɬɨ ɤɨɦɩɨɧɟɧɬ ɨɫɜɨɛɨɠɞɚɟɬ ɩɚɦɹɬɶ, ɡɚɧɹɬɭɸ ɩɨɞ ɩɟɪɟɞɚɧɧɵɣ ɦɚɫɫɢɜ ɢ ɫɨɡɞɚɟɬ ɧɨɜɵɣ ɦɚɫɫɢɜ ɧɟɨɛɯɨɞɢɦɨɣ ɞɥɢɧɵ. 3. Ɉɫɜɨɛɨɠɞɟɧɢɟ ɩɚɦɹɬɢ ɩɨɫɥɟ ɢɫɩɨɥɶɡɨɜɚɧɢɹ ɦɚɫɫɢɜɚ ɥɟɠɢɬ ɧɚ ɜɵɡɵɜɚɸɳɟɦ ɤɨɦɩɨɧɟɧɬɟ. ȿɫɥɢ ɨɞɧɨɦɭ ɢɡ ɤɨɦɩɨɧɟɧɬɨɜ ɧɟ ɯɜɚɬɚɟɬ ɩɚɦɹɬɢ ɞɥɹ ɜɵɩɨɥɧɟɧɢɹ ɡɚɩɪɨɫɚ, ɬɨ ɷɬɨɬ ɤɨɦɩɨɧɟɧɬ ɦɨɠɟɬ ɩɟɪɟɞɚɬɶ ɦɚɤɪɨɤɨɦɩɨɧɟɧɬɭ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪ ɡɚɩɪɨɫ ɧɚ ɞɨɩɨɥɧɢɬɟɥɶɧɭɸ ɩɚɦɹɬɶ. ȼ ɷɬɨɦ ɫɥɭɱɚɟ ɦɚɤɪɨɤɨɦɩɨɧɟɧɬ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪ ɩɟɪɟɞɚɟɬ ɜɫɟɦ ɤɨɦɩɨɧɟɧɬɚɦ ɡɚɩɪɨɫ FreeMemory. ɉɪɢ ɢɫɩɨɥɧɟɧɢɢ ɞɚɧɧɨɝɨ ɡɚɩɪɨɫɚ ɤɚɠɞɵɣ ɤɨɦɩɨɧɟɧɬ ɞɨɥɠɟɧ ɨɫɜɨɛɨɞɢɬɶ ɜɫɸ ɩɚɦɹɬɶ, ɧɟ ɹɜɥɹɸɳɭɸɫɹ ɚɛɫɨɥɸɬɧɨ ɧɟɨɛɯɨɞɢɦɨɣ ɞɥɹ ɪɚɛɨɬɵ. ɇɚɩɪɢɦɟɪ, ɤɨɦɩɨɧɟɧɬ ɡɚɞɚɱɧɢɤ ɦɨɠɟɬ ɞɥɹ ɛɵɫɬɪɨɬɵ ɨɛɪɚɛɨɬɤɢ ɞɟɪɠɚɬɶ ɜ ɩɚɦɹɬɢ ɜɫɟ ɨɛɭɱɚɸɳɟɟ ɦɧɨɠɟɫɬɜɨ. Ɉɞɧɚɤɨ ɚɛɫɨɥɸɬɧɨ ɧɟɨɛɯɨɞɢɦɨɣ ɹɜɥɹɟɬɫɹ ɩɚɦɹɬɶ, ɞɨɫɬɚɬɨɱɧɚɹ ɞɥɹ ɯɪɚɧɟɧɢɹ ɜ ɩɚɦɹɬɢ ɨɞɧɨɝɨ ɩɪɢɦɟɪɚ. Ɂɚɩɪɨɫ ɧɚ ɨɫɜɨɛɨɠɞɟɧɢɟ ɩɚɦɹɬɢ ɢɫɩɨɥɧɹɟɬɫɹ ɤɚɠɞɵɦ ɤɨɦɩɨɧɟɧɬɨɦ ɢ ɧɟ ɜɤɥɸɱɟɧ ɜ ɨɩɢɫɚɧɢɹ ɡɚɩɪɨɫɨɜ ɤɨɦɩɨɧɟɧɬɨɜ, ɩɪɢɜɟɞɟɧɧɵɟ ɜ ɫɥɟɞɭɸɳɢɯ ɝɥɚɜɚɯ.
2.10 Ɉɛɪɚɛɨɬɤɚ ɨɲɢɛɨɤ ɋɯɟɦɚ ɨɛɪɚɛɨɬɤɢ ɨɲɢɛɨɤ ɞɨɫɬɚɬɨɱɧɨ ɩɪɨɫɬɚ ɩɨ ɫɜɨɟɣ ɢɞɟɟ - ɤɚɠɞɵɣ ɧɨɜɵɣ ɨɛɪɚɛɨɬɱɢɤ ɨɲɢɛɨɤ ɦɨɠɟɬ ɨɛɪɚɛɚɬɵɜɚɬɶ ɬɨɥɶɤɨ ɱɚɫɬɶ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɭ ɨɫɬɚɥɶɧɵɯ ɦɨɠɟɬ ɩɟɪɟɞɚɬɶ ɪɚɧɟɟ ɭɫɬɚɧɨɜɥɟɧɧɨɦɭ ɨɛɪɚɛɨɬɱɢɤɭ. ɉɨɥɶɡɨɜɚɬɟɥɶ ɦɨɠɟɬ ɨɪɝɚɧɢɡɨɜɚɬɶ ɨɛɪɚɛɨɬɤɭ ɨɲɢɛɨɤ ɢ ɧɟ ɩɪɢɛɟɝɚɹ ɤ ɭɫɬɚɧɨɜɤɟ ɨɛɪɚɛɨɬɱɢɤɚ ɨɲɢɛɨɤ - ɨɛɪɚɛɨɬɱɢɤ ɨɲɢɛɨɤ ɩɨ ɭɦɨɥɱɚɧɢɸ ɩɨɱɬɢ ɜɨ ɜɫɟɯ ɫɥɭɱɚɹɯ ɭɫɬɚɧɚɜɥɢɜɚɟɬ ɧɨɦɟɪ ɩɨɫɥɟɞɧɟɣ ɨɲɢɛɤɢ ɜ ɩɟɪɟɦɟɧɧɭɸ Error, ɤɨɬɨɪɚɹ ɦɨɠɟɬ ɛɵɬɶ ɫɱɢɬɚɧɚ ɫ ɩɨɦɨɳɶɸ ɡɚɩɪɨɫɚ GetError ɢ ɨɛɪɚɛɨɬɚɧɚ ɩɪɹɦɨ ɜ ɤɨɦɩɨɧɟɧɬɟ, ɜɵɞɚɜɲɟɦ ɡɚɩɪɨɫ. ȿɫɥɢ ɨɛɪɚɛɨɬɱɢɤ ɨɲɢɛɨɤ ɭɫɬɚɧɚɜɥɢɜɚɟɬ ɧɨɦɟɪ ɩɨɫɥɟɞɧɟɣ ɨɲɢɛɤɢ ɜ ɩɟɪɟɦɟɧɧɨɣ Error, ɬɨ ɜɫɟ ɡɚɩɪɨɫɵ, ɩɨɫɬɭɩɢɜɲɢɟ ɩɨɫɥɟ ɦɨɦɟɧɬɚ ɭɫɬɚɧɨɜɤɢ, ɡɚɜɟɪɲɚɸɬɫɹ ɧɟɭɫɩɟɲɧɨ. ɗɬɨ ɫɨɫɬɨɹɧɢɟ ɫɛɪɚɫɵɜɚɟɬɫɹ ɩɪɢ ɜɵɡɨɜɟ ɡɚɩɪɨɫɚ «ɞɚɬɶ ɧɨɦɟɪ ɨɲɢɛɤɢ».
2.10.1 ɉɪɨɰɟɞɭɪɚ ɨɛɪɚɛɨɬɤɢ ɨɲɢɛɨɤ ɉɪɨɰɟɞɭɪɚ ɨɛɪɚɛɨɬɤɢ ɨɲɢɛɨɤ ɞɨɥɠɧɚ ɭɞɨɜɥɟɬɜɨɪɹɬɶ ɫɥɟɞɭɸɳɢɦ ɬɪɟɛɨɜɚɧɢɹɦ: I. ɗɬɨ ɞɨɥɠɧɚ ɛɵɬɶ ɩɪɨɰɟɞɭɪɚ ɫ ɞɚɥɶɧɢɦ ɬɢɩɨɦ ɚɞɪɟɫɚɰɢɢ. Ɏɨɪɦɚɬ ɨɩɢɫɚɧɢɹ ɩɪɨɰɟɞɭɪɵ ɨɛɪɚɛɨɬɤɢ ɨɲɢɛɨɤ Pascal: Procedure ErrorFunc( ErrorNumber : Long ); Far; C: void far ErrorFunc(Long ErrorNumber) II. ɉɨɫɥɟ ɨɛɪɚɛɨɬɤɢ ɨɲɢɛɨɤ ɩɪɨɰɟɞɭɪɚ ɦɨɠɟɬ ɜɵɡɜɚɬɶ ɪɚɧɟɟ ɭɫɬɚɧɨɜɥɟɧɧɵɣ ɨɛɪɚɛɨɬɱɢɤ ɨɲɢɛɨɤ. Ⱥɞɪɟɫ ɪɚɧɟɟ ɭɫɬɚɧɨɜɥɟɧɧɨɝɨ ɨɛɪɚɛɨɬɱɢɤɚ ɨɲɢɛɨɤ ɩɪɨɰɟɞɭɪɚ ɨɛɪɚɛɨɬɤɢ ɨɲɢɛɨɤ ɩɨɥɭɱɚɟɬ ɜ ɯɨɞɟ ɫɥɟɞɭɸɳɟɣ ɩɪɨɰɟɞɭɪɵ: A. ȼɵɡɨɜ ɩɪɨɰɟɞɭɪɵ ɫ ɧɭɥɟɜɵɦ ɧɨɦɟɪɨɦ ɨɲɢɛɤɢ ɨɡɧɚɱɚɟɬ, ɱɬɨ ɜ ɫɥɟɞɭɸɳɟɦ ɜɵɡɨɜɟ ɛɭɞɟɬ ɩɟɪɟɞɚɧ ɚɞɪɟɫ ɫɬɚɪɨɣ ɩɪɨɰɟɞɭɪɵ ɨɛɪɚɛɨɬɤɢ ɨɲɢɛɨɤ. B. Ɂɧɚɱɟɧɢɟ ɚɪɝɭɦɟɧɬɚ ErrorNumber ɩɪɢ ɜɵɡɨɜɟ, ɫɥɟɞɭɸɳɟɦ ɧɟɩɨɫɪɟɞɫɬɜɟɧɧɨ ɡɚ ɜɵɡɨɜɨɦ ɫ ɧɭɥɟɜɵɦ ɧɨɦɟɪɨɦ ɨɲɢɛɤɢ, ɞɨɥɠɧɨ ɢɧɬɟɪɩɪɟɬɢɪɨɜɚɬɶɫɹ ɤɚɤ ɚɞɪɟɫ ɫɬɚɪɨɣ ɩɪɨɰɟɞɭɪɵ ɨɛɪɚɛɨɬɤɢ ɨɲɢɛɨɤ. ɇɢɠɟ ɩɪɢɜɟɞɟɧɨ ɨɩɢɫɚɧɢɟ ɡɚɩɪɨɫɨɜ, ɫɜɹɡɚɧɧɵɯ ɫ ɨɛɪɚɛɨɬɤɨɣ ɨɲɢɛɨɤ ɢ ɢɫɩɨɥɧɹɟɦɵɯ ɦɚɤɪɨɤɨɦɩɨɧɟɧɬɨɦ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪ.
2.10.2 ɍɫɬɚɧɨɜɢɬɶ ɨɛɪɚɛɨɬɱɢɤ ɨɲɢɛɨɤ (OnError) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function OnError( NewError : ErrorFunc ) : Logic; C: Logic OnError(ErrorFunc NewError) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: NewError - ɚɞɪɟɫ ɧɨɜɨɣ ɩɪɨɰɟɞɭɪɵ ɨɛɪɚɛɨɬɤɢ ɨɲɢɛɨɤ. ɇɚɡɧɚɱɟɧɢɟ -
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Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. ȿɫɥɢ Error <> 0, ɬɨ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. ȼɵɡɨɜ NewError ɫ ɚɪɝɭɦɟɧɬɨɦ 0 - ɧɚɫɬɪɨɣɤɚ ɧɚ ɭɫɬɚɧɨɜɤɭ ɰɟɩɨɱɤɢ ɨɛɪɚɛɨɬɤɢ ɨɲɢɛɨɤ. ȼɵɡɨɜ NewError ɫ ɚɪɝɭɦɟɧɬɨɦ ErrorManager (ɜɦɟɫɬɨ ɞɥɢɧɧɨɝɨ ɰɟɥɨɝɨ ɩɟɪɟɞɚɟɬɫɹ ɚɞɪɟɫ ɫɬɚɪɨɣ ɩɪɨɰɟɞɭɪɵ ɨɛɪɚɛɨɬɤɢ ɨɲɢɛɨɤ). ErrorManager := NewError
2.10.3 Ⱦɚɬɶ ɧɨɦɟɪ ɨɲɢɛɤɢ (GetError) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function GetError : Integer; C:
1. 2.
Integer GetError() ɇɚɡɧɚɱɟɧɢɟ - ɜɨɡɜɪɚɳɚɟɬ ɧɨɦɟɪ ɩɨɫɥɟɞɧɟɣ ɧɟɨɛɪɚɛɨɬɚɧɧɨɣ ɨɲɢɛɤɢ ɢ ɫɛɪɚɫɵɜɚɟɬ ɟɟ. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. GetError := Error Error := 0
ɋɩɢɫɤɢ ɨɲɢɛɨɤ, ɜɨɡɧɢɤɚɸɳɢɯ ɜ ɪɚɡɥɢɱɧɵɯ ɤɨɦɩɨɧɟɧɬɚɯ, ɞɚɧɵ ɜ ɪɚɡɞɟɥɚɯ «Ɉɲɢɛɤɢ ɤɨɦɩɨɧɟɧɬɵ ...», ɜ ɫɨɨɬɜɟɬɫɬɜɭɸɳɢɯ ɝɥɚɜɚɯ. ȼɫɟ ɧɨɦɟɪɚ ɨɲɢɛɨɤ ɤɚɠɞɨɝɨ ɤɨɦɩɨɧɟɧɬɚ ɹɜɥɹɸɬɫɹ ɬɪɟɯɡɧɚɱɧɵɦɢ ɱɢɫɥɚɦɢ ɢ ɧɚɱɢɧɚɸɬɫɹ ɫ ɧɨɦɟɪɚ ɤɨɦɩɨɧɟɧɬɚ, ɭɤɚɡɚɧɧɨɝɨ ɜ ɤɨɥɨɧɤɟ «Ɉɲɢɛɤɚ» ɬɚɛɥ. 16.
2.11 Ɂɚɩɪɨɫɵ, ɨɞɧɨɬɢɩɧɵɟ ɞɥɹ ɜɫɟɯ ɤɨɦɩɨɧɟɧɬ Ɋɹɞ ɡɚɩɪɨɫɨɜ ɨɛɪɚɛɚɬɵɜɚɟɬɫɹ ɜɫɟɦɢ ɤɨɦɩɨɧɟɧɬɚɦɢ, Ɍɚɛɥɢɰɚ 16 ɤɪɨɦɟ ɤɨɦɩɨɧɟɧɬɚ ɢɫɩɨɥɧɢɬɟɥɶ, ɧɨɫɹɳɟɝɨ ɜɫɩɨɦɨɝɚɬɟɥɶɧɵɣ ɉɪɟɮɢɤɫɵ ɤɨɦɩɨɧɟɧɬ ɯɚɪɚɤɬɟɪ. Ɉɞɢɧ ɢɡ ɬɚɤɢɯ ɡɚɩɪɨɫɨɜ – FreeMemory – ɛɵɥ ɨɩɢɫɚɧ ɉɪɟɮɢɤɫɄɨɦɩɨɧɟɧɬɚ ɜ ɪɚɡɞɟɥɟ «ɍɩɪɚɜɥɟɧɢɟ ɩɚɦɹɬɶɸ», ɚ ɞɜɚ ɡɚɩɪɨɫɚ, ɫɜɹɡɚɧɧɵɯ ɫ ɁɚɩɪɨɫɚɈɲɢɛɤɢ ɨɛɪɚɛɨɬɤɨɣ ɨɲɢɛɨɤ – ɜ ɪɚɡɞɟɥɟ «Ɉɛɪɚɛɨɬɤɚ ɨɲɢɛɨɤ». ȼ ɞɚɧex 0 ɂɫɩɨɥɧɢɬɟɥɶ ɧɨɦ ɪɚɡɞɟɥɟ ɩɪɢɜɨɞɹɬɫɹ ɨɩɢɫɚɧɢɹ ɨɫɬɚɥɶɧɵɯ ɡɚɩɪɨɫɨɜ, ɢɦɟɸtb 1 Ɂɚɞɚɱɧɢɤ ɳɢɯ ɨɞɢɧɚɤɨɜɵɣ ɫɦɵɫɥ ɞɥɹ ɜɫɟɯ ɤɨɦɩɨɧɟɧɬɨɜ. ȼ ɨɬɥɢɱɢɟ ɨɬ pr 2 ɉɪɟɞɨɛɪɚɛɨɬɪɚɧɟɟ ɨɩɢɫɚɧɧɵɯ ɡɚɩɪɨɫɨɜ ɷɬɢ ɡɚɩɪɨɫɵ ɨɩɢɪɚɸɬɫɹ ɧɚ ɫɬɪɭɤɬɭɱɢɤ ɪɭ ɢɫɩɨɥɧɹɸɳɟɝɨ ɤɨɦɩɨɧɟɧɬɚ, ɩɨɷɬɨɦɭ ɤ ɢɦɟɧɢ ɡɚɩɪɨɫɚ ɞɨɛɚɜɥɹɟɬɫɹ ɩɪɟɮɢɤɫ, ɡɚɞɚɸɳɢɣ ɤɨɦɩɨɧɟɧɬɚ. ɋɩɢɫɨɤ ɩɪɟɮɢɤɫɨɜ nn 3 ɋɟɬɶ ɩɪɢɜɟɞɟɧ ɜ ɬɚɛɥ. 16. ȿɞɢɧɫɬɜɟɧɧɵɦ ɢɫɤɥɸɱɟɧɢɟɦ ɢɡ ɱɢɫɥɚ es 4 Ɉɰɟɧɤɚ ɤɨɦɩɨɧɟɧɬɨɜ, ɢɫɩɨɥɧɹɸɳɢɯ ɩɟɪɟɱɢɫɥɟɧɧɵɟ ɜ ɞɚɧɧɨɦ ɪɚɡɞɟɥɟ ai 5 ɂɧɬɟɪɩɪɟɬɚɬɨɪ ɡɚɩɪɨɫɵ, ɹɜɥɹɟɬɫɹ ɤɨɦɩɨɧɟɧɬ ɢɫɩɨɥɧɢɬɟɥɶ. ɨɬɜɟɬɚ ȼɫɟ ɨɩɢɫɵɜɚɟɦɵɟ ɜ ɞɚɧɧɨɦ ɪɚɡɞɟɥɟ ɡɚɩɪɨɫɵ ɦɨɠɧɨ in 6 ɍɱɢɬɟɥɶ ɪɚɡɛɢɬɶ ɧɚ ɱɟɬɵɪɟ ɝɪɭɩɩɵ: cn 7 Ʉɨɧɬɪɚɫɬɟɪ 1. ɍɫɬɚɧɨɜɥɟɧɢɟ ɬɟɤɭɳɟɝɨ ɤɨɦɩɨɧɟɧɬɚ. 2. Ɂɚɩɪɨɫɵ ɪɚɛɨɬɵ ɫɨ ɫɬɪɭɤɬɭɪɨɣ ɤɨɦɩɨɧɟɧɬɚ. 3. Ɂɚɩɪɨɫɵ ɧɚ ɩɨɥɭɱɟɧɢɟ ɢɥɢ ɢɡɦɟɧɟɧɢɟ ɩɚɪɚɦɟɬɪɨɜ ɫɬɪɭɤɬɭɪɧɨɣ ɟɞɢɧɢɰɵ. 4. Ɂɚɩɭɫɤ ɪɟɞɚɤɬɨɪɚ ɤɨɦɩɨɧɟɧɬɚ. ȼɫɟ ɢɦɟɧɚ ɡɚɩɪɨɫɨɜ ɧɚɱɢɧɚɸɬɫɹ ɫ ɫɢɦɜɨɥɨɜ «xx», ɤɨɬɨɪɵɟ ɧɟɨɛɯɨɞɢɦɨ ɡɚɦɟɧɢɬɶ ɧɚ ɩɪɟɮɢɤɫ ɢɡ ɬɚɛɥ. 16 ɱɬɨɛɵ ɩɨɥɭɱɢɬɶ ɢɦɹ ɡɚɩɪɨɫɚ ɞɥɹ ɫɨɨɬɜɟɬɫɬɜɭɸɳɟɝɨ ɤɨɦɩɨɧɟɧɬɚ. ɉɪɢ ɭɤɚɡɚɧɢɢ ɨɲɢɛɨɤ ɢɫɩɨɥɶɡɭɟɬɫɹ ɫɢɦɜɨɥ «n», ɤɨɬɨɪɵɣ ɧɭɠɧɨ ɡɚɦɟɧɢɬɶ ɧɚ ɫɨɨɬɜɟɬɫɬɜɭɸɳɢɣ ɩɪɟɮɢɤɫ ɨɲɢɛɤɢ ɢɡ ɬɚɛɥ. 16. Ⱦɚɥɟɟ ɞɚɧɧɨɦ ɪɚɡɞɟɥɟ ɤɨɦɩɨɧɟɧɬɨɦ ɬɚɤɠɟ ɧɚɡɵɜɚɸɬɫɹ ɷɤɡɟɦɩɥɹɪɵ ɤɨɦɩɨɧɟɧɬɚ, ɚ ɧɟ ɬɨɥɶɤɨ ɱɚɫɬɶ ɩɪɨɝɪɚɦɦɵ. ɇɚɩɪɢɦɟɪ, ɨɞɧɚ ɢɡ ɡɚɝɪɭɠɟɧɧɵɯ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ, ɚ ɧɟ ɬɨɥɶɤɨ ɩɪɨɝɪɚɦɦɧɵɣ ɤɨɦɩɨɧɟɧɬ ɫɟɬɶ.
2.11.1 Ɂɚɩɪɨɫ ɧɚ ɭɫɬɚɧɨɜɥɟɧɢɟ ɬɟɤɭɳɟɝɨ ɤɨɦɩɨɧɟɧɬɚ Ʉ ɷɬɨɣ ɝɪɭɩɩɟ ɡɚɩɪɨɫɨɜ ɨɬɧɨɫɢɬɫɹ ɨɞɢɧ ɡɚɩɪɨɫ – xxSetCurrent – ɧɟ ɢɫɩɨɥɧɹɟɦɵɣ ɤɨɦɩɨɧɟɧɬɨɦ ɡɚɞɚɱɧɢɤ.
2.11.1.1 ɋɞɟɥɚɬɶ ɬɟɤɭɳɟɣ (xxSetCurrent) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function xxSetCurrent( CompName : PString) : Logic; C: Logic xxSetCurrent(PString CompName)
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Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɚ: CompName – ɭɤɚɡɚɬɟɥɶ ɧɚ ɫɬɪɨɤɭ ɫɢɦɜɨɥɨɜ, ɫɨɞɟɪɠɚɳɭɸ ɢɦɹ ɤɨɦɩɨɧɟɧɬɚ, ɤɨɬɨɪɨɝɨ ɧɚɞɨ ɫɞɟɥɚɬɶ ɬɟɤɭɳɢɦ.. ɇɚɡɧɚɱɟɧɢɟ – ɫɬɚɜɢɬ ɭɤɚɡɚɧɧɨɝɨ ɜ ɩɚɪɚɦɟɬɪɟ CompName ɤɨɦɩɨɧɟɧɬɚ ɢɡ ɫɩɢɫɤɚ ɡɚɝɪɭɠɟɧɧɵɯ ɤɨɦɩɨɧɟɧɬɨɜ ɧɚ ɩɟɪɜɨɟ ɦɟɫɬɨ ɜ ɫɩɢɫɤɟ. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. 1. ȿɫɥɢ ɫɩɢɫɨɤ ɤɨɦɩɨɧɟɧɬɨɜ ɩɭɫɬ ɢɥɢ ɢɦɹ ɤɨɦɩɨɧɟɧɬɚ, ɩɟɪɟɞɚɧɧɨɟ ɜ ɚɪɝɭɦɟɧɬɟ CompName, ɜ ɷɬɨɦ ɫɩɢɫɤɟ ɧɟ ɧɚɣɞɟɧɨ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ n01 – ɧɟɜɟɪɧɨɟ ɢɦɹ ɤɨɦɩɨɧɟɧɬɚ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 2. ɍɤɚɡɚɧɧɵɣ ɜ ɚɪɝɭɦɟɧɬɟ CompName ɤɨɦɩɨɧɟɧɬ ɩɟɪɟɧɨɫɢɬɫɹ ɜ ɧɚɱɚɥɨ ɫɩɢɫɤɚ.
2.11.2 Ɂɚɩɪɨɫɵ, ɪɚɛɨɬɚɸɳɢɟ ɫɨ ɫɬɪɭɤɬɭɪɨɣ ɤɨɦɩɨɧɟɧɬɚ. Ʉ ɷɬɨɣ ɝɪɭɩɩɟ ɨɬɧɨɫɹɬɫɹ ɡɚɩɪɨɫɵ, ɩɨɡɜɨɥɹɸɳɢɟ ɜɵɹɫɧɢɬɶ ɫɬɪɭɤɬɭɪɭ ɤɨɦɩɨɧɟɧɬɚ, ɩɪɨɱɢɬɚɬɶ ɟɟ ɢɥɢ ɫɨɯɪɚɧɢɬɶ ɧɚ ɞɢɫɤɟ.
2.11.2.1 Ⱦɨɛɚɜɥɟɧɢɟ ɧɨɜɨɝɨ ɷɤɡɟɦɩɥɹɪɚ (xxAdd) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function xxAdd( CompName : PString ) : Logic; C: Logic xxAdd(PString CompName) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɚ: CompName – ɭɤɚɡɚɬɟɥɶ ɧɚ ɫɬɪɨɤɭ ɫɢɦɜɨɥɨɜ, ɫɨɞɟɪɠɚɳɭɸ ɢɦɹ ɮɚɣɥɚ ɤɨɦɩɨɧɟɧɬɚ ɢɥɢ ɚɞɪɟɫ ɨɩɢɫɚɧɢɹ ɤɨɦɩɨɧɟɧɬɚ. ɇɚɡɧɚɱɟɧɢɟ – ɞɨɛɚɜɥɹɟɬ ɧɨɜɵɣ ɷɤɡɟɦɩɥɹɪ ɤɨɦɩɨɧɟɧɬɚ ɜ ɫɩɢɫɨɤ ɤɨɦɩɨɧɟɧɬɨɜ.
1.
2. 3.
Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. ȿɫɥɢ ɜ ɤɚɱɟɫɬɜɟ ɚɪɝɭɦɟɧɬɚ CompName ɞɚɧɚ ɫɬɪɨɤɚ, ɩɟɪɜɵɟ ɱɟɬɵɪɟ ɫɢɦɜɨɥɚ ɤɨɬɨɪɨɣ ɫɨɫɬɚɜɥɹɸɬ ɫɥɨɜɨ File, ɬɨ ɨɫɬɚɥɶɧɚɹ ɱɚɫɬɶ ɫɬɪɨɤɢ ɫɨɞɟɪɠɢɬ ɢɦɹ ɤɨɦɩɨɧɟɧɬɚ ɢ ɩɨɫɥɟ ɩɪɨɛɟɥɚ ɢɦɹ ɮɚɣɥɚ, ɫɨɞɟɪɠɚɳɟɝɨ ɤɨɦɩɨɧɟɧɬɚ. ȼ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ ɫɱɢɬɚɟɬɫɹ, ɱɬɨ ɚɪɝɭɦɟɧɬ CompName ɫɨɞɟɪɠɢɬ ɭɤɚɡɚɬɟɥɶ ɧɚ ɨɛɥɚɫɬɶ ɩɚɦɹɬɢ, ɫɨɞɟɪɠɚɳɭɸ ɨɩɢɫɚɧɢɟ ɤɨɦɩɨɧɟɧɬɚ ɜ ɮɨɪɦɚɬɟ ɞɥɹ ɡɚɩɢɫɢ ɧɚ ɞɢɫɤ. ȿɫɥɢ ɨɩɢɫɚɧɢɟ ɧɟ ɜɦɟɳɚɟɬɫɹ ɜ ɨɞɧɭ ɨɛɥɚɫɬɶ ɩɚɦɹɬɢ, ɬɨ ɞɨɩɭɫɤɚɟɬɫɹ ɜɤɥɸɱɟɧɢɟ ɜ ɬɟɤɫɬ ɨɩɢɫɚɧɢɹ ɤɨɦɩɨɧɟɧɬɚ ɤɥɸɱɟɜɨɝɨ ɫɥɨɜɚ Continue, ɡɚ ɤɨɬɨɪɵɦ ɫɥɟɞɭɟɬ ɱɟɬɵɪɟ ɛɚɣɬɚ, ɫɨɞɟɪɠɚɳɢɟ ɚɞɪɟɫ ɫɥɟɞɭɸɳɟɣ ɨɛɥɚɫɬɢ ɩɚɦɹɬɢ. ɗɤɡɟɦɩɥɹɪ ɤɨɦɩɨɧɟɧɬɚ ɫɱɢɬɵɜɚɟɬɫɹ ɢɡ ɮɚɣɥɚ ɢɥɢ ɢɡ ɩɚɦɹɬɢ ɢ ɞɨɛɚɜɥɹɟɬɫɹ ɩɟɪɜɵɦ ɜ ɫɩɢɫɨɤ ɤɨɦɩɨɧɟɧɬɨɜ (ɫɬɚɧɨɜɢɬɫɹ ɬɟɤɭɳɢɦ). ȿɫɥɢ ɫɱɢɬɵɜɚɧɢɟ ɡɚɜɟɪɲɚɟɬɫɹ ɩɨ ɨɲɢɛɤɟ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ n02 – ɨɲɢɛɤɚ ɫɱɢɬɵɜɚɧɢɹ ɤɨɦɩɨɧɟɧɬɚ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ.
2.11.2.2 ɍɞɚɥɟɧɢɟ ɷɤɡɟɦɩɥɹɪɚ ɤɨɦɩɨɧɟɧɬɚ (xxDelete) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function xxDelete( CompName : PString) : Logic; C: Logic xxDelete(PString CompName) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɚ: CompName – ɭɤɚɡɚɬɟɥɶ ɧɚ ɫɬɪɨɤɭ ɫɢɦɜɨɥɨɜ, ɫɨɞɟɪɠɚɳɭɸ ɩɨɥɧɨɟ ɢɦɹ ɤɨɦɩɨɧɟɧɬɚ. ɇɚɡɧɚɱɟɧɢɟ – ɭɞɚɥɹɟɬ ɭɤɚɡɚɧɧɨɝɨ ɜ ɩɚɪɚɦɟɬɪɟ CompName ɤɨɦɩɨɧɟɧɬɚ ɢɡ ɫɩɢɫɤɚ ɤɨɦɩɨɧɟɧɬɨɜ. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. 1. ȿɫɥɢ ɫɩɢɫɨɤ ɤɨɦɩɨɧɟɧɬɨɜ ɩɭɫɬ ɢɥɢ ɢɦɹ ɤɨɦɩɨɧɟɧɬɚ, ɩɟɪɟɞɚɧɧɨɟ ɜ ɚɪɝɭɦɟɧɬɟ CompName, ɜ ɷɬɨɦ ɫɩɢɫɤɟ ɧɟ ɧɚɣɞɟɧɨ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ n01 – ɧɟɜɟɪɧɨɟ ɢɦɹ ɤɨɦɩɨɧɟɧɬɚ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. Ɂɚɦɟɬɢɦ, ɱɬɨ ɩɨɩɵɬɤɚ ɭɞɚɥɟɧɢɹ ɦɥɚɞɲɟɣ ɫɬɪɭɤɬɭɪɧɨɣ ɟɞɢɧɢɰɵ ɩɪɢɜɨɞɢɬ ɤ ɭɞɚɥɟɧɢɸ ɜɫɟɝɨ ɤɨɦɩɨɧɟɧɬɚ ɫɨɞɟɪɠɚɳɟɝɨ ɞɚɧɧɭɸ ɫɬɪɭɤɬɭɪɧɭɸ ɟɞɢɧɢɰɭ.
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2.11.2.3 Ɂɚɩɢɫɶ ɤɨɦɩɨɧɟɧɬɚ (xxWrite) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function xxWrite( CompName : PString; FileName : PString) : Logic; C: Logic xxWrite(PString CompName, PString FileName) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: CompName – ɭɤɚɡɚɬɟɥɶ ɧɚ ɫɬɪɨɤɭ ɫɢɦɜɨɥɨɜ, ɫɨɞɟɪɠɚɳɭɸ ɢɦɹ ɤɨɦɩɨɧɟɧɬɚ. FileName – ɢɦɹ ɮɚɣɥɚ ɢɥɢ ɚɞɪɟɫ ɩɚɦɹɬɢ, ɤɭɞɚ ɧɚɞɨ ɡɚɩɢɫɚɬɶ ɤɨɦɩɨɧɟɧɬɚ.
1. 2.
3.
4.
ɇɚɡɧɚɱɟɧɢɟ – ɫɨɯɪɚɧɹɟɬ ɜ ɮɚɣɥɟ ɢɥɢ ɜ ɩɚɦɹɬɢ ɤɨɦɩɨɧɟɧɬɚ, ɭɤɚɡɚɧɧɨɝɨ ɜ ɚɪɝɭɦɟɧɬɟ CompName . Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. ȿɫɥɢ ɜ ɤɚɱɟɫɬɜɟ ɚɪɝɭɦɟɧɬɚ CompName ɞɚɧ ɩɭɫɬɨɣ ɭɤɚɡɚɬɟɥɶ, ɢɥɢ ɭɤɚɡɚɬɟɥɶ ɧɚ ɩɭɫɬɭɸ ɫɬɪɨɤɭ, ɬɨ ɢɫɩɨɥɧɹɸɳɢɦ ɡɚɩɪɨɫ ɨɛɴɟɤɬɨɦ ɹɜɥɹɟɬɫɹ ɬɟɤɭɳɢɣ ɤɨɦɩɨɧɟɧɬ. ȿɫɥɢ ɫɩɢɫɨɤ ɤɨɦɩɨɧɟɧɬɨɜ ɩɭɫɬ ɢɥɢ ɢɦɹ ɤɨɦɩɨɧɟɧɬɚ, ɩɟɪɟɞɚɧɧɨɟ ɜ ɚɪɝɭɦɟɧɬɟ CompName, ɜ ɷɬɨɦ ɫɩɢɫɤɟ ɧɟ ɧɚɣɞɟɧɨ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ n01 – ɧɟɜɟɪɧɨɟ ɢɦɹ ɤɨɦɩɨɧɟɧɬɚ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. ȿɫɥɢ ɜ ɤɚɱɟɫɬɜɟ ɚɪɝɭɦɟɧɬɚ FileName ɞɚɧɚ ɫɬɪɨɤɚ, ɩɟɪɜɵɟ ɱɟɬɵɪɟ ɫɢɦɜɨɥɚ ɤɨɬɨɪɨɣ ɫɨɫɬɚɜɥɹɸɬ ɫɥɨɜɨ File, ɬɨ ɨɫɬɚɥɶɧɚɹ ɱɚɫɬɶ ɫɬɪɨɤɢ ɫɨɞɟɪɠɢɬ ɢɦɹ ɮɚɣɥɚ, ɞɥɹ ɡɚɩɢɫɢ ɤɨɦɩɨɧɟɧɬɚ. ȼ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ FileName ɞɨɥɠɟɧ ɫɨɞɟɪɠɚɬɶ ɩɭɫɬɨɣ ɭɤɚɡɚɬɟɥɶ. ȼ ɷɬɨɦ ɫɥɭɱɚɟ ɡɚɩɪɨɫ ɜɟɪɧɟɬ ɜ ɧɟɦ ɭɤɚɡɚɬɟɥɶ ɧɚ ɨɛɥɚɫɬɶ ɩɚɦɹɬɢ, ɤɭɞɚ ɛɭɞɟɬ ɩɨɦɟɳɟɧɨ ɨɩɢɫɚɧɢɟ ɤɨɦɩɨɧɟɧɬɚ ɜ ɮɨɪɦɚɬɟ ɞɥɹ ɡɚɩɢɫɢ ɧɚ ɞɢɫɤ. ȿɫɥɢ ɨɩɢɫɚɧɢɟ ɧɟ ɜɦɟɳɚɟɬɫɹ ɜ ɨɞɧɭ ɨɛɥɚɫɬɶ ɩɚɦɹɬɢ, ɬɨ ɜ ɬɟɤɫɬ ɛɭɞɟɬ ɜɤɥɸɱɟɧɨ ɤɥɸɱɟɜɨɟ ɫɥɨɜɨ Continue, ɡɚ ɤɨɬɨɪɵɦ ɫɥɟɞɭɟɬ ɱɟɬɵɪɟ ɛɚɣɬɚ, ɫɨɞɟɪɠɚɳɢɟ ɚɞɪɟɫ ɫɥɟɞɭɸɳɟɣ ɨɛɥɚɫɬɢ ɩɚɦɹɬɢ. ȿɫɥɢ ɜɨ ɜɪɟɦɹ ɫɨɯɪɚɧɟɧɢɹ ɤɨɦɩɨɧɟɧɬɚ ɜɨɡɧɢɤɧɟɬ ɨɲɢɛɤɚ, ɬɨ ɝɟɧɟɪɢɪɭɟɬɫɹ ɨɲɢɛɤɚ n03 – ɨɲɢɛɤɚ ɫɨɯɪɚɧɟɧɢɹ ɤɨɦɩɨɧɟɧɬɚ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ.
2.11.2.4 ȼɟɪɧɭɬɶ ɢɦɟɧɚ ɫɬɪɭɤɬɭɪɧɵɯ ɟɞɢɧɢɰ (xxGetStructNames) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function xxGetStructNames(CompName : PString; Var Names : PRealArray) : Logic; C: Logic xxGetStructNames(PString CompName, RealArray* Names) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: CompName – ɭɤɚɡɚɬɟɥɶ ɧɚ ɫɬɪɨɤɭ ɫɢɦɜɨɥɨɜ, ɫɨɞɟɪɠɚɳɭɸ ɢɦɹ ɤɨɦɩɨɧɟɧɬɚ ɢɥɢ ɩɨɥɧɨɟ ɢɦɹ ɟɝɨ ɫɬɪɭɤɬɭɪɧɨɣ ɟɞɢɧɢɰɵ. Names – ɦɚɫɫɢɜ ɭɤɚɡɚɬɟɥɟɣ ɧɚ ɢɦɟɧɚ ɫɬɪɭɤɬɭɪɧɵɯ ɟɞɢɧɢɰ. ɇɚɡɧɚɱɟɧɢɟ – ɜɨɡɜɪɚɳɚɟɬ ɢɦɟɧɚ ɜɫɟɯ ɤɨɦɩɨɧɟɧɬɨɜ ɜ ɫɩɢɫɤɟ ɤɨɦɩɨɧɟɧɬɨɜ ɢɥɢ ɢɦɟɧɚ ɜɫɟɯ ɫɬɪɭɤɬɭɪɧɵɯ ɟɞɢɧɢɰ ɫɬɪɭɤɬɭɪɧɨɣ ɟɞɢɧɢɰɵ, ɭɤɚɡɚɧɧɨɣ ɜ ɚɪɝɭɦɟɧɬɟ CompName . Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. 1. ȿɫɥɢ ɜ ɤɚɱɟɫɬɜɟ ɚɪɝɭɦɟɧɬɚ CompName ɞɚɧ ɩɭɫɬɨɣ ɭɤɚɡɚɬɟɥɶ, ɢɥɢ ɭɤɚɡɚɬɟɥɶ ɧɚ ɩɭɫɬɭɸ ɫɬɪɨɤɭ, ɬɨ ɢɫɩɨɥɧɹɸɳɢɦ ɡɚɩɪɨɫ ɨɛɴɟɤɬɨɦ ɹɜɥɹɟɬɫɹ ɫɨɨɬɜɟɬɫɬɜɭɸɳɢɣ ɩɪɨɝɪɚɦɦɧɵɣ ɤɨɦɩɨɧɟɧɬ. ȼ ɤɚɱɟɫɬɜɟ ɨɬɜɟɬɚ ɜ ɭɤɚɡɚɬɟɥɟ Names ɜɨɡɜɪɚɳɚɟɬɫɹ ɦɚɫɫɢɜ, ɤɚɠɞɵɣ ɷɥɟɦɟɧɬ ɤɨɬɨɪɨɝɨ ɹɜɥɹɟɬɫɹ ɭɤɚɡɚɬɟɥɟɦ ɧɚ ɧɟ ɩɨɞɥɟɠɚɳɭɸ ɢɡɦɟɧɟɧɢɸ ɫɢɦɜɨɥɶɧɭɸ ɫɬɪɨɤɭ, ɫɨɞɟɪɠɚɳɭɸ ɢɦɹ ɤɨɦɩɨɧɟɧɬɚ ɢɡ ɫɩɢɫɤɚ. ɉɨɫɥɟ ɚɞɪɟɫɚ ɢɦɟɧɢ ɩɨɫɥɟɞɧɟɝɨ ɤɨɦɩɨɧɟɧɬɚ ɫɥɟɞɭɟɬ ɩɭɫɬɨɣ ɭɤɚɡɚɬɟɥɶ. ȼɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɭɫɩɟɲɧɨ ɡɚɜɟɪɲɚɟɬɫɹ. 2. ȿɫɥɢ ɢɦɹ ɤɨɦɩɨɧɟɧɬɚ, ɩɟɪɟɞɚɧɧɨɟ ɜ ɚɪɝɭɦɟɧɬɟ CompName, ɧɟ ɧɚɣɞɟɧɨ ɜ ɫɩɢɫɤɟ ɤɨɦɩɨɧɟɧɬɨɜ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ n01 – ɧɟɜɟɪɧɨɟ ɢɦɹ ɤɨɦɩɨɧɟɧɬɚ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 3. ȼɨɡɜɪɚɳɚɟɬɫɹ ɦɚɫɫɢɜ, ɤɚɠɞɵɣ ɷɥɟɦɟɧɬ ɤɨɬɨɪɨɝɨ ɹɜɥɹɟɬɫɹ ɭɤɚɡɚɬɟɥɟɦ ɧɚ ɧɟ ɩɨɞɥɟɠɚɳɭɸ ɢɡɦɟɧɟɧɢɸ ɫɢɦɜɨɥɶɧɭɸ ɫɬɪɨɤɭ, ɫɨɞɟɪɠɚɳɭɸ ɩɫɟɜɞɨɧɢɦ ɫɬɪɭɤɬɭɪɧɨɣ ɟɞɢɧɢɰɵ, ɹɜɥɹɸɳɟɣɫɹ ɱɚɫɬɶɸ ɫɬɪɭɤɬɭɪɧɨɣ ɟɞɢɧɢɰɵ, ɭɤɚɡɚɧɧɨɣ ɜ ɚɪɝɭɦɟɧɬɟ CompName. ɂɦɟɧɚ ɫɬɪɭɤɬɭɪɧɵɯ ɟɞɢɧɢɰ ɩɟɪɟɱɢɫɥɹɸɬɫɹ ɜ ɩɨɪɹɞɤɟ ɫɥɟɞɨɜɚɧɢɹ ɜ ɪɚɡɞɟɥɟ ɨɩɢɫɚɧɢɹ ɫɨɫɬɚɜɚ ɫɬɪɭɤɬɭɪɧɨɣ ɟɞɢɧɢɰɵ, ɢɦɹ ɤɨɬɨɪɨɣ ɭɤɚɡɚɧɨ ɜ ɚɪɝɭɦɟɧɬɟ CompName. ȿɫɥɢ ɨɞɧɚ ɢɡ ɫɬɪɭɤɬɭɪɧɵɯ ɟɞɢɧɢɰ ɡɚɞɚɧɚ ɜ ɨɩɢɫɚɧɢɢ ɫɨɫɬɚɜɚ ɧɟɫɤɨɥɶɤɢɦɢ ɷɤɡɟɦɩɥɹɪɚɦɢ, ɬɨ ɢɦɹ ɤɚɠɞɨɝɨ ɷɤɡɟɦɩɥɹɪɚ ɜɨɡɜɪɚɳɚɟɬɫɹ ɨɬɞɟɥɶɧɨ. ɉɨɫɥɟ ɭɤɚɡɚɬɟɥɹ ɧɚ ɢɦɹ ɩɨɫɥɟɞɧɟɣ ɫɬɪɭɤɬɭɪɧɨɣ ɟɞɢɧɢɰɵ ɫɥɟɞɭɟɬ ɩɭɫɬɨɣ ɭɤɚɡɚɬɟɥɶ.
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2.11.2.5 ȼɟɪɧɭɬɶ ɬɢɩ ɫɬɪɭɤɬɭɪɧɨɣ ɟɞɢɧɢɰɵ (xxGetType) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function xxGetType(CompName , TypeName : PString; Var TypeId : Integer) : Logic; C: Logic xxGetType(PString CompName, PString TypeName, Integer TypeId) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: CompName – ɭɤɚɡɚɬɟɥɶ ɧɚ ɫɬɪɨɤɭ ɫɢɦɜɨɥɨɜ, ɫɨɞɟɪɠɚɳɭɸ ɩɨɥɧɨɟ ɢɦɹ ɫɬɪɭɤɬɭɪɧɨɣ ɟɞɢɧɢɰɵ. TypeName – ɜɨɡɜɪɚɳɚɟɬ ɭɤɚɡɚɬɟɥɶ ɧɚ ɫɬɪɨɤɭ ɫɢɦɜɨɥɨɜ, ɫɨɞɟɪɠɚɳɭɸ ɢɦɹ ɫɬɪɭɤɬɭɪɧɨɣ ɟɞɢɧɢɰɵ, ɞɚɧɧɨɟ ɟɣ ɩɪɢ ɨɩɢɫɚɧɢɢ. TypeId – ɨɞɧɚ ɢɡ ɩɪɟɞɨɩɪɟɞɟɥɟɧɧɵɯ ɤɨɧɫɬɚɧɬ, ɫɨɨɬɜɟɬɫɬɜɭɸɳɚɹ ɬɢɩɭ ɫɬɪɭɤɬɭɪɧɨɣ ɟɞɢɧɢɰɵ.
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ɇɚɡɧɚɱɟɧɢɟ – ɜɨɡɜɪɚɳɚɟɬ ɢɦɹ ɢ ɬɢɩ ɫɬɪɭɤɬɭɪɧɨɣ ɟɞɢɧɢɰɵ. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. ȿɫɥɢ ɫɩɢɫɨɤ ɤɨɦɩɨɧɟɧɬɨɜ ɩɭɫɬ ɢɥɢ ɢɦɹ ɤɨɦɩɨɧɟɧɬɚ, ɩɟɪɟɞɚɧɧɨɟ ɜ ɚɪɝɭɦɟɧɬɟ CompName, ɜ ɷɬɨɦ ɫɩɢɫɤɟ ɧɟ ɧɚɣɞɟɧɨ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ n01 – ɧɟɜɟɪɧɨɟ ɢɦɹ ɤɨɦɩɨɧɟɧɬɚ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. ȼ ɩɟɪɟɦɟɧɧɨɣ TypeId ɜɨɡɜɪɚɳɚɟɬɫɹ ɬɢɩ ɫɬɪɭɤɬɭɪɧɨɣ ɟɞɢɧɢɰɵ. Ɂɧɚɱɟɧɢɹ ɩɪɟɞɨɩɪɟɞɟɥɟɧɧɵɯ ɤɨɧɫɬɚɧɬ, ɫɨɨɬɜɟɬɫɬɜɭɸɳɢɯ ɪɚɡɥɢɱɧɵɦ ɬɢɩɚɦ ɫɬɪɭɤɬɭɪɧɵɯ ɟɞɢɧɢɰ ɪɚɡɥɢɱɧɵɯ ɤɨɦɩɨɧɟɧɬɨɜ ɩɪɢɜɟɞɟɧɵ ɜ ɬɚɛɥ. 11 ɢ ɜ ɫɨɨɬɜɟɬɫɬɜɭɸɳɢɯ ɪɚɡɞɟɥɚɯ ɝɥɚɜ, ɫɨɞɟɪɠɚɳɢɯ ɨɩɢɫɚɧɢɹ ɤɨɦɩɨɧɟɧɬɨɜ. ȿɫɥɢ ɫɬɪɭɤɬɭɪɧɚɹ ɟɞɢɧɢɰɚ ɹɜɥɹɟɬɫɹ ɫɬɚɧɞɚɪɬɧɨɣ, ɬɨ ɭɤɚɡɚɬɟɥɸ TypeName ɩɪɢɫɜɚɢɜɚɟɬɫɹ ɡɧɚɱɟɧɢɟ ɩɭɫɬɨɝɨ ɭɤɚɡɚɬɟɥɹ. ȿɫɥɢ ɫɬɪɭɤɬɭɪɧɚɹ ɟɞɢɧɢɰɚ ɢɦɟɟɬ ɩɨɥɶɡɨɜɚɬɟɥɶɫɤɢɣ ɬɢɩ (ɡɧɚɱɟɧɢɟ ɚɪɝɭɦɟɧɬɚ TypeId ɪɚɜɧɨ -1), ɬɨ ɭɤɚɡɚɬɟɥɶ TypeName ɭɫɬɚɧɚɜɥɢɜɚɟɬɫɹ ɧɚ ɫɬɪɨɤɭ, ɫɨɞɟɪɠɚɳɭɸ ɢɦɹ, ɞɚɧɧɨɟ ɭɤɚɡɚɧɧɨɣ ɜ ɚɪɝɭɦɟɧɬɟ CompName ɫɬɪɭɤɬɭɪɧɨɣ ɟɞɢɧɢɰɟ ɩɪɢ ɟɟ ɨɩɢɫɚɧɢɢ.
2.11.3 Ɂɚɩɪɨɫɵ ɧɚ ɢɡɦɟɧɟɧɢɟ ɩɚɪɚɦɟɬɪɨɜ. Ʉ ɝɪɭɩɩɟ ɡɚɩɪɨɫɨɜ ɧɚ ɢɡɦɟɧɟɧɢɟ ɩɚɪɚɦɟɬɪɨɜ ɨɬɧɨɫɹɬɫɹ ɬɪɢ ɡɚɩɪɨɫɚ: xxGetData – ɩɨɥɭɱɢɬɶ ɩɚɪɚɦɟɬɪɵ ɫɬɪɭɤɬɭɪɧɨɣ ɟɞɢɧɢɰɵ. xxGetName – ɩɨɥɭɱɢɬɶ ɧɚɡɜɚɧɢɹ ɩɚɪɚɦɟɬɪɨɜ ɢ xxSetData – ɭɫɬɚɧɨɜɢɬɶ ɡɧɚɱɟɧɢɹ ɩɚɪɚɦɟɬɪɨɜ ɫɬɪɭɤɬɭɪɧɨɣ ɟɞɢɧɢɰɵ.
2.11.3.1 ɉɨɥɭɱɢɬɶ ɩɚɪɚɦɟɬɪɵ (xxGetData) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function xxGetData( CompName : PString; Var Param : PRealArray ) : Logic; C: Logic xxGetData(PString CompName, PRealArray* Param) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: CompName – ɭɤɚɡɚɬɟɥɶ ɧɚ ɫɬɪɨɤɭ ɫɢɦɜɨɥɨɜ, ɫɨɞɟɪɠɚɳɭɸ ɩɨɥɧɨɟ ɢɦɹ ɫɬɪɭɤɬɭɪɧɨɣ ɟɞɢɧɢɰɵ. Param – ɚɞɪɟɫ ɦɚɫɫɢɜɚ ɩɚɪɚɦɟɬɪɨɜ. ɇɚɡɧɚɱɟɧɢɟ – ɜɨɡɜɪɚɳɚɟɬ ɦɚɫɫɢɜ ɩɚɪɚɦɟɬɪɨɜ ɫɬɪɭɤɬɭɪɧɨɣ ɟɞɢɧɢɰɵ, ɭɤɚɡɚɧɧɨɣ ɜ ɚɪɝɭɦɟɧɬɟ CompName . Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. 1. ȿɫɥɢ Error <> 0, ɬɨ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 2. ȿɫɥɢ ɫɩɢɫɨɤ ɤɨɦɩɨɧɟɧɬɨɜ ɩɭɫɬ ɢɥɢ ɢɦɹ ɤɨɦɩɨɧɟɧɬɚ, ɩɟɪɟɞɚɧɧɨɟ ɜ ɚɪɝɭɦɟɧɬɟ CompName, ɜ ɷɬɨɦ ɫɩɢɫɤɟ ɧɟ ɧɚɣɞɟɧɨ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ n01 – ɧɟɜɟɪɧɨɟ ɢɦɹ ɤɨɦɩɨɧɟɧɬɚ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 3. ȼ ɦɚɫɫɢɜ, ɚɞɪɟɫ ɤɨɬɨɪɨɝɨ ɩɟɪɟɞɚɧ ɜ ɚɪɝɭɦɟɧɬɟ Param, ɡɚɧɨɫɹɬɫɹ ɡɧɚɱɟɧɢɹ ɩɚɪɚɦɟɬɪɨɜ. ɉɚɪɚɦɟɬɪɵ ɡɚɧɨɫɹɬɫɹ ɜ ɦɚɫɫɢɜ ɜ ɩɨɪɹɞɤɟ ɨɩɢɫɚɧɢɹ ɜ ɪɚɡɞɟɥɟ ɨɩɢɫɚɧɢɹ ɫɬɚɬɢɱɟɫɤɢɯ ɩɟɪɟɦɟɧɧɵɯ. ɋɬɚɬɢɱɟɫɤɢɟ ɩɟɪɟɦɟɧɧɵɟ, ɨɩɢɫɚɧɧɵɟ ɜɧɟ ɨɩɢɫɚɧɢɹ ɫɬɪɭɤɬɭɪɧɵɯ ɟɞɢɧɢɰ, ɫɱɢɬɚɸɬɫɹ ɩɚɪɚɦɟɬɪɚɦɢ ɤɨɦɩɨɧɟɧɬɚ.
2.11.3.2 ɉɨɥɭɱɢɬɶ ɢɦɟɧɚ ɩɚɪɚɦɟɬɪɨɜ (xxGetName) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function xxGetName( CompName : PString; Var Param : PRealArray ) : Logic; C: Logic xxGetName(PString CompName, PRealArray* Param)
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Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: CompName – ɭɤɚɡɚɬɟɥɶ ɧɚ ɫɬɪɨɤɭ ɫɢɦɜɨɥɨɜ, ɫɨɞɟɪɠɚɳɭɸ ɩɨɥɧɨɟ ɢɦɹ ɫɬɪɭɤɬɭɪɧɨɣ ɟɞɢɧɢɰɵ. Param – ɚɞɪɟɫ ɦɚɫɫɢɜɚ ɭɤɚɡɚɬɟɥɟɣ ɧɚ ɧɚɡɜɚɧɢɹ ɩɚɪɚɦɟɬɪɨɜ. ɇɚɡɧɚɱɟɧɢɟ – ɜɨɡɜɪɚɳɚɟɬ ɦɚɫɫɢɜ ɭɤɚɡɚɬɟɥɟɣ ɧɚ ɧɚɡɜɚɧɢɹ ɩɚɪɚɦɟɬɪɨɜ ɫɬɪɭɤɬɭɪɧɨɣ ɟɞɢɧɢɰɵ, ɭɤɚɡɚɧɧɨɣ ɜ ɚɪɝɭɦɟɧɬɟ CompName . Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. 1. ȿɫɥɢ Error <> 0, ɬɨ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 2. ȿɫɥɢ ɫɩɢɫɨɤ ɤɨɦɩɨɧɟɧɬɨɜ ɩɭɫɬ ɢɥɢ ɢɦɹ ɤɨɦɩɨɧɟɧɬɚ, ɩɟɪɟɞɚɧɧɨɟ ɜ ɚɪɝɭɦɟɧɬɟ CompName, ɜ ɷɬɨɦ ɫɩɢɫɤɟ ɧɟ ɧɚɣɞɟɧɨ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ n01 – ɧɟɜɟɪɧɨɟ ɢɦɹ ɤɨɦɩɨɧɟɧɬɚ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 3. ȼ ɦɚɫɫɢɜ, ɚɞɪɟɫ ɤɨɬɨɪɨɝɨ ɩɟɪɟɞɚɧ ɜ ɚɪɝɭɦɟɧɬɟ Param, ɡɚɧɨɫɹɬɫɹ ɚɞɪɟɫɚ ɫɢɦɜɨɥɶɧɵɯ ɫɬɪɨɤ, ɫɨɞɟɪɠɚɳɢɯ ɧɚɡɜɚɧɢɹ ɩɚɪɚɦɟɬɪɨɜ.
2.11.3.3 ɍɫɬɚɧɨɜɢɬɶ ɩɚɪɚɦɟɬɪɵ (xxSetData) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function xxSetData( CompName : PString; Param : PRealArray ) : Logic; C: Logic xxSetData(PString CompName, PRealArray Param) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: CompName – ɭɤɚɡɚɬɟɥɶ ɧɚ ɫɬɪɨɤɭ ɫɢɦɜɨɥɨɜ, ɫɨɞɟɪɠɚɳɭɸ ɩɨɥɧɨɟ ɢɦɹ ɫɬɪɭɤɬɭɪɧɨɣ ɟɞɢɧɢɰɵ. Param – ɚɞɪɟɫ ɦɚɫɫɢɜɚ ɩɚɪɚɦɟɬɪɨɜ. ɇɚɡɧɚɱɟɧɢɟ – ɡɚɦɟɧɹɟɬ ɡɧɚɱɟɧɢɹ ɩɚɪɚɦɟɬɪɨɜ ɫɬɪɭɤɬɭɪɧɨɣ ɟɞɢɧɢɰɵ, ɭɤɚɡɚɧɧɨɣ ɜ ɚɪɝɭɦɟɧɬɟ CompName , ɧɚ ɡɧɚɱɟɧɢɹ, ɩɟɪɟɞɚɧɧɵɟ, ɜ ɚɪɝɭɦɟɧɬɟ Param. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. 1. ȿɫɥɢ Error <> 0, ɬɨ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 2. ȿɫɥɢ ɫɩɢɫɨɤ ɤɨɦɩɨɧɟɧɬɨɜ ɩɭɫɬ ɢɥɢ ɢɦɹ ɤɨɦɩɨɧɟɧɬɚ, ɩɟɪɟɞɚɧɧɨɟ ɜ ɚɪɝɭɦɟɧɬɟ CompName, ɜ ɷɬɨɦ ɫɩɢɫɤɟ ɧɟ ɧɚɣɞɟɧɨ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ n01 – ɧɟɜɟɪɧɨɟ ɢɦɹ ɤɨɦɩɨɧɟɧɬɚ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 3. ɉɚɪɚɦɟɬɪɵ, ɡɧɚɱɟɧɢɹ ɤɨɬɨɪɵɯ ɯɪɚɧɹɬɫɹ ɜ ɦɚɫɫɢɜɟ, ɚɞɪɟɫ ɤɨɬɨɪɨɝɨ ɩɟɪɟɞɚɧ ɜ ɚɪɝɭɦɟɧɬɟ Param, ɩɟɪɟɞɚɸɬɫɹ ɭɤɚɡɚɧɧɨɣ ɜ ɚɪɝɭɦɟɧɬɟ CompName ɫɬɪɭɤɬɭɪɧɨɣ ɟɞɢɧɢɰɟ. 4. ȿɫɥɢ ɢɫɩɨɥɧɹɸɳɢɦ ɡɚɩɪɨɫ ɤɨɦɩɨɧɟɧɬɨɦ ɹɜɥɹɟɬɫɹ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɨɬɜɟɬɚ (aiSetData), ɬɨ ɝɟɧɟɪɢɪɭɟɬɫɹ ɡɚɩɪɨɫ SetEstIntParameters ɤ ɤɨɦɩɨɧɟɧɬɭ ɨɰɟɧɤɚ. Ⱥɪɝɭɦɟɧɬɵ ɝɟɧɟɪɢɪɭɟɦɨɝɨ ɡɚɩɪɨɫɚ ɫɨɜɩɚɞɚɸɬ ɫ ɚɪɝɭɦɟɧɬɚɦɢ ɢɫɩɨɥɧɹɟɦɨɝɨ ɡɚɩɪɨɫɚ.
2.11.4 ɂɧɢɰɢɚɰɢɹ ɪɟɞɚɤɬɨɪɚ ɤɨɦɩɨɧɟɧɬɵ. Ʉ ɷɬɨɣ ɝɪɭɩɩɟ ɡɚɩɪɨɫɨɜ ɨɬɧɨɫɢɬɫɹ ɡɚɩɪɨɫ, ɤɨɬɨɪɵɣ ɢɧɢɰɢɢɪɭɟɬ ɪɚɛɨɬɭ ɧɟ ɪɚɫɫɦɚɬɪɢɜɚɟɦɵɯ ɜ ɞɚɧɧɨɣ ɪɚɛɨɬɟ ɤɨɦɩɨɧɟɧɬɨɜ – ɪɟɞɚɤɬɨɪɨɜ ɤɨɦɩɨɧɟɧɬɨɜ.
2.11.4.1 Ɋɟɞɚɤɬɢɪɨɜɚɬɶ ɤɨɦɩɨɧɟɧɬɚ (xxEdit) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Procedure xxEdit(CompName : PString); C: void xxEdit(PString CompName ) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɚ: CompName – ɭɤɚɡɚɬɟɥɶ ɧɚ ɫɬɪɨɤɭ ɫɢɦɜɨɥɨɜ – ɢɦɹ ɮɚɣɥɚ ɢɥɢ ɚɞɪɟɫ ɩɚɦɹɬɢ, ɫɨɞɟɪɠɚɳɢɟ ɨɩɢɫɚɧɢɟ ɪɟɞɚɤɬɢɪɭɟɦɨɝɨ ɤɨɦɩɨɧɟɧɬɚ. ȿɫɥɢ ɜ ɤɚɱɟɫɬɜɟ ɚɪɝɭɦɟɧɬɚ CompName ɞɚɧɚ ɫɬɪɨɤɚ, ɩɟɪɜɵɟ ɱɟɬɵɪɟ ɫɢɦɜɨɥɚ ɤɨɬɨɪɨɣ ɫɨɫɬɚɜɥɹɸɬ ɫɥɨɜɨ File, ɬɨ ɨɫɬɚɥɶɧɚɹ ɱɚɫɬɶ ɫɬɪɨɤɢ ɫɨɞɟɪɠɢɬ ɢɦɹ ɤɨɦɩɨɧɟɧɬɚ ɢ ɩɨɫɥɟ ɩɪɨɛɟɥɚ ɢɦɹ ɮɚɣɥɚ, ɫɨɞɟɪɠɚɳɟɝɨ ɨɩɢɫɚɧɢɟ ɤɨɦɩɨɧɟɧɬɚ. ȼ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ ɫɱɢɬɚɟɬɫɹ, ɱɬɨ ɚɪɝɭɦɟɧɬ CompName ɫɨɞɟɪɠɢɬ ɭɤɚɡɚɬɟɥɶ ɧɚ ɨɛɥɚɫɬɶ ɩɚɦɹɬɢ, ɫɨɞɟɪɠɚɳɭɸ ɨɩɢɫɚɧɢɟ ɤɨɦɩɨɧɟɧɬɚ ɜ ɮɨɪɦɚɬɟ ɞɥɹ ɡɚɩɢɫɢ ɧɚ ɞɢɫɤ. ȿɫɥɢ ɨɩɢɫɚɧɢɟ ɧɟ ɜɦɟɳɚɟɬɫɹ ɜ ɨɞɧɭ ɨɛɥɚɫɬɶ ɩɚɦɹɬɢ, ɬɨ ɞɨɩɭɫɤɚɟɬɫɹ ɜɤɥɸɱɟɧɢɟ ɜ ɬɟɤɫɬ ɨɩɢɫɚɧɢɹ ɤɨɦɩɨɧɟɧɬɚ ɤɥɸɱɟɜɨɝɨ ɫɥɨɜɚ Continue, ɡɚ ɤɨɬɨɪɵɦ ɫɥɟɞɭɟɬ ɱɟɬɵɪɟ ɛɚɣɬɚ, ɫɨɞɟɪɠɚɳɢɟ ɚɞɪɟɫ ɫɥɟɞɭɸɳɟɣ ɨɛɥɚɫɬɢ ɩɚɦɹɬɢ. ȿɫɥɢ ɜ ɤɚɱɟɫɬɜɟ ɚɪɝɭɦɟɧɬɚ CompName ɩɟɪɟɞɚɧ ɩɭɫɬɨɣ ɭɤɚɡɚɬɟɥɶ ɢɥɢ ɭɤɚɡɚɬɟɥɶ ɧɚ ɩɭɫɬɭɸ ɫɬɪɨɤɭ, ɬɨ ɪɟɞɚɤɬɨɪ ɫɨɡɞɚɟɬ ɧɨɜɵɣ ɷɤɡɟɦɩɥɹɪ ɤɨɦɩɨɧɟɧɬɚ.
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2.12 Ɉɩɢɫɚɧɢɟ ɡɚɞɚɱɢ, ɢɫɩɨɥɶɡɭɟɦɨɣ ɞɥɹ ɩɪɢɦɟɪɚ ȼ ɝɥɚɜɚɯ, ɩɨɫɜɹɳɟɧɧɵɯ ɨɩɢɫɚɧɢɸ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ, ɡɚɞɚɱɧɢɤɚ, ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ ɨɬɜɟɬɚ ɢ ɨɰɟɧɤɢ ɜ ɤɚɱɟɫɬɜɟ ɩɪɢɦɟɪɚ ɢɫɩɨɥɶɡɭɟɬɫɹ ɦɟɬɟɨɪɨɥɨɝɢɱɟɫɤɚɹ ɡɚɞɚɱɚ. ȼɯɨɞɧɚɹ ɛɚɡɚ ɞɚɧɧɵɯ ɫɨɞɟɪɠɢɬ ɡɧɚɱɟɧɢɹ ɫɥɟɞɭɸɳɢɯ ɩɨɤɚɡɚɬɟɥɟɣ: Ɍɟɦɩɟɪɚɬɭɪɚ ɜɨɡɞɭɯɚ – ɞɟɣɫɬɜɢɬɟɥɶɧɨɟ ɱɢɫɥɨ, ɢɡɦɟɧɹɸɳɟɟɫɹ ɨɬ 273 ɞɨ 393 ɝɪɚɞɭɫɨɜ Ʉɟɥɶɜɢɧɚ. Ɉɛɥɚɱɧɨɫɬɶ – ɛɢɧɚɪɧɵɣ ɩɪɢɡɧɚɤ, ɨɡɧɚɱɚɸɳɢɣ ɧɚɥɢɱɢɟ (2) ɢɥɢ ɨɬɫɭɬɫɬɜɢɟ ɨɛɥɚɱɧɨɫɬɢ (1). ɇɚɩɪɚɜɥɟɧɢɟ ɜɟɬɪɚ – ɧɟɭɩɨɪɹɞɨɱɟɧɧɵɣ ɤɚɱɟɫɬɜɟɧɧɵɣ ɩɪɢɡɧɚɤ, ɩɪɢɧɢɦɚɸɳɢɣ ɨɞɧɨ ɢɡ ɜɨɫɶɦɢ ɡɧɚɱɟɧɢɣ: 1 – ɫɟɜɟɪɧɵɣ, 2 – ɫɟɜɟɪɨ-ɜɨɫɬɨɱɧɵɣ, 3 – ɜɨɫɬɨɱɧɵɣ, ɢ ɬ.ɞ. Ɉɫɚɞɤɢ – ɭɩɨɪɹɞɨɱɟɧɧɵɣ ɤɚɱɟɫɬɜɟɧɧɵɣ ɩɪɢɡɧɚɤ, ɩɪɢɧɢɦɚɸɳɢɣ ɫɥɟɞɭɸɳɢɟ ɡɧɚɱɟɧɢɹ: 1 – ɛɟɡ ɨɫɚɞɤɨɜ, 2 – ɫɥɚɛɵɟ ɨɫɚɞɤɢ, 3 – ɫɢɥɶɧɵɟ ɨɫɚɞɤɢ. ȼ ɤɚɱɟɫɬɜɟ ɨɬɜɟɬɨɜ ɬɪɟɛɭɟɬɫɹ ɩɪɟɞɫɤɚɡɚɬɶ ɡɧɚɱɟɧɢɹ ɬɟɯ ɠɟ ɩɨɤɚɡɚɬɟɥɟɣ ɱɟɪɟɡ 8 ɱɚɫɨɜ. Ɍɚɤɚɹ ɩɨɫɬɚɧɨɜɤɚ ɡɚɞɚɱɢ ɧɟ ɨɱɟɧɶ ɥɨɝɢɱɧɚ, ɧɨ ɩɨɡɜɨɥɹɟɬ ɩɪɨɞɟɦɨɧɫɬɪɢɪɨɜɚɬɶ ɜɨɡɦɨɠɧɨɫɬɢ ɹɡɵɤɨɜ ɨɩɢɫɚɧɢɹ ɤɨɦɩɨɧɟɧɬɨɜ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚ.
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3. Ɂɚɞɚɱɧɢɤ ɢ ɨɛɭɱɚɸɳɟɟ ɦɧɨɠɟɫɬɜɨ ɗɬɚ ɝɥɚɜɚ ɩɨɫɜɹɳɟɧɚ ɨɞɧɨɦɭ ɢɡ ɧɚɢɛɨɥɟɟ ɜɚɠɧɵɯ ɢ ɨɛɞɟɥɟɧɧɵɯ ɜɧɢɦɚɧɢɟɦ ɤɨɦɩɨɧɟɧɬɨɜ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚ – ɡɚɞɚɱɧɢɤɭ. ȼɚɠɧɨɫɬɶ ɷɬɨɝɨ ɤɨɦɩɨɧɟɧɬɚ ɨɩɪɟɞɟɥɹɟɬɫɹ ɬɟɦ, ɱɬɨ ɩɪɢ ɨɛɭɱɟɧɢɢ ɫɟɬɟɣ ɜɫɟɯ ɜɢɞɨɜ ɫ ɢɫɩɨɥɶɡɨɜɚɧɢɟɦ ɥɸɛɵɯ ɚɥɝɨɪɢɬɦɨɜ ɨɛɭɱɟɧɢɹ ɫɟɬɢ ɧɟɨɛɯɨɞɢɦɨ ɩɪɟɞɴɹɜɥɹɬɶ ɩɪɢɦɟɪɵ, ɧɚ ɤɨɬɨɪɵɯ ɨɧɚ ɨɛɭɱɚɟɬɫɹ ɪɟɲɟɧɢɸ ɡɚɞɚɱɢ. ɂɫɬɨɱɧɢɤɨɦ ɞɚɧɧɵɯ ɞɥɹ ɫɟɬɢ ɹɜɥɹɟɬɫɹ ɡɚɞɚɱɧɢɤ. Ʉɪɨɦɟ ɬɨɝɨ, ɡɚɞɚɱɧɢɤ ɫɨɞɟɪɠɢɬ ɩɪɚɜɢɥɶɧɵɟ ɨɬɜɟɬɵ ɞɥɹ ɫɟɬɟɣ, ɨɛɭɱɚɟɦɵɯ ɫ ɭɱɢɬɟɥɟɦ.
3.1 Ɉɛɫɭɠɞɟɧɢɟ Ɂɚɞɚɱɧɢɤɚ ȼ ɷɬɨɦ ɪɚɡɞɟɥɟ ɪɚɫɫɦɚɬɪɢɜɚɸɬɫɹ ɨɫɧɨɜɧɵɟ ɫɬɪɭɤɬɭɪɵ ɢ ɮɭɧɤɰɢɢ ɤɨɦɩɨɧɟɧɬɚ ɡɚɞɚɱɧɢɤ. Ɉɬɦɟɬɢɦ, ɱɬɨ ɡɚɞɚɱɧɢɤ ɪɚɫɫɦɚɬɪɢɜɚɟɬɫɹ ɬɨɥɶɤɨ ɫ ɬɨɱɤɢ ɡɪɟɧɢɹ ɟɝɨ ɢɫɩɨɥɶɡɨɜɚɧɢɹ ɧɟɣɪɨɧɧɨɣ ɫɟɬɶɸ. ɋɨɜɟɪɲɟɧɧɨ ɨɱɟɜɢɞɧɨ, ɱɬɨ ɧɟɜɨɡɦɨɠɧɨ ɩɪɟɞɭɫɦɨɬɪɟɬɶ ɜɫɟɯ ɜɚɪɢɚɧɬɨɜ ɢɧɬɟɪɮɟɣɫɚ ɦɟɠɞɭ ɩɨɥɶɡɨɜɚɬɟɥɟɦ ɢ ɡɚɞɚɱɧɢɤɨɦ. Ⱦɟɣɫɬɜɢɬɟɥɶɧɨ, ɛɵɥɨ ɛɵ ɫɬɪɚɧɧɨ, ɟɫɥɢ ɛɵ ɜ ɨɞɧɨɦ ɢ ɬɨɦ ɠɟ ɢɧɬɟɪɮɟɣɫɟ ɨɛɪɚɛɚɬɵɜɚɥɢɫɶ ɡɚɞɚɱɧɢɤɢ, ɫɨɞɟɪɠɚɳɢɟ ɬɨɥɶɤɨ ɱɢɫɥɨɜɵɟ ɩɨɥɹ, ɡɚɞɚɱɧɢɤɢ, ɫɨɞɟɪɠɚɳɢɟ ɢɫɤɥɸɱɢɬɟɥɶɧɨ ɝɪɚɮɢɱɟɫɤɭɸ ɢɧɮɨɪɦɚɰɢɸ ɢ ɡɚɞɚɱɧɢɤɢ ɫɦɟɲɚɧɧɨɝɨ ɬɢɩɚ.
3.1.1 ɋɬɪɭɤɬɭɪɵ ɞɚɧɧɵɯ ɡɚɞɚɱɧɢɤɚ ɋ ɬɨɱɤɢ ɡɪɟɧɢɹ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚ ɡɚɞɚɱɧɢɤ ɩɪɟɞɫɬɚɜɥɹɟɬ ɫɨɛɨɣ ɩɪɹɦɨɭɝɨɥɶɧɭɸ ɬɚɛɥɢɰɭ, ɩɨɥɹ ɤɨɬɨɪɨɣ ɫɨɞɟɪɠɚɬ ɢɧɮɨɪɦɚɰɢɸ ɨ ɜɯɨɞɧɵɯ ɞɚɧɧɵɯ ɩɪɢɦɟɪɨɜ ɡɚɞɚɱɢ, ɩɪɚɜɢɥɶɧɵɟ ɨɬɜɟɬɵ ɢ ɞɪɭɝɭɸ ɢɧɮɨɪɦɚɰɢɸ. ɇɚ ɞɚɧɧɵɣ ɦɨɦɟɧɬ ɫɭɳɟɫɬɜɭɟɬ ɬɪɢ ɨɫɧɨɜɧɵɯ ɫɩɨɫɨɛɚ ɯɪɚɧɟɧɢɹ ɨɞɧɨɬɢɩɧɵɯ ɞɚɧɧɵɯ – ɛɚɡɵ ɞɚɧɧɵɯ, ɷɥɟɤɬɪɨɧɧɵɟ ɬɚɛɥɢɰɵ, ɬɟɤɫɬɨɜɵɟ ɮɚɣɥɵ. Ʉɚɤɨɣ ɢɡ ɧɢɯ ɹɜɥɹɟɬɫɹ ɨɩɬɢɦɚɥɶɧɵɦ ɞɥɹ ɯɪɚɧɟɧɢɹ ɞɚɧɧɵɯ ɜ ɡɚɞɚɱɧɢɤɟ? Ɉɫɧɨɜɧɵɦɢ ɤɪɢɬɟɪɢɹɦɢ ɹɜɥɹɸɬɫɹ ɭɞɨɛɫɬɜɨ ɜ ɢɫɩɨɥɶɡɨɜɚɧɢɢ, ɤɨɦɩɚɤɬɧɨɫɬɶ ɢ ɭɧɢɜɟɪɫɚɥɶɧɨɫɬɶ. ɉɨɫɤɨɥɶɤɭ ɡɚɞɚɱɧɢɤ ɞɨɥɠɟɧ ɯɪɚɧɢɬɶ ɨɞɧɨɬɢɩɧɵɟ ɞɚɧɧɵɟ ɢ ɩɪɟɞɨɫɬɚɜɥɹɬɶ ɢɯ ɞɥɹ ɨɛɪɚɛɨɬɤɢ ɞɪɭɝɢɦ ɤɨɦɩɨɧɟɧɬɚɦ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚ, ɚ ɧɟ ɩɪɨɢɡɜɨɞɢɬɶ ɜɵɱɢɫɥɟɧɢɹ, ɬɨ ɮɭɧɤɰɢɨɧɚɥɶɧɨ ɡɚɞɚɱɧɢɤ ɞɨɥɠɟɧ ɹɜɥɹɬɶɫɹ ɛɚɡɨɣ ɞɚɧɧɵɯ. ɇɚɢɛɨɥɟɟ ɩɨɞɯɨɞɹɳɢɦ ɤɚɠɟɬɫɹ ɮɨɪɦɚɬ ɬɚɛɥɢɱɧɵɯ (ɪɟɥɹɰɢɨɧɧɵɯ) ɛɚɡ ɞɚɧɧɵɯ. ȼ ɫɨɜɪɟɦɟɧɧɵɯ ɨɩɟɪɚɰɢɨɧɧɵɯ ɫɢɫɬɟɦɚɯ ɩɪɟɞɭɫɦɨɬɪɟɧɵ ɪɚɡɥɢɱɧɵɟ ɫɩɨɫɨɛɵ ɨɛɦɟɧɚ ɞɚɧɧɵɦɢ ɦɟɠɞɭ ɩɪɢɥɨɠɟɧɢɹɦɢ. ɇɚɢɛɨɥɟɟ ɭɧɢɜɟɪɫɚɥɶɧɵɦ ɹɜɥɹɟɬɫɹ ɨɛɦɟɧ ɜ ɫɢɦɜɨɥɶɧɨɦ ɮɨɪɦɚɬɟ. Ɇɵ ɩɪɟɞɥɚɝɚɟɦ ɢɫɩɨɥɶɡɨɜɚɬɶ ɢɦɟɧɧɨ ɫɢɦɜɨɥɶɧɵɣ ɮɨɪɦɚɬ ɞɚɧɧɵɯ ɞɥɹ ɨɛɦɟɧɚ ɦɟɠɞɭ ɩɪɢɥɨɠɟɧɢɹɦɢ. ȼɨɩɪɨɫ ɤɨɧɤɪɟɬɧɨɣ ɪɟɚɥɢɡɚɰɢɢ ɨɛɦɟɧɚ ɨɫɬɚɜɢɦ ɡɚ ɩɪɟɞɟɥɚɦɢ ɪɚɫɫɦɨɬɪɟɧɢɹ, ɩɨɫɤɨɥɶɤɭ ɷɬɨ ɱɢɫɬɨ ɬɟɯɧɢɱɟɫɤɢɣ ɜɨɩɪɨɫ. Ɇɵ ɩɨɥɚɝɚɟɦ, ɱɬɨ ɜɧɟ ɡɚɜɢɫɢɦɨɫɬɢ ɨɬ ɬɨɝɨ, ɤɚɤɢɦ ɩɭɬɟɦ ɢ ɢɡ ɤɚɤɨɝɨ ɩɪɢɥɨɠɟɧɢɹ ɞɚɧɧɵɟ ɩɨɩɚɥɢ ɜ ɡɚɞɚɱɧɢɤ, ɢɯ ɩɪɟɞɫɬɚɜɥɟɧɢɟ ɞɨɥɠɧɨ ɛɵɬɶ ɨɞɢɧɚɤɨɜɵɦ (ɩɪɢɧɹɬɵɦ ɜ ɞɚɧɧɨɣ ɪɟɚɥɢɡɚɰɢɢ ɡɚɞɚɱɧɢɤɚ).
3.1.2 ɉɨɥɹ ɡɚɞɚɱɧɢɤɚ Ⱦɚɥɟɟ ɛɭɞɟɦ ɩɨɥɚɝɚɬɶ, ɱɬɨ ɡɚɞɚɱɧɢɤ ɹɜɥɹɟɬɫɹ ɪɟɥɹɰɢɨɧɧɨɣ ɛɚɡɨɣ ɞɚɧɧɵɯ ɢɡ ɨɞɧɨɣ ɬɚɛɥɢɰɵ. Ʉɚɠɞɨɦɭ ɩɪɢɦɟɪɭ ɫɨɨɬɜɟɬɫɬɜɭɟɬ ɨɞɧɚ ɡɚɩɢɫɶ ɛɚɡɵ ɞɚɧɧɵɯ. Ʉɚɠɞɨɦɭ ɞɚɧɧɨɦɭ – ɨɞɧɨ ɩɨɥɟ. ȼ ɞɚɧɧɨɦ ɪɚɡɞɟɥɟ ɪɚɫɫɦɨɬɪɟɧɵ ɞɨɩɭɫɬɢɦɵɟ ɬɢɩɵ ɩɨɥɟɣ, ɫ ɬɨɱɤɢ ɡɪɟɧɢɹ ɬɢɩɚ ɯɪɚɧɹɳɢɯɫɹ ɜ ɧɢɯ ɞɚɧɧɵɯ. ȼ ɪɚɡɞ. «ɋɨɫɬɚɜ ɞɚɧɧɵɯ ɡɚɞɚɱɧɢɤɚ» ɜɫɟ ɩɨɥɹ ɪɚɡɛɢɜɚɸɬɫɹ ɩɨ ɫɦɵɫɥɨɜɨɣ ɧɚɝɪɭɡɤɟ. ȼɫɟ ɩɨɥɹ ɛɚɡɵ ɞɚɧɧɵɯ ɦɨɠɧɨ ɪɚɡɛɢɬɶ ɧɚ ɱɟɬɵɪɟ ɬɢɩɚ – ɱɢɫɥɨɜɵɟ ɩɨɥɹ, ɬɟɤɫɬɨɜɵɟ ɩɨɥɹ, ɩɟɪɟɱɢɫɥɢɦɵɟ ɩɨɥɹ ɢ ɩɨɥɹ ɬɢɩɚ ɪɢɫɭɧɨɤ. ɑɢɫɥɨɜɵɟ ɩɨɥɹ. ɉɨɥɹ ɱɢɫɥɨɜɵɯ ɬɢɩɨɜ ɞɚɧɧɵɯ Integer, Long ɢ Real ɩɪɟɞɧɚɡɧɚɱɟɧɵ ɞɥɹ ɯɪɚɧɟɧɢɹ ɪɚɡɥɢɱɧɵɯ ɱɢɫɟɥ. Ɉɬɦɟɬɢɦ, ɩɨɥɹ ɱɢɫɥɨɜɨɝɨ ɬɢɩɚ ɦɨɝɭɬ ɧɟɫɬɢ ɥɸɛɭɸ ɫɦɵɫɥɨɜɭɸ ɧɚɝɪɭɡɤɭ. ɉɟɪɟɱɢɫɥɢɦɵɟ ɩɨɥɹ. ɉɨɥɹ ɩɟɪɟɱɢɫɥɢɦɨɝɨ ɬɢɩɚ ɫɥɭɠɚɬ ɞɥɹ ɯɪɚɧɟɧɢɹ ɤɚɱɟɫɬɜɟɧɧɵɯ ɩɪɢɡɧɚɤɨɜ – ɩɨɥɟɣ ɛɚɡɵ ɞɚɧɧɵɯ, ɫɨɞɟɪɠɚɳɢɯ, ɤɚɤ ɩɪɚɜɢɥɨ, ɬɟɤɫɬɨɜɭɸ ɢɧɮɨɪɦɚɰɢɸ, ɧɨ ɢɦɟɸɳɢɯ ɦɚɥɨɟ ɱɢɫɥɨ ɪɚɡɥɢɱɧɵɯ ɡɧɚɱɟɧɢɣ. ɉɪɨɫɬɟɣɲɢɦ ɩɪɢɦɟɪɨɦ ɩɨɥɹ ɩɟɪɟɱɢɫɥɢɦɨɝɨ ɬɢɩɚ ɹɜɥɹɟɬɫɹ ɩɨɥɟ «ɩɨɥ» – ɷɬɨ ɩɨɥɟ ɦɨɠɟɬ ɩɪɢɧɢɦɚɬɶ ɬɨɥɶɤɨ ɞɜɚ ɡɧɚɱɟɧɢɹ – «ɦɭɠɫɤɨɣ» ɢɥɢ «ɠɟɧɫɤɢɣ». ɉɨɥɟ ɩɟɪɟɱɢɫɥɢɦɨɝɨ ɬɢɩɚ ɧɟ ɯɪɚɧɢɬ ɫɨɨɬɜɟɬɫɬɜɭɸɳɟɝɨ ɬɟɤɫɬɨɜɨɝɨ ɡɧɚɱɟɧɢɹ, ɜɦɟɫɬɨ ɧɟɝɨ ɜ ɩɨɥɟ ɫɨɞɟɪɠɢɬɫɹ ɧɨɦɟɪ ɡɧɚɱɟɧɢɹ. ɉɨɥɹ ɩɟɪɟɱɢɫɥɢɦɨɝɨ ɬɢɩɚ ɦɨɝɭɬ ɛɵɬɶ ɬɨɥɶɤɨ ɜɯɨɞɧɵɦɢ ɞɚɧɧɵɦɢ, ɤɨɦɦɟɧɬɚɪɢɹɦɢ ɢɥɢ ɨɬɜɟɬɚɦɢ. ɋɬɪɨɤɢ (ɬɟɤɫɬɨɜɵɟ ɩɨɥɹ). ɉɨɥɹ ɬɟɤɫɬɨɜɨɝɨ ɬɢɩɚ ɩɪɟɞɧɚɡɧɚɱɟɧɵ ɞɥɹ ɯɪɚɧɟɧɢɹ ɬɟɫɬɨɜɨɣ ɢɧɮɨɪɦɚɰɢɢ. Ɉɧɢ ɦɨɝɭɬ ɛɵɬɶ ɬɨɥɶɤɨ ɤɨɦɦɟɧɬɚɪɢɹɦɢ. Ɋɢɫɭɧɨɤ. ɉɨɥɹ ɬɢɩɚ ɪɢɫɭɧɨɤ ɩɪɟɞɧɚɡɧɚɱɟɧɵ ɞɥɹ ɯɪɚɧɟɧɢɹ ɝɪɚɮɢɱɟɫɤɨɣ ɢɧɮɨɪɦɚɰɢɢ. ȼ ɞɚɧɧɨɣ ɪɚɛɨɬɟ ɧɟ ɭɫɬɚɧɚɜɥɢɜɚɟɬɫɹ ɫɬɚɧɞɚɪɬ ɯɪɚɧɟɧɢɹ ɩɨɥɟɣ ɬɢɩɚ ɪɢɫɭɧɨɤ. Ɉɝɨɜɚɪɢɜɚɟɬɫɹ ɬɨɥɶɤɨ ɫɩɨɫɨɛ ɯɪɚɧɟɧɢɹ ɩɨɥɟɣ ɬɢɩɚ ɪɢɫɭɧɨɤ ɧɚ ɞɢɫɤɟ ɞɥɹ ɮɚɣɥɨɜ ɡɚɞɚɱɧɢɤɚ, ɫɨɡɞɚɧɧɨɝɨ ɜ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɟ. ɉɪɢ ɩɟɪɟɞɚɱɟ ɪɢɫɭɧɤɨɜ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɭ ɢɫɩɨɥɶɡɭɟɬɫɹ ɮɨɪɦɚɬ, ɫɨɝɥɚɫɨɜɚɧɧɵɣ ɞɥɹ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ ɢ ɡɚɞɚɱɧɢɤɚ.
3.1.3 ɋɨɫɬɚɜ ɞɚɧɧɵɯ ɡɚɞɚɱɧɢɤɚ Ʉɨɦɩɨɧɟɧɬ ɡɚɞɚɱɧɢɤ ɹɜɥɹɟɬɫɹ ɧɟɨɛɯɨɞɢɦɨɣ ɱɚɫɬɶɸ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚ ɜɧɟ ɡɚɜɢɫɢɦɨɫɬɢ ɨɬ ɬɢɩɚ ɩɪɢɦɟɧɹɟɦɵɯ ɜ ɧɟɦ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ. Ɉɞɧɚɤɨ ɜ ɡɚɜɢɫɢɦɨɫɬɢ ɨɬ ɪɟɲɚɟɦɨɣ ɡɚɞɚɱɢ ɫɨɞɟɪɠɢɦɨɟ ɡɚɞɚɱɧɢɤɚ
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ɦɨɠɟɬ ɦɟɧɹɬɶɫɹ. Ɍɚɤ, ɧɚɩɪɢɦɟɪ, ɞɥɹ ɪɟɲɟɧɢɹ ɡɚɞɚɱɢ ɤɥɚɫɫɢɮɢɤɚɰɢɢ ɛɟɡ ɭɱɢɬɟɥɹ ɢɫɩɨɥɶɡɭɸɬ ɧɟɣɪɨɫɟɬɢ, ɨɫɧɨɜɚɧɧɵɟ ɧɚ ɦɟɬɨɞɟ ɞɢɧɚɦɢɱɟɫɤɢɯ ɹɞɟɪ [223, 261] (ɧɚɢɛɨɥɟɟ ɢɡɜɟɫɬɧɵɦ ɱɚɫɬɧɵɦ ɫɥɭɱɚɟɦ ɬɚɤɢɯ ɫɟɬɟɣ ɹɜɥɹɸɬɫɹ ɫɟɬɢ Ʉɨɯɨɧɟɧɚ [98, 99]). Ɂɚɞɚɱɧɢɤ ɞɥɹ ɬɚɤɨɣ ɫɟɬɢ ɞɨɥɠɟɧ ɫɨɞɟɪɠɚɬɶ ɬɨɥɶɤɨ ɜɟɤɬɨɪɵ ɜɯɨɞɧɵɯ ɞɚɧɧɵɯ ɢ ɩɪɟɞɨɛɪɚɛɨɬɚɧɧɵɯ ɜɯɨɞɧɵɯ ɞɚɧɧɵɯ. ɉɪɢ ɢɫɩɨɥɶɡɨɜɚɧɢɢ ɨɛɭɱɚɟɦɵɯ ɫɟɬɟɣ, ɨɫɧɨɜɚɧɧɵɯ ɧɚ ɩɪɢɧɰɢɩɟ ɞɜɨɣɫɬɜɟɧɧɨɫɬɢ, ɤ ɡɚɞɚɱɧɢɤɭ ɧɟɨɛɯɨɞɢɦɨ ɞɨɛɚɜɢɬɶ ɜɟɤɬɨɪ ɨɬɜɟɬɨɜ ɫɟɬɢ. Ʉɪɨɦɟ ɬɨɝɨ, ɧɟɤɨɬɨɪɵɟ ɢɫɫɥɟɞɨɜɚɬɟɥɢ ɯɨɬɹɬ ɢɦɟɬɶ ɜɨɡɦɨɠɧɨɫɬɶ ɩɪɨɫɦɨɬɪɟɬɶ ɨɬɜɟɬɵ, ɜɵɞɚɧɧɵɟ ɫɟɬɶɸ, ɜɟɤɬɨɪ ɨɰɟɧɨɤ ɩɪɢɦɟɪɚ, ɩɨɤɚɡɚɬɟɥɢ ɡɧɚɱɢɦɨɫɬɢ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɢ, ɜɨɡɦɨɠɧɨ, ɧɟɤɨɬɨɪɵɟ ɞɪɭɝɢɟ ɜɟɥɢɱɢɧɵ. ɉɨɷɬɨɦɭ, ɫɬɚɧɞɚɪɬɧɵɣ ɡɚɞɚɱɧɢɤ ɞɨɥɠɟɧ ɢɦɟɬɶ ɜɨɡɦɨɠɧɨɫɬɶ ɩɪɟɞɨɫɬɚɜɢɬɶ ɩɨɥɶɡɨɜɚɬɟɥɸ ɜɫɸ ɧɟɨɛɯɨɞɢɦɭɸ ɢɧɮɨɪɦɚɰɢɸ. ɇɨ ɟɫɥɢ ɡɚɞɚɱɧɢɤ ɜɫɟɝɞɚ ɛɭɞɟɬ ɨɬɜɨɞɢɬɶ ɩɚɦɹɬɶ ɞɥɹ ɜɫɟɯ ɧɟɨɛɯɨɞɢɦɵɯ ɜɟɥɢɱɢɧ, ɬɨ ɞɥɹ ɩɪɨɫɬɟɣɲɢɯ ɫɥɭɱɚɟɜ ɢɫɩɨɥɶɡɨɜɚɧɢɟ ɩɚɦɹɬɢ ɛɭɞɟɬ ɧɟɷɮɮɟɤɬɢɜɧɵɦ. Ɍɚɤɢɦ ɨɛɪɚɡɨɦ, ɜɨɡɧɢɤɚɟɬ ɩɪɨɬɢɜɨɪɟɱɢɟ, ɦɟɠɞɭ ɧɟɨɛɯɨɞɢɦɨɫɬɶɸ ɩɪɟɞɨɫɬɚɜɢɬɶ ɩɨɥɶɡɨɜɚɬɟɥɸ ɜɫɸ ɧɟɨɛɯɨɞɢɦɭɸ ɟɦɭ ɢɧɮɨɪɦɚɰɢɸ ɢ ɷɮɮɟɤɬɢɜɧɨɫɬɶɸ ɢɫɩɨɥɶɡɨɜɚɧɢɹ ɩɚɦɹɬɢ. Ɋɚɫɫɦɨɬɪɢɦ ɛɨɥɟɟ ɩɨɞɪɨɛɧɨ ɫɨɫɬɚɜ ɞɚɧɧɵɯ ɡɚɞɚɱɧɢɤɚ.
3.1.3.1 ɐɜɟɬ ɩɪɢɦɟɪɚ ɢ ɨɛɭɱɚɸɳɚɹ ɜɵɛɨɪɤɚ Ⱦɨɜɨɥɶɧɨ ɱɚɫɬɨ ɩɪɢ ɨɛɭɱɟɧɢɢ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ɜɨɡɧɢɤɚɟɬ ɧɟɨɛɯɨɞɢɦɨɫɬɶ ɢɫɩɨɥɶɡɨɜɚɬɶ ɜ ɨɛɭɱɟɧɢɢ ɧɟ ɜɫɟ ɩɪɢɦɟɪɵ ɡɚɞɚɱɧɢɤɚ, ɚ ɬɨɥɶɤɨ ɱɚɫɬɶ. ɇɚɩɪɢɦɟɪ, ɬɚɤɚɹ ɜɨɡɦɨɠɧɨɫɬɶ ɧɟɨɛɯɨɞɢɦɚ ɩɪɢ ɢɫɩɨɥɶɡɨɜɚɧɢɢ ɦɟɬɨɞɚ ɫɤɨɥɶɡɹɳɟɝɨ ɤɨɧɬɪɨɥɹ ɞɥɹ ɨɰɟɧɤɢ ɤɚɱɟɫɬɜɚ ɨɛɭɱɟɧɢɹ ɫɟɬɢ. ɋɭɳɟɫɬɜɭɟɬ ɧɟɫɤɨɥɶɤɨ ɫɩɨɫɨɛɨɜ ɪɟɚɥɢɡɚɰɢɢ ɬɚɤɨɣ ɜɨɡɦɨɠɧɨɫɬɢ. Ʉɪɨɦɟ ɬɨɝɨ, ɱɚɫɬɨ ɛɵɜɚɟɬ ɩɨɥɟɡɧɨ ɩɪɢɩɢɫɚɬɶ ɩɪɢɦɟɪɚɦ ɪɹɞ ɩɪɢɡɧɚɤɨɜ. Ɍɚɤ, ɩɪɢ ɩɪɨɫɦɨɬɪɟ ɡɚɞɚɱɧɢɤɚ, ɩɨɥɶɡɨɜɚɬɟɥɸ ɩɨɥɟɡɧɨ ɜɢɞɟɬɶ ɫɬɟɩɟɧɶ ɨɛɭɱɟɧɧɨɫɬɢ ɩɪɢɦɟɪɚ (ɧɚɩɪɢɦɟɪ, ɨɬɨɛɪɚɠɚɬɶ ɡɟɥɟɧɵɦ ɰɜɟɬɨɦ ɩɪɢɦɟɪɵ, ɤɨɬɨɪɵɟ ɪɟɲɚɸɬɫɹ ɫɟɬɶɸ ɢɞɟɚɥɶɧɨ, ɠɟɥɬɵɦ – ɬɟ, ɤɨɬɨɪɵɟ ɫɟɬɶ ɪɟɲɚɟɬ ɩɪɚɜɢɥɶɧɨ, ɧɨ ɧɟ ɢɞɟɚɥɶɧɨ, ɚ ɤɪɚɫɧɵɦ – ɬɟ, ɩɪɢ ɪɟɲɟɧɢɢ ɤɨɬɨɪɵɯ ɫɟɬɶ ɞɨɩɭɫɤɚɟɬ ɨɲɢɛɤɢ). Ɍɭ ɱɚɫɬɶ ɡɚɞɚɱɧɢɤɚ, ɤɨɬɨɪɚɹ ɜ ɞɚɧɧɵɣ ɦɨɦɟɧɬ ɢɫɩɨɥɶɡɭɟɬɫɹ ɜ ɨɛɭɱɟɧɢɢ ɧɟɣɪɨɧɧɨɣ ɫɟɬɢ, ɛɭɞɟɦ ɧɚɡɵɜɚɬɶ ɨɛɭɱɚɸɳɟɣ ɜɵɛɨɪɤɨɣ. Ⱦɥɹ ɜɵɞɟɥɟɧɢɹ ɢɡ ɡɚɞɚɱɧɢɤɚ ɨɛɭɱɚɸɳɟɣ ɜɵɛɨɪɤɢ ɩɪɟɞɥɚɝɚɟɬɫɹ ɢɫɩɨɥɶɡɨɜɚɬɶ ɦɟɯɚɧɢɡɦ «ɰɜɟɬɨɜ». ȿɫɥɢ ɜɫɟ ɩɪɢɦɟɪɵ ɩɨɤɪɚɲɟɧɵ ɜ ɧɟɤɨɬɨɪɵɟ ɰɜɟɬɚ, ɬɨ ɨɛɭɱɚɸɳɭɸ ɜɵɛɨɪɤɭ ɦɨɠɧɨ ɡɚɞɚɬɶ, ɭɤɚɡɚɜ ɰɜɟɬɚ ɩɪɢɦɟɪɨɜ, ɤɨɬɨɪɵɟ ɧɟɨɛɯɨɞɢɦɨ ɢɫɩɨɥɶɡɨɜɚɬɶ ɜ ɨɛɭɱɟɧɢɢ. ȼ ɫɨɨɬɜɟɬɫɬɜɢɢ ɫ ɩɪɟɞɥɚɝɚɟɦɨɣ ɫɯɟɦɨɣ, ɤɚɠɞɵɣ ɩɪɢɦɟɪ ɩɨɤɪɚɲɟɧ ɤɚɤɢɦ–ɬɨ ɰɜɟɬɨɦ, ɚ ɩɪɢ ɡɚɞɚɧɢɢ ɨɛɭɱɚɸɳɟɣ ɜɵɛɨɪɤɢ ɦɨɠɧɨ ɡɚɞɚɬɶ ɤɨɦɛɢɧɚɰɢɸ ɰɜɟɬɨɜ. ɋɯɟɦɚ ɪɚɛɨɬɵ ɫ ɰɜɟɬɚɦɢ ɞɟɬɚɥɶɧɨ ɪɚɫɫɦɨɬɪɟɧɚ ɜ ɪɚɡɞɟɥɟ «ɉɟɪɟɦɟɧɧɵɟ ɬɢɩɚ ɰɜɟɬ ɢ ɨɩɟɪɚɰɢɢ ɫ ɰɜɟɬɚɦɢ» ɝɥɚɜɵ «Ɉɛɳɢɣ ɫɬɚɧɞɚɪɬ». ȼɵɞɟɥɟɧɧɭɸ ɫ ɩɨɦɨɳɶɸ ɦɟɯɚɧɢɡɦɚ ɰɜɟɬɨɜ ɱɚɫɬɶ ɡɚɞɚɱɧɢɤɚ ɛɭɞɟɦ ɞɚɥɟɟ ɧɚɡɵɜɚɬɶ ɬɟɤɭɳɟɣ ɜɵɛɨɪɤɨɣ. Ɉɱɟɜɢɞɧɨ, ɱɬɨ ɨɛɭɱɚɸɳɚɹ ɜɵɛɨɪɤɚ ɹɜɥɹɟɬɫɹ ɱɚɫɬɧɵɦ ɫɥɭɱɚɟɦ ɬɟɤɭɳɟɣ ɜɵɛɨɪɤɢ.
3.1.3.2 ȼɯɨɞɧɵɟ ɞɚɧɧɵɟ ȼɯɨɞɧɵɟ ɞɚɧɧɵɟ – ɞɚɧɧɵɟ, ɧɟɨɛɯɨɞɢɦɵɟ ɞɥɹ ɪɟɲɟɧɢɹ ɫɟɬɶɸ ɩɪɢɦɟɪɚ. ȼɯɨɞɧɵɟ ɞɚɧɧɵɟ ɹɜɥɹɸɬɫɹ ɜɟɤɬɨɪɨɦ. ɋɭɳɟɫɬɜɭɟɬ ɜɫɟɝɨ ɧɟɫɤɨɥɶɤɨ ɜɢɞɨɜ ɜɯɨɞɧɵɯ ɞɚɧɧɵɯ. Ʉɚɠɞɵɣ ɤɨɦɩɨɧɟɧɬ ɜɟɤɬɨɪɚ ɜɯɨɞɧɵɯ ɞɚɧɧɵɯ ɦɨɠɟɬ ɛɵɬɶ: ɱɢɫɥɨɦ; ɩɨɥɟɦ ɫ ɨɝɪɚɧɢɱɟɧɧɵɦ ɱɢɫɥɨɦ ɫɨɫɬɨɹɧɢɣ; ɪɢɫɭɧɤɨɦ.
3.1.3.3 Ʉɨɦɦɟɧɬɚɪɢɢ ɉɨɥɶɡɨɜɚɬɟɥɸ, ɩɪɢ ɪɚɛɨɬɟ ɫ ɡɚɞɚɱɧɢɤɨɦ, ɱɚɫɬɨ ɛɵɜɚɟɬ ɧɟɨɛɯɨɞɢɦɨ ɢɦɟɬɶ ɜɨɡɦɨɠɧɨɫɬɶ ɢɞɟɧɬɢɮɢɰɢɪɨɜɚɬɶ ɩɪɢɦɟɪɵ ɧɟ ɬɨɥɶɤɨ ɩɨ ɧɨɦɟɪɚɦ. ɇɚɩɪɢɦɟɪ, ɩɪɢ ɪɚɛɨɬɟ ɫ ɦɟɞɢɰɢɧɫɤɢɦɢ ɛɚɡɚɦɢ ɞɚɧɧɵɯ ɩɨɥɟɡɧɨ ɢɦɟɬɶ ɩɨɥɟ, ɫɨɞɟɪɠɚɳɟɟ ɮɚɦɢɥɢɸ ɛɨɥɶɧɨɝɨ ɢɥɢ ɧɨɦɟɪ ɢɫɬɨɪɢɢ ɛɨɥɟɡɧɢ. Ⱦɥɹ ɷɬɢɯ ɰɟɥɟɣ ɜ ɡɚɞɚɱɧɢɤɟ ɦɨɠɟɬ ɩɨɬɪɟɛɨɜɚɬɶɫɹ ɯɪɚɧɢɬɶ ɜɟɤɬɨɪ ɤɨɦɦɟɧɬɚɪɢɟɜ, ɤɨɬɨɪɵɟ ɧɟ ɦɨɝɭɬ ɛɵɬɶ ɢɫɩɨɥɶɡɨɜɚɧɵ ɜ ɨɛɭɱɟɧɢɢ.
3.1.3.4 ɉɪɟɞɨɛɪɚɛɨɬɚɧɧɵɟ ɞɚɧɧɵɟ ɉɪɟɞɨɛɪɚɛɨɬɚɧɧɵɟ ɞɚɧɧɵɟ – ɷɬɨ ɜɟɤɬɨɪ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɫɟɬɢ, ɩɨɥɭɱɟɧɧɵɣ ɢɡ ɜɯɨɞɧɵɯ ɞɚɧɧɵɯ ɩɨɫɥɟ ɩɪɟɞɨɛɪɚɛɨɬɤɢ, ɜɵɩɨɥɧɹɟɦɨɣ ɤɨɦɩɨɧɟɧɬɨɦ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ. ɏɪɚɧɟɧɢɟ ɡɚɞɚɱɧɢɤɨɦ ɷɬɨɝɨ ɜɟɤɬɨɪɚ ɧɟɨɛɹɡɚɬɟɥɶɧɨ. Ʉɚɠɞɵɣ ɷɥɟɦɟɧɬ ɜɟɤɬɨɪɚ ɩɪɟɞɨɛɪɚɛɨɬɚɧɧɵɯ ɞɚɧɧɵɯ ɹɜɥɹɟɬɫɹ ɞɟɣɫɬɜɢɬɟɥɶɧɵɦ ɱɢɫɥɨɦ. ɋɥɟɞɭɟɬ ɨɬɦɟɬɢɬɶ, ɱɬɨ ɥɸɛɚɹ ɧɟɬɪɢɜɢɚɥɶɧɚɹ ɩɪɟɞɨɛɪɚɛɨɬɤɚ, ɤɚɤ ɩɪɚɜɢɥɨ, ɢɡɦɟɧɹɟɬ ɪɚɡɦɟɪɧɨɫɬɶ ɜɟɤɬɨɪɚ.
3.1.3.5 ɉɪɚɜɢɥɶɧɵɟ ɨɬɜɟɬɵ ɉɪɚɜɢɥɶɧɵɟ ɨɬɜɟɬɵ – ɜɟɤɬɨɪ ɨɬɜɟɬɨɜ, ɤɨɬɨɪɵɟ ɞɨɥɠɧɚ ɜɵɞɚɬɶ ɨɛɭɱɟɧɧɚɹ ɧɟɣɪɨɧɧɚɹ ɫɟɬɶ ɩɪɢ ɪɟɲɟɧɢɢ ɩɪɢɦɟɪɚ. ɗɬɨɬ ɜɟɤɬɨɪ ɚɛɫɨɥɸɬɧɨ ɧɟɨɛɯɨɞɢɦ ɩɪɢ ɨɛɭɱɟɧɢɢ ɫɟɬɟɣ ɫ ɭɱɢɬɟɥɟɦ. ɉɪɢ ɢɫɩɨɥɶɡɨɜɚɧɢɢ ɞɪɭɝɢɯ ɜɢɞɨɜ ɫɟɬɟɣ ɯɪɚɧɟɧɢɟ ɡɚɞɚɱɧɢɤɨɦ ɷɬɨɝɨ ɜɟɤɬɨɪɚ ɧɟɨɛɹɡɚɬɟɥɶɧɨ. Ʉɨɦɩɨɧɟɧɬɚɦɢ ɜɟɤɬɨɪɚ ɨɬɜɟɬɚ ɦɨɝɭɬ ɛɵɬɶ ɤɚɤ ɱɢɫɥɚ, ɬɚɤ ɢ ɩɨɥɹ ɫ ɨɝɪɚɧɢɱɟɧɧɵɦ ɧɚɛɨɪɨɦ ɫɨɫɬɨɹɧɢɣ. ȼ ɩɟɪɜɨɦ ɫɥɭɱɚɟ ɛɭɞɟɦ ɝɨɜɨɪɢɬɶ ɨ ɡɚɞɚɱɟ ɚɩɩɪɨɤɫɢɦɚɰɢɢ ɮɭɧɤɰɢɢ, ɚ ɜɨ ɜɬɨɪɨɦ – ɨ ɡɚɞɚɱɟ ɤɥɚɫɫɢɮɢɤɚɰɢɢ ɨɛɴɟɤɬɨɜ.
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3.1.3.6 ɉɨɥɭɱɟɧɧɵɟ ɨɬɜɟɬɵ ɉɨɥɭɱɟɧɧɵɟ ɨɬɜɟɬɵ – ɜɟɤɬɨɪ ɨɬɜɟɬɨɜ, ɜɵɞɚɧɧɵɯ ɫɟɬɶɸ ɩɪɢ ɪɟɲɟɧɢɢ ɩɪɢɦɟɪɚ. Ⱦɥɹ ɡɚɞɚɱɧɢɤɚ ɯɪɚɧɟɧɢɟ ɷɬɨɣ ɱɚɫɬɢ ɩɪɢɦɟɪɚ ɧɟ ɨɛɹɡɚɬɟɥɶɧɨ.
3.1.3.7 Ɉɰɟɧɤɢ Ɉɰɟɧɤɢ – ɜɟɤɬɨɪ ɨɰɟɧɨɤ, ɩɨɥɭɱɟɧɧɵɣ ɫɟɬɶɸ ɡɚ ɪɟɲɟɧɢɟ ɜɫɟɯ ɩɨɞɡɚɞɚɱ ɩɪɢɦɟɪɚ (ɱɢɫɥɨ ɩɨɞɡɚɞɚɱ ɪɚɜɧɨ ɱɢɫɥɭ ɨɬɜɟɬɨɜ ɩɪɢɦɟɪɚ). ɏɪɚɧɟɧɢɟ ɷɬɨɝɨ ɜɟɤɬɨɪɚ ɡɚɞɚɱɧɢɤɨɦ ɧɟ ɨɛɹɡɚɬɟɥɶɧɨ.
3.1.3.8 ȼɟɫ ɩɪɢɦɟɪɚ ȼɟɫ ɩɪɢɦɟɪɚ – ɫɤɚɥɹɪɧɵɣ ɩɚɪɚɦɟɬɪ, ɩɨɡɜɨɥɹɸɳɢɣ ɪɟɝɭɥɢɪɨɜɚɬɶ ɢɧɬɟɧɫɢɜɧɨɫɬɶ ɭɱɚɫɬɢɹ ɩɪɢɦɟɪɚ ɜ ɩɪɨɰɟɫɫɟ ɨɛɭɱɟɧɢɹ. Ⱦɥɹ ɧɟ ɨɛɭɱɚɟɦɵɯ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ɜɟɫ ɩɪɢɦɟɪɚ ɦɨɠɟɬ ɢɫɩɨɥɶɡɨɜɚɬɶɫɹ ɞɥɹ ɭɱɟɬɚ ɜɤɥɚɞɚ ɞɚɧɧɵɯ ɩɪɢɦɟɪɚ ɜ ɮɨɪɦɢɪɭɟɦɭɸ ɤɚɪɬɭ ɫɜɹɡɟɣ. ɉɪɢɦɟɧɟɧɢɟ ɜɟɫɨɜ ɩɪɢɦɟɪɨɜ ɡɚɜɢɫɢɬ ɨɬ ɬɢɩɚ ɢɫɩɨɥɶɡɭɟɦɨɣ ɫɟɬɢ.
3.1.3.9 Ⱦɨɫɬɨɜɟɪɧɨɫɬɶ ɨɬɜɟɬɚ ɉɪɢ ɫɨɫɬɚɜɥɟɧɢɢ ɡɚɞɚɱɧɢɤɚ ɨɬɜɟɬɵ ɞɨɜɨɥɶɧɨ ɱɚɫɬɨ ɩɨɥɭɱɚɸɬɫɹ ɤɚɤ ɪɟɡɭɥɶɬɚɬ ɢɡɦɟɪɟɧɢɹ ɢɥɢ ɩɭɬɟɦ ɥɨɝɢɱɟɫɤɢɯ ɜɵɜɨɞɨɜ ɜ ɭɫɥɨɜɢɹɯ ɧɟɱɟɬɤɨɣ ɢɧɮɨɪɦɚɰɢɢ (ɧɚɩɪɢɦɟɪ, ɜ ɦɟɞɢɰɢɧɟ). ȼ ɷɬɢɯ ɫɥɭɱɚɹɯ ɨɞɧɢ ɨɬɜɟɬɵ ɢɦɟɸɬ ɛɨɥɶɲɭɸ ɞɨɫɬɨɜɟɪɧɨɫɬɶ, ɱɟɦ ɞɪɭɝɢɟ. ɇɟɤɨɬɨɪɵɟ ɫɩɨɫɨɛɵ ɩɨɫɬɪɨɟɧɢɹ ɨɰɟɧɤɢ ɢɥɢ ɮɨɪɦɢɪɨɜɚɧɢɹ ɤɚɪɬɵ ɫɜɹɡɟɣ ɧɟɣɪɨɧɧɨɣ ɫɟɬɢ ɩɨɡɜɨɥɹɸɬ ɢɫɩɨɥɶɡɨɜɚɬɶ ɷɬɢ ɞɚɧɧɵɟ. Ⱦɨɫɬɨɜɟɪɧɨɫɬɶ ɨɬɜɟɬɚ ɹɜɥɹɟɬɫɹ ɜɟɤɬɨɪɨɦ, ɩɨɫɤɨɥɶɤɭ ɨɬɜɟɬ ɤɚɠɞɨɣ ɩɨɞɡɚɞɚɱɢ ɞɚɧɧɨɝɨ ɩɪɢɦɟɪɚ ɦɨɠɟɬ ɢɦɟɬɶ ɫɜɨɸ ɞɨɫɬɨɜɟɪɧɨɫɬɶ. Ʉɚɠɞɵɣ ɷɥɟɦɟɧɬ ɜɟɤɬɨɪɚ ɞɨɫɬɨɜɟɪɧɨɫɬɢ ɨɬɜɟɬɚ ɹɜɥɹɟɬɫɹ ɞɟɣɫɬɜɢɬɟɥɶɧɵɦ ɱɢɫɥɨɦ ɨɬ ɧɭɥɹ ɞɨ ɟɞɢɧɢɰɵ.
3.1.3.10 ɍɜɟɪɟɧɧɨɫɬɶ ɜ ɨɬɜɟɬɟ ɉɪɢ ɢɫɩɨɥɶɡɨɜɚɧɢɢ ɧɟɤɨɬɨɪɵɯ ɜɢɞɨɜ ɨɰɟɧɤɢ (ɫɦ. ɝɥɚɜɭ «Ɉɰɟɧɤɚ ɢ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɨɬɜɟɬɚ») ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɨɬɜɟɬɚ ɫɩɨɫɨɛɟɧ ɨɰɟɧɢɬɶ ɭɜɟɪɟɧɧɨɫɬɶ ɫɟɬɢ ɜ ɩɨɥɭɱɟɧɧɨɦ ɨɬɜɟɬɟ. ȼɟɤɬɨɪ ɭɜɟɪɟɧɧɨɫɬɢ ɫɟɬɢ ɜ ɨɬɜɟɬɚɯ (ɞɥɹ ɤɚɠɞɨɝɨ ɨɬɜɟɬɚ ɫɜɨɹ ɭɜɟɪɟɧɧɨɫɬɶ) ɦɨɠɟɬ ɨɤɚɡɚɬɶɫɹ ɩɨɥɟɡɧɵɦ ɞɥɹ ɩɨɥɶɡɨɜɚɬɟɥɹ. Ʉɚɠɞɵɣ ɷɥɟɦɟɧɬ ɜɟɤɬɨɪɚ ɭɜɟɪɟɧɧɨɫɬɢ ɜ ɨɬɜɟɬɟ ɹɜɥɹɟɬɫɹ ɞɟɣɫɬɜɢɬɟɥɶɧɵɦ ɱɢɫɥɨɦ ɨɬ ɧɭɥɹ ɞɨ ɟɞɢɧɢɰɵ. ȼɫɟ ɩɟɪɟɱɢɫɥɟɧɧɵɟ ɉɪɢɦɟɪ 3 Ɉɛɳɢɟ ɞɚɧɧɵɟ ɡɚɞɚɱɧɢɤɚ ɜɵɲɟ ɜɟɤɬɨɪɵ ɦɨɠɧɨ ɪɚɡɛɢɬɶ ɉɪɢɦɟɪ 2 Ɉɩɢɫɚɧɢɟ ɩɨɥɟɣ ɞɚɧɧɵɯ ɧɚ ɱɟɬɵɪɟ ɬɢɩɚ ɩɨ ɫɬɪɭɤɬɭɪɟ: ɉɪɢɦɟɪ 1 · ȼɯɨɞɧɵɟ ɞɚɧɧɵɟ. ɌɚɈɩɢɫɚɧɢɟ ɩɨɥɟɣ ɤɨɦɦɟɧɬɚɪɢɟɜ ȼɟɫ ɢ ɰɜɟɬ ɩɪɢɦɟɪɚ ɤɢɯ ɜɟɤɬɨɪɨɜ ɨɛɵɱɧɨ Ɉɩɢɫɚɧɢɟ ɩɨɥɟɣ ɨɬɜɟɬɨɜ ȼɯɨɞɧɵɟ ɞɚɧɧɵɟ ɩɪɢɦɟɪɚ ɞɜɚ – ɜɟɤɬɨɪ ɨɩɢɫɚɧɢɹ ɩɨɥɟɣ ɞɚɧɧɵɯ (ɫɨɞɟɪɉɪɟɞɨɛɪɚɛɨɬɚɧɧɵɟ ɞɚɧɧɵɟ ɠɢɬ ɨɩɢɫɚɧɢɟ ɩɨɥɟɣ Ʉɨɦɦɟɧɬɚɪɢɢ ɩɪɢɦɟɪɚ ɞɚɧɧɵɯ: ɢɦɹ ɩɨɥɹ, ɟɝɨ ɉɪɚɜɢɥɶɧɵɟ ɨɬɜɟɬɵ ɬɢɩ ɢ ɜɨɡɦɨɠɧɨ ɧɟɤɨɬɨɪɭɸ ɞɨɩɨɥɧɢɬɟɥɶɧɭɸ ɉɨɥɭɱɟɧɧɵɟ ɨɬɜɟɬɵ ɢɧɮɨɪɦɚɰɢɸ) ɢ ɫɨɛɫɬȾɨɫɬɨɜɟɪɧɨɫɬɢ ɨɬɜɟɬɨɜ ɜɟɧɧɨ ɜɟɤɬɨɪ ɞɚɧɧɵɯ. Ɉɰɟɧɤɢ ɉɪɢɱɟɦ ɤɚɠɞɵɣ ɩɪɢɦɟɪ ɢɦɟɟɬ ɫɜɨɣ ɜɟɤɬɨɪ ɍɜɟɪɟɧɧɨɫɬɶ ɜ ɨɬɜɟɬɟ ɞɚɧɧɵɯ, ɧɨ ɜɟɤɬɨɪ ɨɩɢɫɚɧɢɹ ɩɨɥɟɣ ɞɚɧɧɵɯ Ɋɢɫ. 1. ɋɯɟɦɚ ɞɚɧɧɵɯ ɡɚɞɚɱɧɢɤɚ. ɨɞɢɧ ɞɥɹ ɜɫɟɯ ɩɪɢɦɟɪɨɜ ɡɚɞɚɱɧɢɤɚ. ɗɬɢ ɜɟɤɬɨɪɵ ɢɦɟɸɬ ɨɞɢɧɚɤɨɜɨɟ ɱɢɫɥɨ ɷɥɟɦɟɧɬɨɜ, ɢ ɢɯ ɷɥɟɦɟɧɬɵ ɩɨɩɚɪɧɨ ɫɨɨɬɜɟɬɫɬɜɭɸɬ ɞɪɭɝ ɞɪɭɝɭ. · ȼɟɤɬɨɪ ɨɬɜɟɬɨɜ. ɉɪɢ ɨɛɭɱɟɧɢɢ ɫ ɭɱɢɬɟɥɟɦ, ɜ ɡɚɞɚɱɧɢɤɟ ɟɫɬɶ, ɩɨ ɤɪɚɣɧɟɣ ɦɟɪɟ, ɞɜɚ ɜɟɤɬɨɪɚ ɷɬɨɝɨ ɜɢɞɚ – ɜɟɤɬɨɪ ɨɩɢɫɚɧɢɹ ɩɨɥɟɣ ɨɬɜɟɬɨɜ ɢ ɜɟɤɬɨɪ ɩɪɚɜɢɥɶɧɵɯ ɨɬɜɟɬɨɜ. Ʉɪɨɦɟ ɬɨɝɨ, ɜɨɡɦɨɠɧɨ ɯɪɚɧɟɧɢɟ ɜ ɡɚɞɚɱɧɢɤɟ ɜɟɤɬɨɪɨɜ ɜɵɱɢɫɥɟɧɧɵɯ ɨɬɜɟɬɨɜ, ɞɨɫɬɨɜɟɪɧɨɫɬɢ ɨɬɜɟɬɨɜ ɢ ɭɜɟɪɟɧɧɨɫɬɢ ɜ ɨɬɜɟɬɟ. ȼɟɤɬɨɪ ɨɩɢɫɚɧɢɹ ɩɨɥɟɣ ɨɬɜɟɬɨɜ – ɨɞɢɧ ɞɥɹ ɜɫɟɯ ɩɪɢɦɟɪɨɜ ɡɚɞɚɱɧɢɤɚ. ȼɫɟ ɨɫɬɚɥɶɧɵɟ ɜɟɤɬɨɪɵ ɞɚɧɧɨɝɨ ɬɢɩɚ ɯɪɚɧɹɬɫɹ ɩɨ ɨɞɧɨɦɭ ɷɤɡɟɦɩɥɹɪɭ ɤɚɠɞɨɝɨ ɜɟɤɬɨɪɚ ɧɚ ɩɪɢɦɟɪ. · ȼɟɤɬɨɪ ɤɨɦɦɟɧɬɚɪɢɟɜ. Ɍɚɤɢɯ ɜɟɤɬɨɪɨɜ ɨɛɵɱɧɨ ɬɨɥɶɤɨ ɞɜɚ – ɜɟɤɬɨɪ ɨɩɢɫɚɧɢɹ ɩɨɥɟɣ ɤɨɦɦɟɧɬɚɪɢɟɜ ɢ ɜɟɤɬɨɪ ɤɨɦɦɟɧɬɚɪɢɟɜ. ȼɟɤɬɨɪ ɨɩɢɫɚɧɢɹ ɩɨɥɟɣ ɤɨɦɦɟɧɬɚɪɢɟɜ – ɨɞɢɧ ɧɚ ɜɟɫɶ ɡɚɞɚɱɧɢɤ, ɚ ɜɟɤɬɨɪ ɤɨɦɦɟɧɬɚɪɢɟɜ – ɨɞɢɧ ɧɚ ɩɪɢɦɟɪ.
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ɇɚ ɪɢɫ. 1 ɩɪɢɜɟɞɟɧɨ ɫɯɟɦɚɬɢɱɟɫɤɨɟ ɭɫɬɪɨɣɫɬɜɨ ɡɚɞɚɱɧɢɤɚ. Ɍɚɤɨɟ ɩɪɟɞɫɬɚɜɥɟɧɢɟ ɞɚɧɧɵɯ ɩɨɡɜɨɥɹɟɬ ɝɢɛɤɨ ɢɫɩɨɥɶɡɨɜɚɬɶ ɩɚɦɹɬɶ. Ɉɞɧɚɤɨ ɫɥɟɞɭɟɬ ɭɱɟɫɬɶ, ɱɬɨ ɱɚɫɬɶ ɩɨɥɟɣ ɦɨɠɟɬ ɩɟɪɟɯɨɞɢɬɶ ɢɡ ɨɞɧɨɝɨ ɜɟɤɬɨɪɚ ɜ ɞɪɭɝɨɣ. ɇɚɩɪɢɦɟɪ, ɩɪɢ ɢɫɤɥɸɱɟɧɢɢ ɨɞɧɨɝɨ ɜɯɨɞɧɨɝɨ ɞɚɧɧɨɝɨ ɢɡ ɢɫɩɨɥɶɡɨɜɚɧɢɹ (ɫɦ. ɝɥɚɜɭ «Ʉɨɧɬɪɚɫɬɟɪ»), ɫɨɨɬɜɟɬɫɬɜɭɸɳɟɟ ɟɦɭ ɩɨɥɟ ɩɟɪɟɯɨɞɢɬ ɢɡ ɜɟɤɬɨɪɚ ɜɯɨɞɧɵɯ ɞɚɧɧɵɯ ɜ ɜɟɤɬɨɪ ɤɨɦɦɟɧɬɚɪɢɟɜ.
3.1.4 Ɋɟɤɨɦɟɧɞɭɟɦɨɟ ɭɩɪɚɜɥɟɧɢɟ ɩɚɦɹɬɶɸ ɇɚɢɛɨɥɟɟ ɛɵɫɬɪɵɦ ɜ ɪɚɛɨɬɟ ɹɜɥɹɟɬɫɹ ɯɪɚɧɟɧɢɟ ɜɫɟɯ ɩɪɢɦɟɪɨɜ ɜ ɩɚɦɹɬɢ. Ɉɞɧɚɤɨ ɩɪɢ ɛɨɥɶɲɨɦ ɱɢɫɥɟ ɩɪɢɦɟɪɨɜ ɯɪɚɧɟɧɢɟ ɜɫɟɝɨ ɡɚɞɚɱɧɢɤɚ ɜ ɩɚɦɹɬɢ ɹɜɥɹɟɬɫɹ ɧɟɜɨɡɦɨɠɧɵɦ. ɇɨ ɩɪɢɦɟɧɟɧɢɟ ɦɚɫɤɢ ɢɫɩɨɥɶɡɭɟɦɵɯ ɞɚɧɧɵɯ ɩɨɡɜɨɥɹɟɬ ɡɚɞɚɱɧɢɤɭ ɞɟɪɠɚɬɶ ɜ ɩɚɦɹɬɢ ɤɨɦɩɶɸɬɟɪɚ ɬɨɥɶɤɨ ɬɟ ɜɟɤɬɨɪɵ ɞɚɧɧɵɯ ɩɪɢɦɟɪɨɜ, ɤɨɬɨɪɵɟ ɛɭɞɟɬ ɧɟɨɛɯɨɞɢɦɨ ɜɨɡɜɪɚɳɚɬɶ ɜ ɡɚɩɪɨɫɚɯ. Ɍɚɤ, ɧɚɩɪɢɦɟɪ, ɩɪɢ ɨɛɭɱɟɧɢɢ ɫɟɬɢ ɦɟɬɨɞɨɦ ɨɛɪɚɬɧɨɝɨ ɪɚɫɩɪɨɫɬɪɚɧɟɧɢɹ ɨɲɢɛɤɢ ɡɚɞɚɱɧɢɤɭ ɬɪɟɛɭɟɬɫɹ ɯɪɚɧɢɬɶ ɜ ɩɚɦɹɬɢ ɬɨɥɶɤɨ ɜɟɤɬɨɪɵ ɩɪɚɜɢɥɶɧɵɯ ɨɬɜɟɬɨɜ ɢ ɩɪɟɞɨɛɪɚɛɨɬɚɧɧɵɯ ɞɚɧɧɵɯ. ȼɫɟ ɨɫɬɚɥɶɧɵɟ ɜɟɤɬɨɪɵ ɦɨɠɧɨ ɯɪɚɧɢɬɶ ɧɚ ɞɢɫɤɚɯ. Ɍɚɤɨɟ ɭɩɪɚɜɥɟɧɢɟ ɩɨɡɜɨɥɹɟɬ ɦɚɤɫɢɦɚɥɶɧɨ ɷɮɮɟɤɬɢɜɧɨ ɢɫɩɨɥɶɡɨɜɚɬɶ ɨɩɟɪɚɬɢɜɧɭɸ ɩɚɦɹɬɶ ɤɨɦɩɶɸɬɟɪɚ. Ɂɚɞɚɱɧɢɤ ɜ ɯɨɞɟ ɪɚɛɨɬɵ ɦɨɠɟɬ ɜɵɛɢɪɚɬɶ ɜɫɸ ɫɜɨɛɨɞɧɭɸ ɩɚɦɹɬɶ ɞɥɹ ɯɪɚɧɟɧɢɹ ɞɚɧɧɵɯ. Ɉɞɧɚɤɨ ɨɧ ɞɨɥɠɟɧ ɢɦɟɬɶ ɷɤɨɧɨɦɧɵɣ ɪɟɠɢɦ – ɪɟɠɢɦ ɪɚɛɨɬɵ ɩɪɢ ɦɢɧɢɦɚɥɶɧɨɦ ɢɫɩɨɥɶɡɨɜɚɧɢɢ ɨɩɟɪɚɬɢɜɧɨɣ ɩɚɦɹɬɢ. Ɂɚɩɪɨɫ «Ɉɫɜɨɛɨɞɢɬɶ ɩɚɦɹɬɶ» (FreeMemory), ɨɩɢɫɚɧɧɵɣ ɜ ɝɥɚɜɟ «Ɉɛɳɢɣ ɫɬɚɧɞɚɪɬ», ɨɬɧɨɫɢɬɫɹ ɤɨ ɜɫɟɦɭ ɡɚɞɚɱɧɢɤɭ ɜ ɰɟɥɨɦ. ɉɪɢ ɨɛɪɚɛɨɬɤɟ ɫɥɟɞɭɸɳɢɯ ɡɚɩɪɨɫɨɜ ɡɚɞɚɱɧɢɤ ɦɨɠɟɬ ɫɧɨɜɚ ɡɚɛɢɪɚɬɶ ɫɜɨɛɨɞɧɭɸ ɩɚɦɹɬɶ. Ɍɚɤɨɟ ɢɫɩɨɥɶɡɨɜɚɧɢɟ ɩɚɦɹɬɢ ɩɨɡɜɨɥɹɟɬ ɨɫɬɚɥɶɧɵɦ ɤɨɦɩɨɧɟɧɬɚɦ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚ ɢɫɩɨɥɶɡɨɜɚɬɶ ɢɡɥɢɲɤɢ ɩɚɦɹɬɢ, ɩɨɬɪɟɛɥɹɟɦɨɣ ɡɚɞɚɱɧɢɤɨɦ. ɇɚɩɪɢɦɟɪ, ɩɪɢ ɡɚɩɭɫɤɟ ɩɪɨɰɟɞɭɪɵ ɨɛɭɱɟɧɢɹ ɤɨɦɩɨɧɟɧɬɟ ɧɟɣɪɨɧɧɚɹ ɫɟɬɶ ɧɟɨɛɯɨɞɢɦɨ ɫɨɡɞɚɬɶ ɧɟɫɤɨɥɶɤɨ ɤɨɩɢɣ ɫɢɧɚɩɬɢɱɟɫɤɨɣ ɤɚɪɬɵ. ȿɫɥɢ ɨɛɧɚɪɭɠɟɧɚ ɧɟɯɜɚɬɤɚ ɩɚɦɹɬɢ, ɨɧɚ ɜɵɞɚɟɬ ɦɚɤɪɨɤɨɦɩɨɧɟɧɬɭ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪ ɡɚɩɪɨɫ ɧɚ ɨɫɜɨɛɨɠɞɟɧɢɟ ɩɚɦɹɬɢ, ɢ ɜɫɟ ɤɨɦɩɨɧɟɧɬɵ, ɜɵɩɨɥɧɹɹ ɷɬɨɬ ɡɚɩɪɨɫ, ɨɫɬɚɜɥɹɸɬ ɫɟɛɟ ɬɨɥɶɤɨ ɦɢɧɢɦɚɥɶɧɨ ɧɟɨɛɯɨɞɢɦɭɸ ɩɚɦɹɬɶ. ɉɨɫɥɟ ɫɨɡɞɚɧɢɹ ɧɟɨɛɯɨɞɢɦɨɝɨ ɱɢɫɥɚ ɤɨɩɢɣ ɫɢɧɚɩɬɢɱɟɫɤɨɣ ɤɚɪɬɵ ɨɫɬɚɬɨɤ ɩɚɦɹɬɢ ɦɨɝɭɬ ɢɫɩɨɥɶɡɨɜɚɬɶ ɬɟ ɤɨɦɩɨɧɟɧɬɵ, ɤɨɬɨɪɵɦ ɨɧɚ ɧɟɨɛɯɨɞɢɦɚ.
3.2 ɋɬɚɧɞɚɪɬ ɩɟɪɜɨɝɨ ɭɪɨɜɧɹ ɤɨɦɩɨɧɟɧɬɚ ɡɚɞɚɱɧɢɤ ȼ ɷɬɨɦ ɪɚɡɞɟɥɟ ɩɪɢɜɨɞɢɬɫɹ ɨɩɢɫɚɧɢɟ ɯɪɚɧɟɧɢɹ ɡɚɞɚɱɧɢɤɚ ɧɚ ɜɧɟɲɧɟɦ ɧɨɫɢɬɟɥɟ.
Ɍɚɛɥɢɰɚ 1. Ʉɥɸɱɟɜɵɟ ɫɥɨɜɚ ɫɩɟɰɢɮɢɱɟɫɤɢɟ ɞɥɹ ɹɡɵɤɚ ɨɩɢɫɚɧɢɹ ɡɚɞɚɱɧɢɤɚ Ʉɥɸɱɟɜɨɟ ɫɥɨɜɨɄɪɚɬɤɨɟ ɨɩɢɫɚɧɢɟ 3.2.1 əɡɵɤ ɨɩɢɫɚɧɢɹ ɡɚɞɚɱTaskBook Ɂɚɝɨɥɨɜɨɤ ɨɩɢɫɚɧɢɹ ɡɚɞɚɱɧɢɤɚ Structure Ɂɚɝɨɥɨɜɨɤ ɨɩɢɫɚɧɢɹ ɫɬɪɭɤɬɭɪɵ ɡɚɞɚɱɧɢɤɚ ɧɢɤɚ Field ɇɚɱɚɥɨ ɨɩɢɫɚɧɢɹ ɩɨɥɹ ȼ ɹɡɵɤɟ ɨɩɢɫɚɧɢɹ ɡɚɞɚɱɧɢɤɚ Picture ɉɨɥɟ ɬɢɩɚ ɪɢɫɭɧɨɤ ɢɫɩɨɥɶɡɭɟɬɫɹ ɪɹɞ ɤɥɸɱɟɜɵɯ ɫɥɨɜ, Source Ɉɩɢɫɚɧɢɟ ɢɫɬɨɱɧɢɤɚ ɞɚɧɧɵɯ ɫɩɟɰɢɮɢɱɟɫɤɢɯ ɞɥɹ ɷɬɨɝɨ ɹɡɵɤɚ. ɗɬɢ External Ɉɩɢɫɚɧɢɟ ɜɧɟɲɧɟɝɨ ɢɫɬɨɱɧɢɤɚ ɞɚɧɧɵɯ ɤɥɸɱɟɜɵɟ ɫɥɨɜɚ ɩɪɢɜɟɞɟɧɵ ɜ ɬɚɛɥ. 1. ɋɩɢɫɨɤ ɩɪɟɞɨɩɪɟɞɟɥɟɧɧɵɯ ɤɨɧɫɬɚɧɬ ɹɡɵɤɚ ɨɩɢɫɚɧɢɹ ɡɚɞɚɱɧɢɤɚ ɩɪɢɜɟɞɟɧ ɜ ɬɚɛɥ. 2. ɗɬɢ ɤɨɧɫɬɚɧɬɵ ɢɫɩɨɥɶɡɭɸɬɫɹ ɩɪɢ ɭɤɚɡɚɧɢɢ ɬɢɩɚ Ɍɚɛɥɢɰɚ 2 ɜɟɤɬɨɪɚ, ɤ ɤɨɬɨɪɨɦɭ ɩɪɢɧɚɞɥɟɠɢɬ ɉɪɟɞɨɩɪɟɞɟɥɟɧɧɵɟ ɤɨɧɫɬɚɧɬɵ ɨɩɢɫɵɜɚɟɦɨɟ ɩɨɥɟ, ɩɪɢ ɭɤɚɡɚɧɢɢ ɂɞɟɧɬɢɮɢɤɚɬɨɪɁɧɚɱɟɧɢɟɋɦɵɫɥ ɢɫɩɨɥɶɡɭɟɦɵɯ ɜɟɤɬɨɪɨɜ ɜ ɡɚɩɪɨɫɟ ɧɚ tbColor 1 ɐɜɟɬ ɩɪɢɦɟɪɚ ɨɬɤɪɵɬɢɟ ɫɟɚɧɫɚ ɢ ɩɪɢ ɭɤɚɡɚɧɢɢ tbInput 2 ȼɯɨɞɧɨɣ ɫɢɝɧɚɥ ɬɢɩɚ ɜɟɤɬɨɪɚ ɜ ɡɚɩɪɨɫɚɯ ɧɚ ɩɨɥɭɱɟtbPrepared 3 ɉɪɟɞɨɛɪɚɛɨɬɚɧɧɵɟ ɞɚɧɧɵɟ ɧɢɟ ɢɥɢ ɡɚɧɟɫɟɧɢɟ ɞɚɧɧɵɯ. tbAnswers 4 ɉɪɚɜɢɥɶɧɵɟ ɨɬɜɟɬɵ tbReliability 5 Ⱦɨɫɬɨɜɟɪɧɨɫɬɶ ɨɬɜɟɬɚ 3.2.1.1 ȻɇɎ ɹɡɵɤɚ ɨɩɢɫɚɧɢɹ tbCalcAnswers 6 ɉɨɥɭɱɟɧɧɵɟ ɨɬɜɟɬɵ ɡɚɞɚɱɧɢɤɚ tbCalcReliability 7 ɍɜɟɪɟɧɧɨɫɬɶ ɜ ɨɬɜɟɬɟ Ɉɛɨɡɧɚɱɟɧɢɹ, ɩɪɢɧɹɬɵɟ ɜ tbWeight 8 ȼɟɫ ɩɪɢɦɟɪɚ ɞɚɧɧɨɦ ɪɚɫɲɢɪɟɧɢɢ ȻɇɎ ɢ ɨɩɢɫɚtbEstimation 9 Ɉɰɟɧɤɢ ɧɢɟ ɪɹɞɚ ɤɨɧɫɬɪɭɤɰɢɣ ɩɪɢɜɟɞɟɧɵ ɜ tbComment 10 Ʉɨɦɦɟɧɬɚɪɢɢ ɝɥɚɜɟ «Ɉɛɳɢɣ ɫɬɚɧɞɚɪɬ» ɜ ɪɚɡɞɟɥɟ «Ɉɩɢɫɚɧɢɟ ɹɡɵɤɚ ɨɩɢɫɚɧɢɹ ɤɨɦɩɨɧɟɧɬɨɜ». <Ɉɩɢɫɚɧɢɟ ɡɚɞɚɱɧɢɤɚ> ::= <Ɂɚɝɨɥɨɜɨɤ ɡɚɞɚɱɧɢɤɚ> <Ɉɩɢɫɚɧɢɟ ɫɬɪɭɤɬɭɪɵ ɡɚɞɚɱɧɢɤɚ> <Ɉɩɢɫɚɧɢɟ ɢɫɬɨɱɧɢɤɚ ɞɚɧɧɵɯ> <Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɡɚɞɚɱɧɢɤɚ> <Ɂɚɝɨɥɨɜɨɤ ɡɚɞɚɱɧɢɤɚ> ::= TaskBook <ɂɦɹ ɡɚɞɚɱɧɢɤɚ> <ɂɦɹ ɡɚɞɚɱɧɢɤɚ> ::= <ɂɞɟɧɬɢɮɢɤɚɬɨɪ> <Ɉɩɢɫɚɧɢɟ ɫɬɪɭɤɬɭɪɵ ɡɚɞɚɱɧɢɤɚ> ::= <Ɂɚɝɨɥɨɜɨɤ ɨɩɢɫɚɧɢɹ ɫɬɪɭɤɬɭɪɵ> <Ɉɩɢɫɚɧɢɟ ɩɨɥɟɣ> <Ɉɩɢɫɚɧɢɟ ɰɜɟɬɚ><Ɉɩɢɫɚɧɢɟ ɜɟɫɚ> <Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɫɬɪɭɤɬɭɪɵ>
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<Ɂɚɝɨɥɨɜɨɤ ɨɩɢɫɚɧɢɹ ɫɬɪɭɤɬɭɪɵ> ::= Structure <Ɉɩɢɫɚɧɢɟ ɰɜɟɬɚ> ::= Field <ɂɦɹ ɩɨɥɹ ɰɜɟɬ> tbColor Color End Field <ɂɦɹ ɩɨɥɹ ɰɜɟɬ> ::= <Ʉɨɧɫɬɚɧɬɚ ɬɢɩɚ String> <Ɉɩɢɫɚɧɢɟ ɜɟɫɚ> ::= Field <ɂɦɹ ɩɨɥɹ ɜɟɫ> tbWeight Real End Field <ɂɦɹ ɩɨɥɹ ɜɟɫ> ::= <Ʉɨɧɫɬɚɧɬɚ ɬɢɩɚ String> <Ɉɩɢɫɚɧɢɟ ɩɨɥɟɣ> ::= <Ɉɩɢɫɚɧɢɟ ɩɨɥɹ> [<Ɉɩɢɫɚɧɢɟ ɩɨɥɟɣ>] <Ɉɩɢɫɚɧɢɟ ɩɨɥɹ> ::= Field <ɂɦɹ ɩɨɥɹ> <Ɍɢɩ ɜɟɤɬɨɪɚ> {<Ɉɩɢɫɚɧɢɟ ɰɟɥɨɝɨ ɩɨɥɹ> ½ <Ɉɩɢɫɚɧɢɟ ɞɟɣɫɬɜɢɬɟɥɶɧɨɝɨ ɩɨɥɹ> ½ <Ɉɩɢɫɚɧɢɟ ɩɟɪɟɱɢɫɥɢɦɨɝɨ ɩɨɥɹ> ½ <Ɉɩɢɫɚɧɢɟ ɩɨɥɹ ɪɢɫɭɧɤɚ> ½ <Ɉɩɢɫɚɧɢɟ ɬɟɤɫɬɨɜɨɝɨ ɩɨɥɹ>} End Field <ɂɦɹ ɩɨɥɹ> ::= <Ʉɨɧɫɬɚɧɬɚ ɬɢɩɚ String> <Ɍɢɩ ɜɟɤɬɨɪɚ> ::= {tbInput ½ tbAnswers ½ tbReliability ½ tbCalcAnswers ½ tbCalcReliability ½ tbEstimation} <Ɉɩɢɫɚɧɢɟ ɰɟɥɨɝɨ ɩɨɥɹ> ::= {Long ½ Integer} <Ɉɩɢɫɚɧɢɟ ɞɟɣɫɬɜɢɬɟɥɶɧɨɝɨ ɩɨɥɹ> ::= Real <Ɉɩɢɫɚɧɢɟ ɩɟɪɟɱɢɫɥɢɦɨɝɨ ɩɨɥɹ> ::= Enumerated <ɋɩɢɫɨɤ ɢɦɟɧ ɡɧɚɱɟɧɢɣ> ; <ɋɩɢɫɨɤ ɢɦɟɧ ɡɧɚɱɟɧɢɣ> ::= <ɂɦɹ ɡɧɚɱɟɧɢɹ> [, <ɋɩɢɫɨɤ ɢɦɟɧ ɡɧɚɱɟɧɢɣ>] <ɂɦɹ ɡɧɚɱɟɧɢɹ> ::= <Ʉɨɧɫɬɚɧɬɚ ɬɢɩɚ String> <Ɉɩɢɫɚɧɢɟ ɬɟɤɫɬɨɜɨɝɨ ɩɨɥɹ> ::= String <Ɇɚɤɫɢɦɚɥɶɧɚɹ ɞɥɢɧɚ ɫɬɪɨɤɢ> <Ɇɚɤɫɢɦɚɥɶɧɚɹ ɞɥɢɧɚ ɫɬɪɨɤɢ> ::= <Ʉɨɧɫɬɚɧɬɚ ɬɢɩɚ Integer> <Ɉɩɢɫɚɧɢɟ ɩɨɥɹ ɪɢɫɭɧɤɚ> ::= Picture <Ɋɚɡɦɟɪ ɩɚɦɹɬɢ ɞɥɹ ɪɢɫɭɧɤɚ> <Ɋɚɡɦɟɪ ɩɚɦɹɬɢ ɞɥɹ ɪɢɫɭɧɤɚ> ::= <Ʉɨɧɫɬɚɧɬɚ ɬɢɩɚ Long> <Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɫɬɪɭɤɬɭɪɵ> ::= End Structure <Ɉɩɢɫɚɧɢɟ ɢɫɬɨɱɧɢɤɚ ɞɚɧɧɵɯ> ::= Source {<ȼɧɟɲɧɢɣ ɢɫɬɨɱɧɢɤ> ½ <ɉɨɞɝɨɬɨɜɥɟɧɨ ɜ ɡɚɞɚɱɧɢɤɟ>} <ȼɧɟɲɧɢɣ ɢɫɬɨɱɧɢɤ> ::= <ɂɦɹ ɩɪɢɥɨɠɟɧɢɹ, ɤɨɬɨɪɨɦɭ ɧɭɠɧɨ ɩɟɪɟɞɚɬɶ ɡɚɩɪɨɫ> <SQL – ɡɚɩɪɨɫ> <ɂɦɹ ɩɪɢɥɨɠɟɧɢɹ, ɤɨɬɨɪɨɦɭ ɧɭɠɧɨ ɩɟɪɟɞɚɬɶ ɡɚɩɪɨɫ> ::= <Ʉɨɧɫɬɚɧɬɚ ɬɢɩɚ String> <SQL – ɡɚɩɪɨɫ> ::= <Ʉɨɧɫɬɚɧɬɚ ɬɢɩɚ String> <ɉɨɞɝɨɬɨɜɥɟɧɨ ɜ ɡɚɞɚɱɧɢɤɟ> – ȼ ɫɨɨɬɜɟɬɫɬɜɢɢ ɫ ɩɨɪɹɞɤɨɦ ɨɩɢɫɚɧɢɹ ɩɨɥɟɣ ɜɵɜɨɞɹɬɫɹ ɫɢɦɜɨɥɶɧɵɟ ɩɪɟɞɫɬɚɜɥɟɧɢɹ ɩɨɥɟɣ, ɪɚɡɞɟɥɟɧɧɵɟ ɫɢɦɜɨɥɨɦ ɬɚɛɭɥɹɰɢɢ (ɛɚɣɬɨɦ ɫɨɞɟɪɠɚɳɢɦ ɤɨɞ 9). ɉɪɢɦɟɪɵ (ɜ ɬɟɪɦɢɧɨɥɨɝɢɢ ɛɚɡ ɞɚɧɧɵɯ – ɡɚɩɢɫɢ) ɪɚɡɞɟɥɹɸɬɫɹ ɫɢɦɜɨɥɨɦ ɤɨɧɰɚ ɚɛɡɚɰɚ (ɩɟɪɟɜɨɞɨɦ ɫɬɪɨɤɢ – ɛɚɣɬɨɦ, ɫɨɞɟɪɠɚɳɢɦ ɤɨɞ 13). ɉɨɥɹ ɪɢɫɭɧɤɢ ɜɵɜɨɞɹɬɫɹ ɜ ɜɢɞɟ ɩɨɫɥɟɞɨɜɚɬɟɥɶɧɨɫɬɢ <Ɋɚɡɦɟɪ ɩɚɦɹɬɢ ɞɥɹ ɪɢɫɭɧɤɚ> ɰɟɥɵɯ ɱɢɫɟɥ, ɪɚɡɞɟɥɟɧɧɵɯ ɩɪɨɛɟɥɚɦɢ, ɤɚɠɞɨɟ ɢɡ ɤɨɬɨɪɵɯ ɹɜɥɹɟɬɫɹ ɞɟɫɹɬɢɱɧɵɦ ɩɪɟɞɫɬɚɜɥɟɧɢɟɦ ɱɢɫɥɚ (ɨɬ 0 ɞɨ 255), ɫɨɞɟɪɠɚɳɟɝɨɫɹ ɜ ɫɨɨɬɜɟɬɫɬɜɭɸɳɟɦ ɛɚɣɬɟ ɨɛɥɚɫɬɢ ɩɚɦɹɬɢ, ɨɬɜɟɞɟɧɧɨɣ ɞɥɹ ɯɪɚɧɟɧɢɹ ɪɢɫɭɧɤɚ. <Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɡɚɞɚɱɧɢɤɚ> ::= End TaskBook
3.2.1.2 Ɉɩɢɫɚɧɢɟ ɹɡɵɤɚ ɨɩɢɫɚɧɢɹ ɡɚɞɚɱɧɢɤɚ ȼ ɷɬɨɦ ɪɚɡɞɟɥɟ ɩɪɢɜɟɞɟɧɨ ɩɨɞɪɨɛɧɨɟ ɨɩɢɫɚɧɢɟ ɞɨɩɨɥɧɢɬɟɥɶɧɨɣ ɢɧɮɨɪɦɚɰɢɢ (ɢɧɮɨɪɦɚɰɢɢ, ɫɥɟɞɭɸɳɟɣ ɡɚ ɬɢɩɨɦ ɞɚɧɧɵɯ ɩɨɥɹ) ɞɥɹ ɩɨɥɟɣ, ɜ ɛɥɨɤɚɯ ɨɩɢɫɚɧɢɹ ɤɨɬɨɪɵɯ ɨɧɚ ɢɫɩɨɥɶɡɭɟɬɫɹ. ɉɟɪɟɱɢɫɥɢɦɵɣ ɬɢɩ ɩɨɥɹ. ɉɪɢ ɢɫɩɨɥɶɡɨɜɚɧɢɢ ɩɟɪɟɱɢɫɥɢɦɨɝɨ ɬɢɩɚ ɩɨɥɹ ɜ ɜɟɤɬɨɪɚɯ ɞɚɧɧɵɯ ɯɪɚɧɹɬɫɹ ɧɟ ɫɚɦɢ ɡɧɚɱɟɧɢɹ, ɚ ɢɯ ɧɨɦɟɪɚ. Ⱦɥɹ ɨɬɨɛɪɚɠɟɧɢɹ ɜ ɪɟɞɚɤɬɨɪɟ ɡɚɞɚɱɧɢɤɚ ɡɧɚɱɟɧɢɣ ɩɨɥɟɣ ɢɯ ɧɟɨɛɯɨɞɢɦɨ ɛɪɚɬɶ ɢɡ ɛɥɨɤɚ ɨɩɢɫɚɧɢɹ ɩɨɥɹ. ȼ ɫɩɢɫɤɟ ɢɦɟɧ ɡɧɚɱɟɧɢɣ ɛɥɨɤɚ ɨɩɢɫɚɧɢɹ ɩɟɪɟɱɢɫɥɢɦɨɝɨ ɩɨɥɹ ɯɪɚɧɹɬɫɹ ɫɢɦɜɨɥɶɧɵɟ ɤɨɧɫɬɚɧɬɵ, ɩɟɪɜɚɹ ɢɡ ɤɨɬɨɪɵɯ ɫɨɞɟɪɠɢɬ ɧɚɡɜɚɧɢɟ ɫɨɫɬɨɹɧɢɹ, ɫɨɨɬɜɟɬɫɬɜɭɸɳɟɟ ɧɟɨɩɪɟɞɟɥɟɧɧɨɦɭ ɡɧɚɱɟɧɢɸ ɩɨɥɹ; ɜɬɨɪɚɹ – ɩɟɪɜɨɦɭ ɢɡ ɡɧɚɱɟɧɢɣ, ɤɨɬɨɪɵɟ ɦɨɠɟɬ ɩɪɢɧɢɦɚɬɶ ɩɨɥɟ, ɢ ɬ.ɞ. ɋɬɪɨɤɚ. ɉɨɥɹ ɬɢɩɚ ɫɬɪɨɤɚ ɩɪɟɞɧɚɡɧɚɱɟɧɵ ɞɥɹ ɯɪɚɧɟɧɢɹ ɫɢɦɜɨɥɶɧɵɯ ɫɬɪɨɤ Ɍɚɛɥɢɰɚ 3 ɮɢɤɫɢɪɨɜɚɧɧɨɣ ɞɥɢɧɵ. Ⱦɥɢɧɚ ɫɬɪɨɤɢ ɡɚɞɚɁɧɚɱɟɧɢɟ ɩɟɪɜɵɯ ɫɟɦɢ ɛɚɣɬ ɩɨɥɹ ɬɢɩɚ ɪɢɫɭɧɨɤ ɟɬɫɹ ɡɧɚɱɟɧɢɟɦ ɩɚɪɚɦɟɬɪɚ <Ɇɚɤɫɢɦɚɥɶɧɚɹ ȼɟɥɢɱɢɧɚɁɧɚɱɟɧɢɟ ɞɥɢɧɚ ɫɬɪɨɤɢ>. Ȼ2*256+Ȼ1 ɉɨɥɨɠɢɬɟɥɶɧɨɟ ɰɟɥɨɟ ɱɢɫɥɨ, Ɋɢɫɭɧɨɤ. ɉɨɥɹ ɬɢɩɚ ɪɢɫɭɧɨɤ ɩɪɟɞɡɚɞɚɸɳɟɟ ɪɚɡɦɟɪ ɪɢɫɭɧɤɚ ɩɨ ɧɚɡɧɚɱɟɧɵ ɞɥɹ ɯɪɚɧɟɧɢɹ ɝɪɚɮɢɱɟɫɤɨɣ ɢɧɝɨɪɢɡɨɧɬɚɥɢ ɜ ɩɢɤɫɟɥɹɯ. ɮɨɪɦɚɰɢɢ. ɉɟɪɜɵɟ ɫɟɦɶ ɛɚɣɬ ɩɨɥɹ ɢɦɟɸɬ Ȼ4*256+Ȼ3 ɉɨɥɨɠɢɬɟɥɶɧɨɟ ɰɟɥɨɟ ɱɢɫɥɨ, ɫɦɵɫɥ, ɩɪɢɜɟɞɟɧɧɵɣ ɜ ɬɚɛɥ. 3. ȼ ɬɚɛɥɢɰɟ ɡɚɞɚɸɳɟɟ ɪɚɡɦɟɪ ɪɢɫɭɧɤɚ ɩɨ ɩɪɢɧɹɬɨ ɨɛɨɡɧɚɱɟɧɢɟ Ȼ1 – ɜɟɥɢɱɢɧɚ, ɯɪɚɜɟɪɬɢɤɚɥɢ ɜ ɩɢɤɫɟɥɹɯ. ɧɹɳɚɹɫɹ ɜ ɩɟɪɜɨɦ ɛɚɣɬɟ, Ȼ2 – ɜɨ ɜɬɨɪɨɦ ɢ (Ȼ7*256+Ȼ6)*256+Ȼ5 ɑɢɫɥɨ ɰɜɟɬɨɜ, ɢɫɩɨɥɶɡɨɜɚɧɧɵɯ ɬ.ɞ. Ɋɢɫɭɧɨɤ ɪɚɡɜɨɪɚɱɢɜɚɟɬɫɹ ɩɨ ɫɬɪɨɤɚɦ, ɜ ɪɢɫɭɧɤɟ ɧɚɱɢɧɚɹ ɫ ɥɟɜɨɝɨ ɜɟɪɯɧɟɝɨ ɭɝɥɚ, ɜ ɧɟɩɪɟɪɵɜɧɵɣ ɦɚɫɫɢɜ, ɪɚɡɦɟɪɨɦ (Ȼ2*256+Ȼ1)(Ȼ4*256+Ȼ3).
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ȿɫɥɢ ɱɢɫɥɨ ɰɜɟɬɨɜ ɪɚɜɧɨ ɟɞɢɧɢɰɟ (ɱɟɪɧɨ-ɛɟɥɨɟ ɢɡɨɛɪɚɠɟɧɢɟ), ɬɨ ɤɚɠɞɵɣ ɫɥɟɞɭɸɳɢɣ ɛɚɣɬ ɫɨɞɟɪɠɢɬ ɜɨɫɟɦɶ ɩɢɤɫɟɥɟɣ ɢɡɨɛɪɚɠɟɧɢɹ. ɋɚɦɵɣ ɦɥɚɞɲɢɣ ɛɢɬ ɜɨɫɶɦɨɝɨ ɛɚɣɬɚ ɫɨɨɬɜɟɬɫɬɜɭɟɬ ɥɟɜɨɦɭ ɜɟɪɯɧɟɦɭ ɩɢɤɫɟɥɸ ɪɢɫɭɧɤɚ. ȿɫɥɢ ɱɢɫɥɨ ɰɜɟɬɨɜ ɪɚɜɧɨ ɬɪɟɦ, ɬɨ ɤɚɠɞɵɣ ɛɚɣɬ, ɧɚɱɢɧɚɹ ɫ ɜɨɫɶɦɨɝɨ, ɫɨɞɟɪɠɢɬ ɢɧɮɨɪɦɚɰɢɸ ɨ ɱɟɬɵɪɟɯ ɩɢɤɫɟɥɹɯ. Ɇɥɚɞɲɢɟ ɞɜɚ ɛɢɬɚ ɡɚɞɚɸɬ ɥɟɜɵɣ ɜɟɪɯɧɢɣ ɩɢɤɫɟɥɶ ɪɢɫɭɧɤɚ. ȿɫɥɢ ɱɢɫɥɨ ɰɜɟɬɨɜ ɨɬ 4 ɞɨ 15, ɬɨ ɤɚɠɞɵɣ ɛɚɣɬ, ɧɚɱɢɧɚɹ ɫ ɜɨɫɶɦɨɝɨ, ɫɨɞɟɪɠɢɬ ɢɧɮɨɪɦɚɰɢɸ ɨ ɞɜɭɯ ɩɢɤɫɟɥɹɯ. Ɇɥɚɞɲɢɟ ɱɟɬɵɪɟ ɛɢɬɚ ɡɚɞɚɸɬ ɥɟɜɵɣ ɜɟɪɯɧɢɣ ɩɢɤɫɟɥɶ ɪɢɫɭɧɤɚ. ȿɫɥɢ ɱɢɫɥɨ ɰɜɟɬɨɜ ɨɬ 16 ɞɨ 255, ɬɨ ɤɚɠɞɵɣ ɛɚɣɬ, ɧɚɱɢɧɚɹ ɫ ɜɨɫɶɦɨɝɨ, ɫɨɞɟɪɠɢɬ ɢɧɮɨɪɦɚɰɢɸ ɨɛ ɨɞɧɨɦ ɩɢɤɫɟɥɟ. Ɂɧɚɱɟɧɢɟ ɜ ɜɨɫɶɦɨɦ ɛɚɣɬɟ ɫɨɨɬɜɟɬɫɬɜɭɟɬ ɥɟɜɨɦɭ ɜɟɪɯɧɟɦɭ ɩɢɤɫɟɥɸ ɪɢɫɭɧɤɚ. ɉɪɢ ɱɢɫɥɟ ɰɜɟɬɨɜ ɨɬ 256 ɞɨ 65535 ɤɚɠɞɵɟ ɞɜɚ ɛɚɣɬɚ, ɧɚɱɢɧɚɹ ɫ ɜɨɫɶɦɨɝɨ, ɫɨɞɟɪɠɚɬ ɢɧɮɨɪɦɚɰɢɸ ɨɛ ɨɞɧɨɦ ɩɢɤɫɟɥɟ (ɩɟɪɜɵɣ ɩɢɤɫɟɥɶ ɢɦɟɟɬ ɰɜɟɬ ɧɨɦɟɪ Ȼ9*256+Ȼ8). Ɂɧɚɱɟɧɢɟ ɜ ɜɨɫɶɦɨɦ ɢ ɞɟɜɹɬɨɦ ɛɚɣɬɚɯ ɫɨɨɬɜɟɬɫɬɜɭɟɬ ɥɟɜɨɦɭ ɜɟɪɯɧɟɦɭ ɩɢɤɫɟɥɸ ɪɢɫɭɧɤɚ. ɉɪɢ ɱɢɫɥɟ ɰɜɟɬɨɜ ɨɬ 65535 ɞɨ 16777215 ɤɚɠɞɵɟ ɬɪɢ ɛɚɣɬɚ, ɧɚɱɢɧɚɹ ɫ ɜɨɫɶɦɨɝɨ, ɫɨɞɟɪɠɚɬ ɢɧɮɨɪɦɚɰɢɸ ɨɛ ɨɞɧɨɦ ɩɢɤɫɟɥɟ (ɩɟɪɜɵɣ ɩɢɤɫɟɥɶ ɢɦɟɟɬ ɰɜɟɬ ɧɨɦɟɪ (Ȼ10*256+Ȼ9)*256+Ȼ8). Ɂɧɚɱɟɧɢɟ ɜ ɜɨɫɶɦɨɦ, ɞɟɜɹɬɨɦ ɢ ɞɟɫɹɬɨɦ ɛɚɣɬɚɯ ɫɨɨɬɜɟɬɫɬɜɭɟɬ ɥɟɜɨɦɭ ɜɟɪɯɧɟɦɭ ɩɢɤɫɟɥɸ ɪɢɫɭɧɤɚ. Ⱥɥɶɬɟɪɧɚɬɢɜɧɵɣ ɫɩɨɫɨɛ ɡɚɩɢɫɢ ɪɢɫɭɧɤɚ. ɉɪɟɞɥɨɠɟɧɧɵɣ ɜɵɲɟ ɫɩɨɫɨɛ ɯɨɪɨɲ ɫɜɨɟɣ ɩɪɨɫɬɨɬɨɣ ɢ ɩɥɨɯ ɛɨɥɶɲɢɦ ɨɛɴɟɦɨɦ ɞɚɧɧɵɯ. Ȼɨɥɶɲɢɧɫɬɜɨ ɝɪɚɮɢɱɟɫɤɢɯ ɮɨɪɦɚɬɨɜ ɮɚɣɥɨɜ (ɧɚɩɪɢɦɟɪ GIF) ɨɛɟɫɩɟɱɢɜɚɸɬ ɜɵɫɨɤɭɸ ɫɬɟɩɟɧɶ ɤɨɦɩɪɟɫɫɢɢ ɝɪɚɮɢɱɟɫɤɨɣ ɢɧɮɨɪɦɚɰɢɢ. Ⱦɥɹ ɢɫɩɨɥɶɡɨɜɚɧɢɹ ɷɬɨɣ ɜɨɡɦɨɠɧɨɫɬɢ ɩɪɢ ɡɚɩɢɫɢ ɩɨɥɹ ɝɪɚɮɢɱɟɫɤɨɣ ɢɧɮɨɪɦɚɰɢɢ ɧɚ ɞɢɫɤ ɩɪɟɞɥɚɝɚɟɬɫɹ ɚɥɶɬɟɪɧɚɬɢɜɧɵɣ ɮɨɪɦɚɬ. ȼ ɷɬɨɦ ɮɨɪɦɚɬɟ ɩɟɪɜɵɟ ɞɜɚ ɛɚɣɬɚ ɞɨɥɠɧɵ ɛɵɬɶ ɧɭɥɟɜɵɦɢ. ɉɨɫɤɨɥɶɤɭ ɜ ɨɫɧɨɜɧɨɦ ɮɨɪɦɚɬɟ ɡɚɩɢɫɢ ɪɢɫɭɧɤɨɜ ɷɬɢ ɞɜɚ ɛɚɣɬɚ ɮɨɪɦɢɪɨɜɚɥɢ ɪɚɡɦɟɪ ɪɢɫɭɧɤɚ ɩɨ ɝɨɪɢɡɨɧɬɚɥɢ, ɧɭɥɟɜɚɹ ɲɢɪɢɧɚ ɪɢɫɭɧɤɚ ɫɥɭɠɢɬ ɩɪɢɡɧɚɤɨɦ ɢɫɩɨɥɶɡɨɜɚɧɢɹ ɚɥɶɬɟɪɧɚɬɢɜɧɨɝɨ ɮɨɪɦɚɬɚ. ɋɥɟɞɭɸɳɢɟ ɩɹɬɶ ɛɚɣɬ ɯɪɚɧɹɬ ASCII ɤɨɞɵ ɬɢɩɚ ɝɪɚɮɢɱɟɫɤɨɝɨ ɮɨɪɦɚɬɚ. ɂɫɩɨɥɶɡɭɸɬɫɹ ɤɨɞɵ ɡɚɝɥɚɜɧɵɯ ɛɭɤɜ ɥɚɬɢɧɫɤɨɝɨ ɚɥɮɚɜɢɬɚ. ȿɫɥɢ ɬɢɩ ɝɪɚɮɢɱɟɫɤɨɝɨ ɫɬɚɧɞɚɪɬɚ ɫɨɞɟɪɠɢɬ ɦɟɧɟɟ ɩɹɬɢ ɫɢɦɜɨɥɨɜ (PCX, GIF), ɬɨ ɬɢɩ ɞɨɩɨɥɧɹɟɬɫɹ ɫɢɦɜɨɥɚɦɢ ɩɪɨɛɟɥɚ ɫɩɪɚɜɚ. ɋɥɟɞɭɸɳɢɟ ɱɟɬɵɪɟ ɛɚɣɬɚ, ɫ ɜɨɫɶɦɨɝɨ ɩɨ ɨɞɢɧɧɚɞɰɚɬɵɣ, ɫɨɞɟɪɠɚɬ ɱɢɫɥɨ ɛɚɣɬ ɜ ɝɪɚɮɢɱɟɫɤɨɦ ɮɚɣɥɟ. ɇɚɱɢɧɚɹ ɫ ɞɜɟɧɚɞɰɚɬɨɝɨ ɛɚɣɬɚ, ɢɞɟɬ ɢɧɮɨɪɦɚɰɢɹ, ɫɨɞɟɪɠɚɜɲɚɹɫɹ ɜ ɝɪɚɮɢɱɟɫɤɨɦ ɮɚɣɥɟ. ɋɨɩɨɫɬɚɜɥɟɧɢɟ ɷɥɟɦɟɧɬɨɜ ɜɟɤɬɨɪɨɜ ɩɪɚɜɢɥɶɧɵɯ ɨɬɜɟɬɨɜ, ɞɨɫɬɨɜɟɪɧɨɫɬɢ, ɜɵɱɢɫɥɟɧɧɵɯ ɨɬɜɟɬɨɜ, ɭɜɟɪɟɧɧɨɫɬɢ ɢ ɨɰɟɧɤɢ ɩɪɨɢɡɜɨɞɢɬɫɹ ɩɨ ɫɥɟɞɭɸɳɟɦɭ ɩɪɚɜɢɥɭ. ɉɟɪɜɨɦɭ ɩɨɥɸ ɬɢɩɚ ɩɪɚɜɢɥɶɧɵɣ ɨɬɜɟɬ ɫɨɨɬɜɟɬɫɬɜɭɸɬ ɩɟɪɜɨɟ ɩɨɥɟ ɬɢɩɚ ɞɨɫɬɨɜɟɪɧɨɫɬɶ, ɩɟɪɜɨɟ ɩɨɥɟ ɬɢɩɚ ɜɵɱɢɫɥɟɧɧɵɣ ɨɬɜɟɬ, ɩɟɪɜɨɟ ɩɨɥɟ ɬɢɩɚ ɭɜɟɪɟɧɧɨɫɬɶ, ɩɟɪɜɨɟ ɩɨɥɟ ɬɢɩɚ ɨɰɟɧɤɚ, ɜɬɨɪɨɦɭ – ɜɬɨɪɵɟ ɢ ɬ.ɞ.
3.2.1.3 ɇɟɨɩɪɟɞɟɥɟɧɧɵɟ ɡɧɚɱɟɧɢɹ ȼ ɩɪɚɤɬɢɤɟ ɪɚɛɨɬɵ ɛɨɥɶɲɢɧɫɬɜɨ ɬɚɛɥɢɰ ɞɚɧɧɵɯ ɧɟ ɩɨɥɧɵ. Ɍɨ ɟɫɬɶ, ɱɚɫɬɶ ɞɚɧɧɵɯ ɜ ɩɪɢɦɟɪɚɯ ɡɚɞɚɱɧɢɤɚ ɧɟ ɢɡɜɟɫɬɧɚ. Ɂɚɞɚɱɧɢɤ ɞɨɥɠɟɧ ɨɞɧɨɡɧɚɱɧɨ ɭɤɚɡɚɬɶ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɭ ɧɟɢɡɜɟɫɬɧɵɟ ɞɚɧɧɵɟ. Ⱦɥɹ ɷɬɢɯ ɰɟɥɟɣ ɞɥɹ ɤɚɠɞɨɝɨ ɬɢɩɚ ɜɯɨɞɧɵɯ ɞɚɧɧɵɯ ɨɩɪɟɞɟɥɟɧɨ ɫɩɟɰɢɚɥɶɧɨɟ ɡɧɚɱɟɧɢɟ - ɧɟɨɩɪɟɞɟɥɟɧɧɨɟ. Ⱦɥɹ ɩɟɪɟɞɚɱɢ ɧɟɢɡɜɟɫɬɧɵɯ ɡɧɚɱɟɧɢɣ ɢɫɩɨɥɶɡɭɸɬɫɹ ɫɥɟɞɭɸɳɢɟ ɜɟɥɢɱɢɧɵ: 10-40 ɞɥɹ ɞɟɣɫɬɜɢɬɟɥɶɧɵɯ ɱɢɫɟɥ ɢ 0 ɞɥɹ ɜɫɟɯ ɬɢɩɨɜ ɤɚɱɟɫɬɜɟɧɧɵɯ ɩɪɢɡɧɚɤɨɜ.
3.2.1.4 ɉɪɢɦɟɪ ɨɩɢɫɚɧɢɹ ɡɚɞɚɱɧɢɤɚ ȼ ɷɬɨɦ ɪɚɡɞɟɥɟ ɩɪɢɜɟɞɟɧɨ ɨɩɢɫɚɧɢɹ ɩɪɨɫɬɨɝɨ ɡɚɞɚɱɧɢɤɚ ɞɥɹ ɩɪɨɝɧɨɡɢɪɨɜɚɧɢɹ ɤɭɪɫɚ ɚɦɟɪɢɤɚɧɫɤɨɝɨ ɞɨɥɥɚɪɚ ɤ ɪɭɛɥɸ. Ɂɚɞɚɱɧɢɤ ɫɨɞɟɪɠɢɬ ɬɪɢ ɩɪɢɦɟɪɚ. ȼ ɪɚɡɞɟɥɟ ɨɩɢɫɚɧɢɹ ɞɚɧɧɵɯ ɜɦɟɫɬɨ ɫɢɦɜɨɥɚ ɬɚɛɭɥɹɰɢɢ ɢɫɩɨɥɶɡɨɜɚɧ ɫɢɦɜɨɥ «®», ɚ ɜɦɟɫɬɨ ɫɢɦɜɨɥɚ ɤɨɧɰɚ ɚɛɡɚɰɚ – «¿». TaskBook CursValuty Structure Field "ɐɜɟɬ" tbColor Color End Field Field "ȼɟɫ" tbWeight Real End Field Field "Ⱦɚɬɚ" tbComment Real End Field Field "Ɍɟɤɭɳɢɣ ɤɭɪɫ" tbInput Real End Field Field "Ʉɭɪɫ ɧɚ ɫɥɟɞɭɸɳɢɣ ɞɟɧɶ" tbAnswers Real End Field Field "Ⱦɨɫɬɨɜɟɪɧɨɫɬɶ ɤɭɪɫɚ" tbReliability Real End Field Field "ɉɪɟɞɫɤɚɡɚɧɧɵɣ ɤɭɪɫ" tbCalcAnswers Real End Field Field "ɇɚɞɟɠɧɨɫɬɶ ɩɪɟɞɫɤɚɡɚɧɢɹ" tbCalcReliability Real End Field Field "Ɉɰɟɧɤɚ ɩɪɟɞɫɤɚɡɚɧɢɹ" tbEstimation Real End Field End Structure Source HFFFF®1.0®01.01.97®5773®5774®1.0®5775®0.1®0.07¿ HFFFF®1.0®02.01.97®5774®5776®1.0®5777®0.01®0.7¿ HFFFF®1.0®03.01.97®5776®5778®1.0®5779®0.2®0.007¿ End TaskBook
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3.3 ɋɬɚɧɞɚɪɬ ɜɬɨɪɨɝɨ ɭɪɨɜɧɹ ɤɨɦɩɨɧɟɧɬɚ ɡɚɞɚɱɧɢɤ ȼ ɷɬɨɦ ɪɚɡɞɟɥɟ ɨɩɢɫɚɧɵ ɜɫɟ ɡɚɩɪɨɫɵ ɤɨɦɩɨɧɟɧɬɚ ɡɚɞɚɱɧɢɤ ɜ ɜɢɞɟ ɩɪɨɰɟɞɭɪ ɢ ɮɭɧɤɰɢɣ. ɉɪɢ ɨɩɢɫɚɧɢɢ ɢɫɩɨɥɶɡɭɟɬɫɹ ɫɢɧɬɚɤɫɢɫ ɹɡɵɤɨɜ Turbo Pascal ɢ ɋ. ȼ ɉɚɫɤɚɥɶ ɜɚɪɢɚɧɬɟ ɩɪɢɜɟɞɟɧɵ ɡɚɝɨɥɨɜɤɢ ɮɭɧɤɰɢɣ ɢ ɩɪɨɰɟɞɭɪ. ȼ ɋ ɜɚɪɢɚɧɬɟ – ɩɪɨɬɨɬɢɩɵ ɮɭɧɤɰɢɣ. Ȼɨɥɶɲɢɧɫɬɜɨ ɡɚɩɪɨɫɨɜ, ɪɟɚɥɢɡɭɟɬɫɹ ɜ ɜɢɞɟ ɮɭɧɤɰɢɣ, ɫɨɨɛɳɚɸɳɢɯ ɨ ɤɨɪɪɟɤɬɧɨɫɬɢ ɡɚɜɟɪɲɟɧɢɹ ɨɩɟɪɚɰɢɢ. ɉɪɟɞɩɨɥɚɝɚɟɬɫɹ ɜɨɡɦɨɠɧɨɫɬɶ ɨɞɧɨɜɪɟɦɟɧɧɨɣ ɪɚɛɨɬɵ ɧɟɫɤɨɥɶɤɢɯ ɫɟɚɧɫɨɜ ɨɞɧɨɝɨ ɡɚɞɚɱɧɢɤɚ. ɇɚɩɪɢɦɟɪ, ɞɨɩɭɫɤɚɟɬɫɹ ɪɟɞɚɤɬɢɪɨɜɚɧɢɟ ɡɚɞɚɱɧɢɤɚ ɢ ɨɞɧɨɜɪɟɦɟɧɧɨɟ ɨɛɭɱɟɧɢɟ ɫɟɬɢ ɩɨ ɬɨɦɭ ɠɟ ɡɚɞɚɱɧɢɤɭ. ȼɫɟ ɡɚɩɪɨɫɵ ɤ ɤɨɦɩɨɧɟɧɬɭ ɡɚɞɚɱɧɢɤ ɦɨɠɧɨ ɪɚɡɛɢɬɶ ɧɚ ɫɥɟɞɭɸɳɢɟ ɝɪɭɩɩɵ. 1. ɑɬɟɧɢɟ ɢ ɡɚɩɢɫɶ ɡɚɞɚɱɧɢɤɚ. 2. ɇɚɱɚɥɨ ɢ ɤɨɧɟɰ ɫɟɚɧɫɚ. 3. ɉɟɪɟɦɟɳɟɧɢɟ ɩɨ ɩɪɢɦɟɪɚɦ. 4. Ɉɩɪɟɞɟɥɟɧɢɟ, ɩɨɥɭɱɟɧɢɟ ɢ ɢɡɦɟɧɟɧɢɟ ɞɚɧɧɵɯ. 5. Ɉɤɪɚɫɤɚ ɩɪɢɦɟɪɨɜ. 6. ɍɫɬɚɧɨɜɥɟɧɢɟ ɫɬɪɭɤɬɭɪɵ Ɂɚɞɚɱɧɢɤɚ. 7. Ⱦɨɛɚɜɥɟɧɢɟ ɢ ɭɞɚɥɟɧɢɟ ɩɪɢɦɟɪɨɜ. 8. Ɉɛɪɚɛɨɬɤɚ ɨɲɢɛɨɤ.
3.3.1 ɑɬɟɧɢɟ ɢ ɡɚɩɢɫɶ ɡɚɞɚɱɧɢɤɚ Ʉ ɷɬɨɣ ɝɪɭɩɩɟ ɡɚɩɪɨɫɨɜ ɨɬɧɨɫɹɬɫɹ ɡɚɩɪɨɫɵ, ɪɚɛɨɬɚɸɳɢɟ ɫɨ ɜɫɟɦ ɡɚɞɚɱɧɢɤɨɦ ɜ ɰɟɥɨɦ. ɗɬɢ ɡɚɩɪɨɫɵ ɫɱɢɬɵɜɚɸɬ ɡɚɞɚɱɧɢɤ, ɫɨɯɪɚɧɹɸɬ ɡɚɞɚɱɧɢɤ ɧɚ ɞɢɫɤɟ ɢɥɢ ɜɵɝɪɭɠɚɸɬ ɪɚɧɟɟ ɫɱɢɬɚɧɧɵɣ ɢɥɢ ɫɨɡɞɚɧɧɵɣ ɡɚɞɚɱɧɢɤ.
3.3.1.1 ɉɪɨɱɢɬɚɬɶ ɡɚɞɚɱɧɢɤ (tbAdd) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function tbAdd( CompName : PString ) : Logic; C: Logic tbAdd( PString CompName ) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɚ: CompName – ɭɤɚɡɚɬɟɥɶ ɧɚ ɫɬɪɨɤɭ ɫɢɦɜɨɥɨɜ, ɫɨɞɟɪɠɚɳɭɸ ɢɦɹ ɮɚɣɥɚ ɡɚɞɚɱɧɢɤɚ. ɇɚɡɧɚɱɟɧɢɟ – ɫɥɭɠɢɬ ɞɥɹ ɫɱɢɬɵɜɚɧɢɹ ɡɚɞɚɱɧɢɤɚ.
1. 2.
3. 4.
Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. ȿɫɥɢ Error <> 0, ɬɨ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. ȿɫɥɢ ɜ ɞɚɧɧɵɣ ɦɨɦɟɧɬ ɫɱɢɬɚɧ ɡɚɞɚɱɧɢɤ, ɬɨ ɝɟɧɟɪɢɪɭɟɬɫɹ ɡɚɩɪɨɫ tbDelete. ȿɫɥɢ ɡɚɩɪɨɫ tbDelete ɡɚɜɟɪɲɚɟɬɫɹ ɧɟɭɫɩɟɲɧɨ, ɬɨ ɝɟɧɟɪɢɪɭɟɬɫɹ ɜɧɭɬɪɟɧɧɹɹ ɨɲɢɛɤɚ 104 – ɩɨɩɵɬɤɚ ɫɱɢɬɵɜɚɧɢɹ ɡɚɞɚɱɧɢɤɚ ɩɪɢ ɨɬɤɪɵɬɵɯ ɫɟɚɧɫɚɯ ɪɚɧɟɟ ɫɱɢɬɚɧɧɨɝɨ ɡɚɞɚɱɧɢɤɚ. ɍɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ. ȼɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. ɉɟɪɜɵɟ ɱɟɬɵɪɟ ɫɢɦɜɨɥɚ ɫɬɪɨɤɢ CompName ɫɨɫɬɚɜɥɹɸɬ ɫɥɨɜɨ File. Ɉɫɬɚɥɶɧɚɹ ɱɚɫɬɶ ɫɬɪɨɤɢ ɫɨɞɟɪɠɢɬ ɢɦɹ ɤɨɦɩɨɧɟɧɬɚ ɢ ɩɨɫɥɟ ɩɪɨɛɟɥɚ ɢɦɹ ɮɚɣɥɚ, ɫɨɞɟɪɠɚɳɟɝɨ ɤɨɦɩɨɧɟɧɬ. ȿɫɥɢ ɜɨ ɜɪɟɦɹ ɜɵɩɨɥɧɟɧɢɹ ɡɚɩɪɨɫɚ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ, ɬɨ ɝɟɧɟɪɢɪɭɟɬɫɹ ɜɧɭɬɪɟɧɧɹɹ ɨɲɢɛɤɚ 102 – ɨɲɢɛɤɚ ɱɬɟɧɢɹ ɡɚɞɚɱɧɢɤɚ. ɍɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ. ȼɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. ȼ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɭɫɩɟɲɧɨ ɡɚɜɟɪɲɚɟɬɫɹ.
3.3.1.2 Ɂɚɩɢɫɚɬɶ ɡɚɞɚɱɧɢɤ (tbWrite) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function tbWrite( CompName, FileName : PString) : Logic; C: Logic tbWrite(PString CompName, PString FileName) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: CompName – ɭɤɚɡɚɬɟɥɶ ɧɚ ɫɬɪɨɤɭ ɫɢɦɜɨɥɨɜ, ɫɨɞɟɪɠɚɳɭɸ ɢɦɹ ɡɚɞɚɱɧɢɤɚ. FileName – ɢɦɹ ɮɚɣɥɚ, ɤɭɞɚ ɧɚɞɨ ɡɚɩɢɫɚɬɶ ɤɨɦɩɨɧɟɧɬɚ.
1.
ɇɚɡɧɚɱɟɧɢɟ – ɫɨɯɪɚɧɹɟɬ ɡɚɞɚɱɧɢɤ ɜ ɮɚɣɥɟ. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. ȿɫɥɢ Error <> 0, ɬɨ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ.
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2.
3. 4.
ȿɫɥɢ ɜ ɦɨɦɟɧɬ ɩɨɥɭɱɟɧɢɹ ɡɚɩɪɨɫɚ ɨɬɫɭɬɫɬɜɭɟɬ ɫɱɢɬɚɧɧɵɣ ɡɚɞɚɱɧɢɤ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 101 – ɡɚɩɪɨɫ ɩɪɢ ɨɬɫɭɬɫɬɜɢɢ ɡɚɞɚɱɧɢɤɚ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. Ɂɚɞɚɱɧɢɤ ɡɚɩɢɫɵɜɚɟɬɫɹ ɜ ɮɚɣɥ FileName ɩɨɞ ɢɦɟɧɟɦ CompName. ȿɫɥɢ ɜɨ ɜɪɟɦɹ ɜɵɩɨɥɧɟɧɢɹ ɡɚɩɪɨɫɚ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ, ɬɨ ɝɟɧɟɪɢɪɭɟɬɫɹ ɜɧɭɬɪɟɧɧɹɹ ɨɲɢɛɤɚ 103 – ɨɲɢɛɤɚ ɡɚɩɢɫɢ ɡɚɞɚɱɧɢɤɚ. ɍɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ. ȼɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. ȼ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɭɫɩɟɲɧɨ ɡɚɜɟɪɲɚɟɬɫɹ.
3.3.1.3 Ɂɚɤɪɵɬɶ ɡɚɞɚɱɧɢɤ (tbDelete) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function tbDelete : Logic; C:
1. 2. 3.
Logic tbDelete() ɇɚɡɧɚɱɟɧɢɟ – ɭɞɚɥɹɟɬ ɢɡ ɩɚɦɹɬɢ ɪɚɧɟɟ ɫɱɢɬɚɧɧɵɣ ɡɚɞɚɱɧɢɤ. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. ȿɫɥɢ Error <> 0, ɬɨ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. ȿɫɥɢ ɟɫɬɶ ɨɬɤɪɵɬɵɟ ɫɟɚɧɫɵ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 105 – ɡɚɤɪɵɬɢɟ ɡɚɞɚɱɧɢɤɚ ɩɪɢ ɨɬɤɪɵɬɵɯ ɫɟɚɧɫɚɯ. ɍɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ. ȼɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. Ɂɚɞɚɱɧɢɤ ɡɚɤɪɵɜɚɟɬɫɹ. Ɂɚɩɪɨɫ ɭɫɩɟɲɧɨ ɡɚɜɟɪɲɚɟɬɫɹ.
3.3.2 ɇɚɱɚɥɨ ɢ ɤɨɧɟɰ ɫɟɚɧɫɚ Ʉ ɷɬɨɣ ɝɪɭɩɩɟ ɡɚɩɪɨɫɨɜ ɨɬɧɨɫɹɬɫɹ ɞɜɚ ɡɚɩɪɨɫɚ, ɨɬɤɪɵɜɚɸɳɢɟ ɢ ɡɚɤɪɵɜɚɸɳɢɟ ɫɟɚɧɫɵ ɪɚɛɨɬɵ ɫ ɡɚɞɚɱɧɢɤɨɦ.
3.3.2.1 ɇɚɱɚɥɨ ɫɟɚɧɫɚ (InitSession) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function InitSession( NewColor : Color; Oper : Integer; Var Handle: Integer ) : Logic; C: Logic InitSession(Color NewColor, Integer Oper, Integer* Handle) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: NewColor – ɰɜɟɬ ɞɥɹ ɨɬɛɨɪɚ ɩɪɢɦɟɪɨɜ ɡɚɞɚɱɧɢɤɚ ɜ ɬɟɤɭɳɭɸ ɜɵɛɨɪɤɭ. Oper – ɨɩɟɪɚɰɢɹ ɞɥɹ ɨɬɛɨɪɚ ɜ ɬɟɤɭɳɭɸ ɜɵɛɨɪɤɭ. Ⱦɨɥɠɧɚ ɛɵɬɶ ɨɞɧɨɣ ɢɡ ɤɨɧɫɬɚɧɬ CEqual, CIn, CInclude, Cxclude, CIntersect Handle – ɧɨɦɟɪ ɫɟɚɧɫɚ. ɇɚɱɚɥɶɧɨɟ ɡɧɚɱɟɧɢɟ ɧɟ ɜɚɠɧɨ. ȼ ɷɬɨɦ ɚɪɝɭɦɟɧɬɟ ɜɨɡɜɪɚɳɚɟɬɫɹ ɧɨɦɟɪ ɫɟɚɧɫɚ. ɇɚɡɧɚɱɟɧɢɟ – ɧɚɱɢɧɚɟɬ ɫɟɚɧɫ. Ɉɬɛɢɪɚɟɬ ɬɟɤɭɳɭɸ ɜɵɛɨɪɤɭ. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. 1. ȿɫɥɢ Error <> 0, ɬɨ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 2. ȿɫɥɢ ɚɪɝɭɦɟɧɬ Oper ɹɜɥɹɟɬɫɹ ɧɟɞɨɩɭɫɬɢɦɵɦ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 106 – ɧɟɞɨɩɭɫɬɢɦɵɣ ɤɨɞ ɨɩɟɪɚɰɢɢ ɩɪɢ ɨɬɤɪɵɬɢɢ ɫɟɚɧɫɚ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ. ɋɟɚɧɫ ɧɟ ɨɬɤɪɵɜɚɟɬɫɹ. ȼɨɡɜɪɚɳɚɟɬɫɹ ɡɧɚɱɟɧɢɟ ɥɨɠɶ. 3. ɋɨɡɞɚɟɬɫɹ ɧɨɜɵɣ ɫɟɚɧɫ (ɜ ɨɞɧɨ-ɫɟɚɧɫɨɜɵɯ ɡɚɞɚɱɧɢɤɚɯ ɩɪɨɫɬɨ ɢɧɢɰɢɢɪɭɟɬɫɹ ɫɟɚɧɫ). ɇɨɦɟɪ ɫɟɚɧɫɚ ɡɚɧɨɫɢɬɫɹ ɜ ɚɪɝɭɦɟɧɬ Handle. 4. Ɂɧɚɱɟɧɢɹ ɚɪɝɭɦɟɧɬɨɜ NewColor ɢ Oper ɫɨɯɪɚɧɹɸɬɫɹ ɜɨ ɜɧɭɬɪɟɧɧɢɯ ɩɟɪɟɦɟɧɧɵɯ ɡɚɞɚɱɧɢɤɚ 5. ɍɤɚɡɚɬɟɥɸ ɬɟɤɭɳɟɝɨ ɩɪɢɦɟɪɚ ɩɪɢɫɜɚɢɜɚɟɬɫɹ ɫɨɫɬɨɹɧɢɟ «ɞɨ ɩɟɪɜɨɝɨ ɩɪɢɦɟɪɚ» 6. InitSession := Next(Handle) – ɪɟɡɭɥɶɬɚɬ ɜɵɩɨɥɧɟɧɢɹ ɡɚɩɪɨɫɚ ɫɨɜɩɚɞɚɟɬ ɫ ɪɟɡɭɥɶɬɚɬɨɦ ɜɵɩɨɥɧɟɧɢɹ ɜɵɡɜɚɧɧɨɝɨ ɡɚɩɪɨɫɚ «ɋɥɟɞɭɸɳɢɣ ɩɪɢɦɟɪ».
3.3.2.2 Ʉɨɧɟɰ ɫɟɚɧɫɚ (EndSession) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Procedure EndSession( Handle : Integer ); C: void EndSession(Integer Handle) ɇɚɡɧɚɱɟɧɢɟ – ɡɚɤɪɵɜɚɟɬ ɫɟɚɧɫ. Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: Handle – ɧɨɦɟɪ ɫɟɚɧɫɚ. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ.
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1. 2. 3.
ȿɫɥɢ Error <> 0, ɬɨ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. ȿɫɥɢ ɚɪɝɭɦɟɧɬ Handle ɧɟ ɤɨɪɪɟɤɬɟɧ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 107 – ɧɟɜɟɪɧɵɣ ɧɨɦɟɪ ɫɟɚɧɫɚ. ɍɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ. ȼɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. Ɉɫɜɨɛɨɠɞɚɟɬɫɹ ɜɫɹ ɩɚɦɹɬɶ, ɜɡɹɬɚɹ ɞɥɹ ɜɵɩɨɥɧɟɧɢɹ ɫɟɚɧɫɚ. ɉɨɫɥɟ ɷɬɨɝɨ ɫɟɚɧɫ ɡɚɜɟɪɲɚɟɬɫɹ.
3.3.3 ɉɟɪɟɦɟɳɟɧɢɟ ɩɨ ɩɪɢɦɟɪɚɦ ȼ ɷɬɭ ɝɪɭɩɩɭ ɡɚɩɪɨɫɨɜ ɜɯɨɞɹɬ ɡɚɩɪɨɫɵ ɩɨɡɜɨɥɹɸɳɢɟ ɭɩɪɚɜɥɹɬɶ ɩɨɥɨɠɟɧɢɟɦ ɬɟɤɭɳɟɝɨ ɭɤɚɡɚɬɟɥɹ ɜ ɬɟɤɭɳɟɣ ɜɵɛɨɪɤɟ.
3.3.3.1 ȼ ɧɚɱɚɥɨ (Home) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function ɇɨɦɟ( Handle : Integer ) : Logic; C: Logic ɇɨɦɟ(Integer Handle) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: Handle – ɧɨɦɟɪ ɫɟɚɧɫɚ. ɇɚɡɧɚɱɟɧɢɟ – ɞɟɥɚɟɬ ɬɟɤɭɳɢɦ ɩɟɪɜɵɣ ɩɪɢɦɟɪ ɬɟɤɭɳɟɣ ɜɵɛɨɪɤɢ. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. 1. ȿɫɥɢ Error <> 0, ɬɨ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 2. ȿɫɥɢ ɚɪɝɭɦɟɧɬ Handle ɧɟ ɤɨɪɪɟɤɬɟɧ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 107 – ɧɟɜɟɪɧɵɣ ɧɨɦɟɪ ɫɟɚɧɫɚ. ɍɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ. ȼɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 3. ɍɤɚɡɚɬɟɥɸ ɧɚ ɬɟɤɭɳɢɣ ɩɪɢɦɟɪ ɩɪɢɫɜɚɢɜɚɟɬɫɹ ɡɧɚɱɟɧɢɟ «ɞɨ ɩɟɪɜɨɝɨ ɩɪɢɦɟɪɚ» 4. Home := Next(Handle) – ɪɟɡɭɥɶɬɚɬ ɜɵɩɨɥɧɟɧɢɹ ɡɚɩɪɨɫɚ ɫɨɜɩɚɞɚɟɬ ɫ ɪɟɡɭɥɶɬɚɬɨɦ ɜɵɩɨɥɧɟɧɢɹ ɜɵɡɜɚɧɧɨɝɨ ɡɚɩɪɨɫɚ «ɋɥɟɞɭɸɳɢɣ»
3.3.3.2 ȼ ɤɨɧɟɰ (End) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function End( Handle : Integer ) : Logic; C: Logic End(Integer Handle) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: Handle – ɧɨɦɟɪ ɫɟɚɧɫɚ. ɇɚɡɧɚɱɟɧɢɟ – ɞɟɥɚɟɬ ɬɟɤɭɳɢɦ ɩɨɫɥɟɞɧɢɣ ɩɪɢɦɟɪ ɬɟɤɭɳɟɣ ɜɵɛɨɪɤɢ. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. 1. ȿɫɥɢ Error <> 0, ɬɨ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 2. ȿɫɥɢ ɚɪɝɭɦɟɧɬ Handle ɧɟ ɤɨɪɪɟɤɬɟɧ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 107 – ɧɟɜɟɪɧɵɣ ɧɨɦɟɪ ɫɟɚɧɫɚ. ɍɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ. ȼɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 3. ɍɤɚɡɚɬɟɥɸ ɧɚ ɬɟɤɭɳɢɣ ɩɪɢɦɟɪ ɩɪɢɫɜɚɢɜɚɟɬɫɹ ɡɧɚɱɟɧɢɟ «ɩɨɫɥɟ ɩɨɫɥɟɞɧɟɝɨ ɩɪɢɦɟɪɚ» 4. Home := Prev(Handle) – ɪɟɡɭɥɶɬɚɬ ɜɵɩɨɥɧɟɧɢɹ ɡɚɩɪɨɫɚ ɫɨɜɩɚɞɚɟɬ ɫ ɪɟɡɭɥɶɬɚɬɨɦ ɜɵɩɨɥɧɟɧɢɹ ɜɵɡɜɚɧɧɨɝɨ ɡɚɩɪɨɫɚ «ɉɪɟɞɵɞɭɳɢɣ»
3.3.3.3 ɋɥɟɞɭɸɳɢɣ (Next) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function Next( Handle : Integer ) : Logic; C: Logic Next(Integer Handle) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: Handle – ɧɨɦɟɪ ɫɟɚɧɫɚ. ɇɚɡɧɚɱɟɧɢɟ – ɞɟɥɚɟɬ ɬɟɤɭɳɢɦ ɫɥɟɞɭɸɳɢɣ ɩɪɢɦɟɪ ɬɟɤɭɳɟɣ ɜɵɛɨɪɤɢ. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. 1. ȿɫɥɢ Error <> 0, ɬɨ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 2. ȿɫɥɢ ɚɪɝɭɦɟɧɬ Handle ɧɟ ɤɨɪɪɟɤɬɟɧ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 107 – ɧɟɜɟɪɧɵɣ ɧɨɦɟɪ ɫɟɚɧɫɚ. ɍɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ. ȼɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 3. ȿɫɥɢ ɡɧɚɱɟɧɢɟ ɭɤɚɡɚɬɟɥɹ ɪɚɜɧɨ «ɩɨɫɥɟ ɩɨɫɥɟɞɧɟɝɨ ɩɪɢɦɟɪɚ», ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 108 – ɩɟɪɟɯɨɞ ɡɚ ɤɨɧɟɱɧɭɸ ɝɪɚɧɢɰɭ ɬɟɤɭɳɟɣ ɜɵɛɨɪɤɢ, ɢ ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ. ȼ ɫɥɭɱɚɟ
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4.
5. 6.
7.
ɜɨɡɜɪɚɬɚ ɭɩɪɚɜɥɟɧɢɹ ɜ ɡɚɩɪɨɫ, ɩɪɨɢɫɯɨɞɢɬ ɧɟɦɟɞɥɟɧɧɵɣ ɜɵɯɨɞ ɢɡ ɡɚɩɪɨɫɚ ɫ ɜɨɡɜɪɚɳɟɧɢɟɦ ɡɧɚɱɟɧɢɹ ɥɨɠɶ. ȿɫɥɢ ɡɧɚɱɟɧɢɟ ɭɤɚɡɚɬɟɥɹ ɬɟɤɭɳɟɝɨ ɩɪɢɦɟɪɚ ɪɚɜɧɨ «ɞɨ ɩɟɪɜɨɝɨ ɩɪɢɦɟɪɚ», ɬɨ ɩɪɢɫɜɚɢɜɚɟɦ ɭɤɚɡɚɬɟɥɸ ɚɞɪɟɫ ɩɟɪɜɨɝɨ ɩɪɢɦɟɪɚ ɡɚɞɚɱɧɢɤɚ. ȿɫɥɢ ɚɞɪɟɫ ɜ ɩɟɪɟɦɟɧɧɨɣ ɜ ɡɚɞɚɱɧɢɤɟ ɧɟɬ ɩɪɢɦɟɪɨɜ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 108 – ɩɟɪɟɯɨɞ ɡɚ ɤɨɧɟɱɧɭɸ ɝɪɚɧɢɰɭ ɬɟɤɭɳɟɣ ɜɵɛɨɪɤɢ, ɢ ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ. ȼ ɫɥɭɱɚɟ ɜɨɡɜɪɚɬɚ ɭɩɪɚɜɥɟɧɢɹ ɜ ɡɚɩɪɨɫ, ɩɪɨɢɫɯɨɞɢɬ ɧɟɦɟɞɥɟɧɧɵɣ ɜɵɯɨɞ ɢɡ ɡɚɩɪɨɫɚ ɫ ɜɨɡɜɪɚɳɟɧɢɟɦ ɡɧɚɱɟɧɢɹ ɥɨɠɶ. ȼ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ ɩɟɪɟɯɨɞɢɦ ɤ ɲɚɝɭ 6 ɍɤɚɡɚɬɟɥɶ ɩɟɪɟɦɟɳɚɟɬɫɹ ɧɚ ɫɥɟɞɭɸɳɢɣ ɩɪɢɦɟɪ ɡɚɞɚɱɧɢɤɚ. ȿɫɥɢ ɫɥɟɞɭɸɳɟɝɨ ɩɪɢɦɟɪɚ ɡɚɞɚɱɧɢɤɚ ɧɟɬ, ɬɨ ɭɤɚɡɚɬɟɥɸ ɩɪɢɫɜɚɢɜɚɟɬɫɹ ɡɧɚɱɟɧɢɟ «ɩɨɫɥɟ ɩɨɫɥɟɞɧɟɝɨ ɩɪɢɦɟɪɚ». ɉɟɪɟɯɨɞɢɦ ɤ ɲɚɝɭ 5, ɟɫɥɢ ɧɟ ɜɟɪɧɨ ɭɫɥɨɜɢɟ: ((GetColor Oper NewColor) And Last, ɝɞɟ Oper ɢ NewColor – ɚɪɝɭɦɟɧɬɵ ɡɚɩɪɨɫɚ InitSession, ɤɨɬɨɪɵɦ ɛɵɥ ɨɬɤɪɵɬ ɞɚɧɧɵɣ ɫɟɚɧɫ. Next := Not Last (ɉɟɪɟɯɨɞ ɤ ɫɥɟɞɭɸɳɟɦɭ ɩɪɢɦɟɪɭ ɡɚɜɟɪɲɢɥɫɹ ɭɞɚɱɧɨ, ɟɫɥɢ ɭɤɚɡɚɬɟɥɶ ɧɟ ɭɫɬɚɧɨɜɥɟɧ ɜ ɡɧɚɱɟɧɢɟ «ɩɨɫɥɟ ɩɨɫɥɟɞɧɟɝɨ ɩɪɢɦɟɪɚ»).
3.3.3.4 ɉɪɟɞɵɞɭɳɢɣ (Prev) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function Prev( Handle : Integer ): Logic; C: Logic Prev(Integer Handle) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: Handle – ɧɨɦɟɪ ɫɟɚɧɫɚ. ɇɚɡɧɚɱɟɧɢɟ – ɞɟɥɚɟɬ ɬɟɤɭɳɢɦ ɩɪɟɞɵɞɭɳɢɣ ɩɪɢɦɟɪ ɬɟɤɭɳɟɣ ɜɵɛɨɪɤɢ. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. 1. ȿɫɥɢ Error <> 0, ɬɨ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 2. ȿɫɥɢ ɚɪɝɭɦɟɧɬ Handle ɧɟ ɤɨɪɪɟɤɬɟɧ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 107 – ɧɟɜɟɪɧɵɣ ɧɨɦɟɪ ɫɟɚɧɫɚ. ɍɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ. ȼɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 3. ȿɫɥɢ ɡɧɚɱɟɧɢɟ ɭɤɚɡɚɬɟɥɹ ɪɚɜɧɨ «ɞɨ ɩɟɪɜɨɝɨ ɩɪɢɦɟɪɚ», ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 109 – ɩɟɪɟɯɨɞ ɡɚ ɧɚɱɚɥɶɧɭɸ ɝɪɚɧɢɰɭ ɬɟɤɭɳɟɣ ɜɵɛɨɪɤɢ, ɢ ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ. ȼ ɫɥɭɱɚɟ ɜɨɡɜɪɚɬɚ ɭɩɪɚɜɥɟɧɢɹ ɜ ɡɚɩɪɨɫ, ɩɪɨɢɫɯɨɞɢɬ ɧɟɦɟɞɥɟɧɧɵɣ ɜɵɯɨɞ ɢɡ ɡɚɩɪɨɫɚ ɫ ɜɨɡɜɪɚɳɟɧɢɟɦ ɡɧɚɱɟɧɢɹ ɥɨɠɶ. 4. ȿɫɥɢ ɡɧɚɱɟɧɢɟ ɭɤɚɡɚɬɟɥɹ ɪɚɜɧɨ «ɩɨɫɥɟ ɩɨɫɥɟɞɧɟɝɨ ɩɪɢɦɟɪɚ», ɬɨ ɩɪɢɫɜɚɢɜɚɟɦ ɭɤɚɡɚɬɟɥɸ ɚɞɪɟɫ ɩɨɫɥɟɞɧɟɝɨ ɩɪɢɦɟɪɚ ɡɚɞɚɱɧɢɤɚ. ȿɫɥɢ ɜ ɡɚɞɚɱɧɢɤɟ ɧɟɬ ɩɪɢɦɟɪɨɜ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 109 – ɩɟɪɟɯɨɞ ɡɚ ɧɚɱɚɥɶɧɭɸ ɝɪɚɧɢɰɭ ɬɟɤɭɳɟɣ ɜɵɛɨɪɤɢ, ɢ ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ. ȼ ɫɥɭɱɚɟ ɜɨɡɜɪɚɬɚ ɭɩɪɚɜɥɟɧɢɹ ɜ ɡɚɩɪɨɫ, ɩɪɨɢɫɯɨɞɢɬ ɧɟɦɟɞɥɟɧɧɵɣ ɜɵɯɨɞ ɢɡ ɡɚɩɪɨɫɚ ɫ ɜɨɡɜɪɚɳɟɧɢɟɦ ɡɧɚɱɟɧɢɹ ɥɨɠɶ. 5. ȼ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ ɲɚɝ 7. 6. ɍɤɚɡɚɬɟɥɶ ɩɟɪɟɦɟɳɚɟɬɫɹ ɧɚ ɩɪɟɞɵɞɭɳɢɣ ɩɪɢɦɟɪ ɡɚɞɚɱɧɢɤɚ. ȿɫɥɢ ɩɪɟɞɵɞɭɳɟɝɨ ɩɪɢɦɟɪɚ ɡɚɞɚɱɧɢɤɚ ɧɟɬ, ɬɨ ɭɤɚɡɚɬɟɥɸ ɩɪɢɫɜɚɢɜɚɟɬɫɹ ɡɧɚɱɟɧɢɟ «ɞɨ ɩɟɪɜɨɝɨ ɩɪɢɦɟɪɚ». 7. ɒɚɝ 6 ɩɨɜɬɨɪɹɟɬɫɹ ɞɨ ɬɟɯ ɩɨɪ, ɩɨɤɚ ɧɟ ɜɵɩɨɥɧɢɬɫɹ ɭɫɥɨɜɢɟ: ((GetColor Oper NewColor) And First 8. Next := Not Last (ɉɟɪɟɯɨɞ ɤ ɫɥɟɞɭɸɳɟɦɭ ɩɪɢɦɟɪɭ ɡɚɜɟɪɲɢɥɫɹ ɭɞɚɱɧɨ, ɟɫɥɢ ɭɤɚɡɚɬɟɥɶ ɧɟ ɭɫɬɚɧɨɜɥɟɧ ɜ ɡɧɚɱɟɧɢɟ «ɩɨɫɥɟ ɩɨɫɥɟɞɧɟɝɨ ɩɪɢɦɟɪɚ»).
3.3.3.5 Ʉɨɧɟɰ (Last) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function Last( Handle : Integer ) : Logic; C: Logic Last(Integer Handle) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: Handle – ɧɨɦɟɪ ɫɟɚɧɫɚ. ɇɚɡɧɚɱɟɧɢɟ – ɜɨɡɜɪɚɳɚɟɬ ɡɧɚɱɟɧɢɟ ɢɫɬɢɧɚ, ɟɫɥɢ ɬɟɤɭɳɢɦ ɹɜɥɹɟɬɫɹ ɫɨɫɬɨɹɧɢɟ «ɩɨɫɥɟ ɩɨɫɥɟɞɧɟɝɨ ɩɪɢɦɟɪɚ», ɢ ɥɨɠɶ – ɜ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. 1. ȿɫɥɢ Error <> 0, ɬɨ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 2. ȿɫɥɢ ɚɪɝɭɦɟɧɬ Handle ɧɟ ɤɨɪɪɟɤɬɟɧ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 107 – ɧɟɜɟɪɧɵɣ ɧɨɦɟɪ ɫɟɚɧɫɚ. ɍɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ. ȼɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ.
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ȼɨɡɜɪɚɳɚɟɬ ɡɧɚɱɟɧɢɟ ɢɫɬɢɧɚ, ɟɫɥɢ ɬɟɤɭɳɢɦ ɹɜɥɹɟɬɫɹ ɫɨɫɬɨɹɧɢɟ «ɩɨɫɥɟ ɩɨɫɥɟɞɧɟɝɨ ɩɪɢɦɟɪɚ», ɢ ɥɨɠɶ – ɜ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ.
3.3.3.6 ɇɚɱɚɥɨ (First) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function First( Handle : Integer ): Logic; C: Logic First(Integer Handle) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: Handle – ɧɨɦɟɪ ɫɟɚɧɫɚ. ɇɚɡɧɚɱɟɧɢɟ – ɜɨɡɜɪɚɳɚɟɬ ɡɧɚɱɟɧɢɟ ɢɫɬɢɧɚ, ɟɫɥɢ ɬɟɤɭɳɢɦ ɹɜɥɹɟɬɫɹ ɫɨɫɬɨɹɧɢɟ «ɩɟɪɟɞ ɩɟɪɜɵɦ ɩɪɢɦɟɪɨɦ», ɢ ɥɨɠɶ ɜ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. 1. ȿɫɥɢ Error <> 0, ɬɨ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 2. ȿɫɥɢ ɚɪɝɭɦɟɧɬ Handle ɧɟ ɤɨɪɪɟɤɬɟɧ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 107 – ɧɟɜɟɪɧɵɣ ɧɨɦɟɪ ɫɟɚɧɫɚ. ɍɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ. ȼɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 3. ȼɨɡɜɪɚɳɚɟɬ ɡɧɚɱɟɧɢɟ ɢɫɬɢɧɚ, ɟɫɥɢ ɬɟɤɭɳɢɦ ɹɜɥɹɟɬɫɹ ɫɨɫɬɨɹɧɢɟ «ɩɟɪɟɞ ɩɟɪɜɵɦ ɩɪɢɦɟɪɨɦ», ɢ ɥɨɠɶ ɜ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ.
3.3.3.7 ɉɪɢɦɟɪ ɧɨɦɟɪ (Example) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function Example( Number : Long; Handle : Integer ) : Logic; C: Logic Example(Long Number, Integer Handle) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: Number – ɧɨɦɟɪ ɩɪɢɦɟɪɚ, ɤɨɬɨɪɵɣ ɞɨɥɠɟɧ ɛɵɬɶ ɫɞɟɥɚɧ ɬɟɤɭɳɢɦ. ɇɭɦɟɪɚɰɢɹ ɩɪɢɦɟɪɨɜ ɜɟɞɟɬɫɹ ɫ ɟɞɢɧɢɰɵ. Handle – ɧɨɦɟɪ ɫɟɚɧɫɚ. ɇɚɡɧɚɱɟɧɢɟ – ɞɟɥɚɟɬ ɬɟɤɭɳɢɦ ɩɪɢɦɟɪ ɬɟɤɭɳɟɣ ɜɵɛɨɪɤɢ ɫ ɭɤɚɡɚɧɧɵɦ ɧɨɦɟɪɨɦ. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. 1. ȿɫɥɢ Error <> 0, ɬɨ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 2. ȿɫɥɢ ɚɪɝɭɦɟɧɬ Handle ɧɟ ɤɨɪɪɟɤɬɟɧ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 107 – ɧɟɜɟɪɧɵɣ ɧɨɦɟɪ ɫɟɚɧɫɚ. ɍɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ. ȼɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 3. ɍɤɚɡɚɬɟɥɶ ɭɫɬɚɧɚɜɥɢɜɚɟɬɫɹ ɜ ɫɨɫɬɨɹɧɢɟ «ɞɨ ɩɟɪɜɨɝɨ ɩɪɢɦɟɪɚ». 4. Number ɪɚɡ ɜɵɩɨɥɧɹɟɦ ɡɚɩɪɨɫ Next. 5. Example := Not Last (ȿɫɥɢ ɧɟ ɭɫɬɚɧɨɜɥɟɧɨ ɫɨɫɬɨɹɧɢɟ «ɩɨɫɥɟ ɩɨɫɥɟɞɧɟɝɨ ɩɪɢɦɟɪɚ», ɬɨ ɡɚɩɪɨɫ ɜɵɩɨɥɧɟɧ ɭɫɩɟɲɧɨ).
3.3.4 Ɉɩɪɟɞɟɥɟɧɢɟ, ɩɨɥɭɱɟɧɢɟ ɢ ɢɡɦɟɧɟɧɢɟ ɞɚɧɧɵɯ Ʉ ɞɚɧɧɨɣ ɝɪɭɩɩɟ ɡɚɩɪɨɫɨɜ ɨɬɧɨɫɹɬɫɹ ɡɚɩɪɨɫɵ ɩɨɡɜɨɥɹɸɳɢɟ ɩɨɥɭɱɚɬɶ ɞɚɧɧɵɟ ɢɡ ɡɚɞɚɱɧɢɤɚ, ɡɚɧɨɫɢɬɶ ɞɚɧɧɵɟ ɜ ɡɚɞɚɱɧɢɤ ɢ ɫɛɪɨɫɢɬɶ ɩɪɟɞɨɛɪɚɛɨɬɤɭ (ɧɟɨɛɯɨɞɢɦɨ ɜɵɩɨɥɧɢɬɶ ɞɚɧɧɵɣ ɡɚɩɪɨɫ ɩɨɫɥɟ ɢɡɦɟɧɟɧɢɣ ɜ ɞɚɧɧɵɯ ɢɥɢ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɟ, ɟɫɥɢ ɡɚɞɚɱɧɢɤ ɯɪɚɧɢɬ ɜɟɤɬɨɪɵ ɩɪɟɞɨɛɪɚɛɨɬɚɧɧɵɯ ɞɚɧɧɵɯ)
3.3.4.1 Ⱦɚɬɶ ɩɪɢɦɟɪ (Get) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function Get( Handle : Integer; Var Data : PRealArray; What : Integer ) : Logic; C: Logic Get(Integer Handle, PRealArray* Data, Integer What) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: Handle – ɧɨɦɟɪ ɫɟɚɧɫɚ; Data – ɭɤɚɡɚɬɟɥɶ ɧɚ ɦɚɫɫɢɜ, ɜ ɤɨɬɨɪɨɦ ɞɨɥɠɧɵ ɛɵɬɶ ɜɨɡɜɪɚɳɟɧɵ ɞɚɧɧɵt; What – ɨɞɧɚ ɢɡ ɩɪɟɞɨɩɪɟɞɟɥɟɧɧɵɯ ɤɨɧɫɬɚɧɬ tbColor, tbInput, tbPrepared, tbAnswers, tbReliability, tbCalcAnswers, tbCalcReliability, tbWeight, tbEstimation, tbComment ɇɚɡɧɚɱɟɧɢɟ – ɜɨɡɜɪɚɳɚɟɬ ɜɫɸ ɭɤɚɡɚɧɧɭɸ ɜ ɡɚɩɪɨɫɟ «ɉɚɪɚɦɟɬɪɵ ɩɪɢɦɟɪɚ» ɢɧɮɨɪɦɚɰɢɸ. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ.
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ȿɫɥɢ Error <> 0, ɬɨ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. ȿɫɥɢ ɚɪɝɭɦɟɧɬ Handle ɧɟ ɤɨɪɪɟɤɬɟɧ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 107 – ɧɟɜɟɪɧɵɣ ɧɨɦɟɪ ɫɟɚɧɫɚ. ɍɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ. ȼɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. ȿɫɥɢ ɚɪɝɭɦɟɧɬ What ɢɦɟɟɬ ɧɟɞɨɩɭɫɬɢɦɨɟ ɡɧɚɱɟɧɢɟ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 110 – ɧɟɜɟɪɧɵɣ ɬɢɩ ɜɟɤɬɨɪɚ ɜ ɡɚɩɪɨɫɟ Get. ɍɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ. ȼɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. ȿɫɥɢ ɬɟɤɭɳɢɣ ɭɤɚɡɚɬɟɥɶ ɭɤɚɡɵɜɚɟɬ ɧɚ ɨɞɧɨ ɢɡ ɫɨɫɬɨɹɧɢɣ «ɞɨ ɩɟɪɜɨɝɨ ɩɪɢɦɟɪɚ» ɢɥɢ «ɩɨɫɥɟ ɩɨɫɥɟɞɧɟɝɨ ɩɪɢɦɟɪɚ», ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 111 – ɩɨɩɵɬɤɚ ɱɬɟɧɢɹ ɞɨ ɢɥɢ ɩɨɫɥɟ ɬɟɤɭɳɟɣ ɜɵɛɨɪɤɢ. ɍɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ. Ɂɚɩɪɨɫ ɡɚɜɟɪɲɚɟɬɫɹ ɧɟɭɫɩɟɲɧɨ. ȿɫɥɢ ɜ ɚɪɝɭɦɟɧɬɟ What ɭɤɚɡɚɧ ɜɟɤɬɨɪ ɩɪɟɞɨɛɪɚɛɨɬɚɧɧɵɯ ɞɚɧɧɵɯ, ɧɨ ɜ ɬɟɤɭɳɟɦ ɩɪɢɦɟɪɟ ɨɧ ɨɬɫɭɬɫɬɜɭɟɬ, ɬɨ ɝɟɧɟɪɢɪɭɟɬɫɹ ɡɚɩɪɨɫ ɩɪɟɞɨɛɪɚɛɨɬɚɬɶ ɞɚɧɧɵɟ. ȿɫɥɢ ɩɪɟɞɨɛɪɚɛɨɬɤɚ ɡɚɜɟɪɲɚɟɬɫɹ ɭɫɩɟɲɧɨ, ɬɨ ɩɨɥɭɱɟɧɧɵɣ ɜɟɤɬɨɪ ɩɪɟɞɨɛɪɚɛɨɬɚɧɧɵɯ ɞɚɧɧɵɯ ɜɤɥɸɱɚɟɬɫɹ ɜ ɩɪɢɦɟɪ, ɜ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. ȼɨɡɜɪɚɳɚɟɬɫɹ ɡɧɚɱɟɧɢɟ ɥɨɠɶ. ȼ ɷɥɟɦɟɧɬɵ ɦɚɫɫɢɜɚ, ɧɚ ɤɨɬɨɪɵɣ ɭɤɚɡɵɜɚɟɬ ɚɪɝɭɦɟɧɬ Data, ɤɨɩɢɪɭɸɬɫɹ ɞɚɧɧɵɟ ɢɡ ɬɨɝɨ ɜɟɤɬɨɪɚ ɞɚɧɧɵɯ ɬɟɤɭɳɟɝɨ ɩɪɢɦɟɪɚ, ɤɨɬɨɪɵɣ ɭɤɚɡɚɧ ɜ ɚɪɝɭɦɟɧɬɟ What. ȿɫɥɢ ɬɪɟɛɭɟɦɵɣ ɜɟɤɬɨɪ ɜ ɡɚɞɚɱɧɢɤɟ ɨɬɫɭɬɫɬɜɭɟɬ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 112 – ɞɚɧɧɵɟ ɨɬɫɭɬɫɬɜɭɸɬ ɢ ɡɚɩɪɨɫ ɡɚɜɟɪɲɚɟɬɫɹ ɫɨ ɡɧɚɱɟɧɢɟɦ ɥɨɠɶ. ȼ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ ɡɚɩɪɨɫ ɭɫɩɟɲɧɨ ɡɚɜɟɪɲɚɟɬɫɹ.
3.3.4.2 Ɉɛɧɨɜɢɬɶ ɞɚɧɧɵɟ (Put) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function Put( Handle : Integer; Data : PRealArray; What : Integer ) : Logic; C: Logic Put(Integer Handle, PRealArray Data, Integer What) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: Handle – ɧɨɦɟɪ ɫɟɚɧɫɚ Data – ɭɤɚɡɚɬɟɥɶ ɧɚ ɦɚɫɫɢɜ, ɜ ɤɨɬɨɪɨɦ ɩɟɪɟɞɚɧɵ ɞɚɧɧɵɟ, ɤɨɬɨɪɵɟ ɞɨɥɠɧɵ ɛɵɬɶ ɡɚɧɟɫɟɧɵ ɜ ɡɚɞɚɱɧɢɤ. What – ɨɞɧɚ ɢɡ ɩɪɟɞɨɩɪɟɞɟɥɟɧɧɵɯ ɤɨɧɫɬɚɧɬ tbColor, tbInput, tbPrepared, tbAnswers, tbReliability, tbCalcAnswers, tbCalcReliability, tbWeight, tbEstimation, tbComment ɇɚɡɧɚɱɟɧɢɟ – ɨɛɧɨɜɢɬɶ ɞɚɧɧɵɟ ɬɟɤɭɳɟɝɨ ɩɪɢɦɟɪɚ Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. 1. ȿɫɥɢ Error <> 0, ɬɨ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 2. ȿɫɥɢ ɚɪɝɭɦɟɧɬ Handle ɧɟ ɤɨɪɪɟɤɬɟɧ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 107 – ɧɟɜɟɪɧɵɣ ɧɨɦɟɪ ɫɟɚɧɫɚ. ɍɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ. ȼɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 3. ȿɫɥɢ ɚɪɝɭɦɟɧɬ What ɢɦɟɟɬ ɧɟɞɨɩɭɫɬɢɦɨɟ ɡɧɚɱɟɧɢɟ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 113 – ɧɟɜɟɪɧɵɣ ɬɢɩ ɜɟɤɬɨɪɚ ɜ ɡɚɩɪɨɫɟ Put. ɍɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ. ȼɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 4. ȿɫɥɢ ɬɟɤɭɳɢɣ ɭɤɚɡɚɬɟɥɶ ɭɤɚɡɵɜɚɟɬ ɧɚ ɨɞɧɨ ɢɡ ɫɨɫɬɨɹɧɢɣ «ɞɨ ɩɟɪɜɨɝɨ ɩɪɢɦɟɪɚ» ɢɥɢ «ɩɨɫɥɟ ɩɨɫɥɟɞɧɟɝɨ ɩɪɢɦɟɪɚ», ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 111 – ɩɨɩɵɬɤɚ ɱɬɟɧɢɹ ɞɨ ɢɥɢ ɩɨɫɥɟ ɬɟɤɭɳɟɣ ɜɵɛɨɪɤɢ. ɍɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ. Ɂɚɩɪɨɫ ɡɚɜɟɪɲɚɟɬɫɹ ɧɟɭɫɩɟɲɧɨ. 5. ȿɫɥɢ ɭɫɬɚɧɚɜɥɢɜɚɟɬɫɹ ɜɟɤɬɨɪ ɜɯɨɞɧɵɯ ɞɚɧɧɵɯ, ɬɨ ɞɥɹ ɬɟɤɭɳɟɝɨ ɩɪɢɦɟɪɚ ɞɨɥɠɟɧ ɛɵɬɶ ɨɫɜɨɛɨɠɞɟɧ ɜɟɤɬɨɪ ɩɪɟɞɨɛɪɚɛɨɬɚɧɧɵɯ ɞɚɧɧɵɯ. 6. ȼ ɞɚɧɧɵɟ ɩɪɢɦɟɪɚ ɤɨɩɢɪɭɸɬɫɹ ɡɧɚɱɟɧɢɹ, ɭɤɚɡɚɧɧɵɟ ɜ ɦɚɫɫɢɜɟ Data. Ɂɚɩɪɨɫ ɭɫɩɟɲɧɨ ɡɚɜɟɪɲɚɟɬɫɹ.
3.3.4.3 ɋɛɪɨɫɢɬɶ ɩɪɟɞɨɛɪɚɛɨɬɤɭ (RemovePrepare) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Procedure RemovePrepare; C:
1. 2.
void RemovePrepare() ɇɚɡɧɚɱɟɧɢɟ – ɨɬɦɟɧɚ ɩɪɟɞɨɛɪɚɛɨɬɤɢ ɜɫɟɯ ɪɚɧɟɟ ɩɪɟɞɨɛɪɚɛɨɬɚɧɧɵɯ ɩɪɢɦɟɪɨɜ. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. ȿɫɥɢ Error <> 0, ɬɨ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. ɍ ɜɫɟɯ ɩɪɢɦɟɪɨɜ ɡɚɞɚɱɧɢɤɚ ɨɫɜɨɛɨɠɞɚɸɬɫɹ ɜɟɤɬɨɪɚ ɩɪɟɞɨɛɪɚɛɨɬɚɧɧɵɯ ɞɚɧɧɵɯ.
3.3.5 Ɉɤɪɚɫɤɚ ɩɪɢɦɟɪɨɜ ȼ ɞɚɧɧɵɣ ɪɚɡɞɟɥ ɩɨɦɟɳɟɧɵ ɡɚɩɪɨɫɵ ɞɥɹ ɪɚɛɨɬɵ ɫ ɰɜɟɬɚɦɢ. Ɉɬɦɟɬɢɦ, ɱɬɨ ɰɜɟɬ ɩɪɢɦɟɪɚ, ɜɨɡɜɪɚɳɚɟɦɵɣ ɡɚɩɪɨɫɨɦ GetColor ɦɨɠɧɨ ɩɨɥɭɱɢɬɶ ɬɚɤɠɟ ɫ ɩɨɦɨɳɶɸ ɡɚɩɪɨɫɚ Get.
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3.3.5.1 Ⱦɚɬɶ ɰɜɟɬ ɩɪɢɦɟɪɚ (GetColor) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function GetColor( Handle : Integer ) : Color; C: Logic GetColor(Integer Handle) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: Handle – ɧɨɦɟɪ ɫɟɚɧɫɚ ɇɚɡɧɚɱɟɧɢɟ – ɜɨɡɜɪɚɳɚɟɬ ɰɜɟɬ ɬɟɤɭɳɟɝɨ ɩɪɢɦɟɪɚ. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. 1. ȿɫɥɢ Error <> 0, ɬɨ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 2. ȿɫɥɢ ɚɪɝɭɦɟɧɬ Handle ɧɟ ɤɨɪɪɟɤɬɟɧ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 107 – ɧɟɜɟɪɧɵɣ ɧɨɦɟɪ ɫɟɚɧɫɚ. ɍɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ. ȼɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 3. ȿɫɥɢ ɬɟɤɭɳɢɣ ɭɤɚɡɚɬɟɥɶ ɭɤɚɡɵɜɚɟɬ ɧɚ ɨɞɧɨ ɢɡ ɫɨɫɬɨɹɧɢɣ «ɞɨ ɩɟɪɜɨɝɨ ɩɪɢɦɟɪɚ» ɢɥɢ «ɩɨɫɥɟ ɩɨɫɥɟɞɧɟɝɨ ɩɪɢɦɟɪɚ», ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 111 – ɩɨɩɵɬɤɚ ɱɬɟɧɢɹ ɞɨ ɢɥɢ ɩɨɫɥɟ ɬɟɤɭɳɟɣ ɜɵɛɨɪɤɢ. ɍɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ. Ɂɚɩɪɨɫ ɡɚɜɟɪɲɚɟɬɫɹ ɧɟɭɫɩɟɲɧɨ. 4. ȼɨɡɜɪɚɳɚɟɬɫɹ ɰɜɟɬ ɬɟɤɭɳɟɝɨ ɩɪɢɦɟɪɚ.
3.3.5.2 ɉɨɤɪɚɫɢɬɶ ɩɪɢɦɟɪ (PaintCurrent) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function PaintCurrent( Handle : Integer; NewColor, ColorMask : Color; Oper : Integer) : Logic; C: Logic PaintCurrent(Integer Handle, Color NewColor, Color ColorMask, Integer Oper) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: Handle – ɧɨɦɟɪ ɫɟɚɧɫɚ. NewColor – ɧɨɜɵɣ ɰɜɟɬ ɞɥɹ ɨɤɪɚɫɤɢ ɩɪɢɦɟɪɚ. ColorMask – ɦɚɫɤɚ ɰɜɟɬɚ ɞɥɹ ɨɤɪɚɫɤɢ ɩɪɢɦɟɪɚ. Oper – ɨɩɟɪɚɰɢɹ, ɢɫɩɨɥɶɡɭɟɦɚɹ ɩɪɢ ɨɤɪɚɫɤɟ ɩɪɢɦɟɪɚ. Ⱦɨɥɠɧɚ ɛɵɬɶ ɨɞɧɨɣ ɢɡ ɤɨɧɫɬɚɧɬ COr, CAnd, CXor, CNot. ɇɚɡɧɚɱɟɧɢɟ – ɢɡɦɟɧɹɟɬ ɰɜɟɬ ɬɟɤɭɳɟɝɨ ɩɪɢɦɟɪɚ. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. 1. ȿɫɥɢ Error <> 0, ɬɨ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 2. ȿɫɥɢ ɚɪɝɭɦɟɧɬ Handle ɧɟ ɤɨɪɪɟɤɬɟɧ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 107 – ɧɟɜɟɪɧɵɣ ɧɨɦɟɪ ɫɟɚɧɫɚ. ɍɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ. ȼɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 3. ȿɫɥɢ Oper ɧɟ ɤɨɪɪɟɤɬɟɧ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 114 – ɧɟɜɟɪɧɚɹ ɨɩɟɪɚɰɢɹ ɨɤɪɚɫɤɢ ɩɪɢɦɟɪɚ. ɍɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ. Ɂɚɩɪɨɫ ɡɚɜɟɪɲɚɟɬɫɹ ɫɨ ɡɧɚɱɟɧɢɟɦ ɥɨɠɶ. 4. ɇɨɜɵɣ ɰɜɟɬ ɩɪɢɦɟɪɚ := (ɋɬɚɪɵɣ ɰɜɟɬ ɩɪɢɦɟɪɚ And ColorMask) Oper NewColor
3.3.5.3 Ɉɲɢɛɤɢ ɤɨɦɩɨɧɟɧɬɚ ɡɚɞɚɱɧɢɤɚ ȼ ɬɚɛɥ. 4 ɩɪɢɜɟɞɟɧ ɩɨɥɧɵɣ ɫɩɢɫɨɤ ɨɲɢɛɨɤ, ɤɨɬɨɪɵɟ ɦɨɝɭɬ ɜɨɡɧɢɤɚɬɶ ɩɪɢ ɜɵɩɨɥɧɟɧɢɢ ɡɚɩɪɨɫɨɜ ɤɨɦɩɨɧɟɧɬɨɦ ɡɚɞɚɱɧɢɤ, ɢ ɞɟɣɫɬɜɢɹ ɫɬɚɧɞɚɪɬɧɨɝɨ ɨɛɪɚɛɨɬɱɢɤɚ ɨɲɢɛɨɤ.
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Ɍɚɛɥɢɰɚ 4. Ɉɲɢɛɤɢ ɤɨɦɩɨɧɟɧɬɚ ɡɚɞɚɱɧɢɤ ɢ ɞɟɣɫɬɜɢɹ ɫɬɚɧɞɚɪɬɧɨɝɨ ɨɛɪɚɛɨɬɱɢɤɚ ɨɲɢɛɨɤ. ʋɇɚɡɜɚɧɢɟ ɨɲɢɛɤɢɋɬɚɧɞɚɪɬɧɚɹ ɨɛɪɚɛɨɬɤɚ 101 Ɂɚɩɪɨɫ ɩɪɢ ɨɬɫɭɬɫɬɜɢɢ ɡɚɞɚɱɧɢɤɚɁɚɧɟɫɟɧɢɟ ɧɨɦɟɪɚ ɜ Error 102 Ɉɲɢɛɤɚ ɱɬɟɧɢɹ ɡɚɞɚɱɧɢɤɚɁɚɧɟɫɟɧɢɟ ɧɨɦɟɪɚ ɜ Error 103 Ɉɲɢɛɤɚ ɡɚɩɢɫɢ ɡɚɞɚɱɧɢɤɚɁɚɧɟɫɟɧɢɟ ɧɨɦɟɪɚ ɜ Error 104 ɉɨɩɵɬɤɚ ɫɱɢɬɵɜɚɧɢɹ ɡɚɞɚɱɧɢɤɚ ɩɪɢ ɨɬɤɪɵɬɵɯ ɫɟɚɧɫɚɯ ɪɚɧɟɟ ɫɱɢɬɚɧɧɨ- Ɂɚɧɟɫɟɧɢɟ ɧɨɦɟɪɚ ɜ Error ɝɨ ɡɚɞɚɱɧɢɤɚ 105 Ɂɚɤɪɵɬɢɟ ɡɚɞɚɱɧɢɤɚ ɩɪɢ ɨɬɤɪɵɬɵɯ ɫɟɚɧɫɚɯɁɚɧɟɫɟɧɢɟ ɧɨɦɟɪɚ ɜ Error 106 ɇɟɞɨɩɭɫɬɢɦɵɣ ɤɨɞ ɨɩɟɪɚɰɢɢ ɩɪɢ ɨɬɤɪɵɬɢɢ ɫɟɚɧɫɚɁɚɧɟɫɟɧɢɟ ɧɨɦɟɪɚ ɜ Error 107 ɇɟɜɟɪɧɵɣ ɧɨɦɟɪ ɫɟɚɧɫɚɁɚɧɟɫɟɧɢɟ ɧɨɦɟɪɚ ɜ Error 108 ɩɟɪɟɯɨɞ ɡɚ ɤɨɧɟɱɧɭɸ ɝɪɚɧɢɰɭ ɬɟɤɭɳɟɣ ɜɵɛɨɪɤɢɂɝɧɨɪɢɪɭɟɬɫɹ 109 ɉɟɪɟɯɨɞ ɡɚ ɧɚɱɚɥɶɧɭɸ ɝɪɚɧɢɰɭ ɬɟɤɭɳɟɣ ɜɵɛɨɪɤɢɂɝɧɨɪɢɪɭɟɬɫɹ 110 ɇɟɜɟɪɧɵɣ ɬɢɩ ɜɟɤɬɨɪɚ ɜ ɡɚɩɪɨɫɟ Get Ɂɚɧɟɫɟɧɢɟ ɧɨɦɟɪɚ ɜ Error 111 ɉɨɩɵɬɤɚ ɱɬɟɧɢɹ ɞɨ ɢɥɢ ɩɨɫɥɟ ɬɟɤɭɳɟɣ ɜɵɛɨɪɤɢɁɚɧɟɫɟɧɢɟ ɧɨɦɟɪɚ ɜ Error 112 Ⱦɚɧɧɵɟ ɨɬɫɭɬɫɬɜɭɸɬɂɝɧɨɪɢɪɭɟɬɫɹ 113 ɇɟɜɟɪɧɵɣ ɬɢɩ ɜɟɤɬɨɪɚ ɜ ɡɚɩɪɨɫɟ Put Ɂɚɧɟɫɟɧɢɟ ɧɨɦɟɪɚ ɜ Error 114 ɇɟɜɟɪɧɚɹ ɨɩɟɪɚɰɢɹ ɨɤɪɚɫɤɢ ɩɪɢɦɟɪɚɁɚɧɟɫɟɧɢɟ ɧɨɦɟɪɚ ɜ Error
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4. ɉɪɟɞɨɛɪɚɛɨɬɱɢɤ Ⱦɚɧɧɚɹ ɝɥɚɜɚ ɩɨɫɜɹɳɟɧɚ ɤɨɦɩɨɧɟɧɬɭ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ. ȼ ɧɟɣ ɪɚɫɫɦɚɬɪɢɜɚɸɬɫɹ ɪɚɡɥɢɱɧɵɟ ɚɫɩɟɤɬɵ ɩɪɟɞɨɛɪɚɛɨɬɤɢ ɜɯɨɞɧɵɯ ɞɚɧɧɵɯ ɞɥɹ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ. ɋɭɳɟɫɬɜɭɟɬ ɦɧɨɠɟɫɬɜɨ ɪɚɡɥɢɱɧɵɯ ɜɢɞɨɜ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ (ɫɦ. ɝɥɚɜɭ «Ɉɩɢɫɚɧɢɟ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ»). Ɉɞɧɚɤɨ, ɞɥɹ ɛɨɥɶɲɢɧɫɬɜɚ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ɯɚɪɚɤɬɟɪɧɨ ɧɚɥɢɱɢɟ ɬɚɤɨɝɨ ɢɧɬɟɪɜɚɥɚ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ, ɜ ɩɪɟɞɟɥɚɯ ɤɨɬɨɪɨɝɨ ɫɢɝɧɚɥɵ ɪɚɡɥɢɱɢɦɵ. Ⱦɥɹ ɪɚɡɥɢɱɧɵɯ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ɷɬɢ ɢɧɬɟɪɜɚɥɵ ɪɚɡɥɢɱɧɵ. Ȼɨɥɶɲɢɧɫɬɜɨ ɪɚɛɨɬɚɸɳɢɯ ɫ ɧɟɣɪɨɧɧɵɦɢ ɫɟɬɹɦɢ ɩɪɟɤɪɚɫɧɨ ɨɫɜɟɞɨɦɥɟɧɵ ɨɛ ɷɬɨɦ ɢɯ ɫɜɨɣɫɬɜɟ, ɧɨ ɞɨ ɫɢɯ ɩɨɪ ɧɟ ɩɪɟɞɩɪɢɧɢɦɚɥɨɫɶ ɧɢɤɚɤɢɯ ɩɨɩɵɬɨɤ ɤɚɤɥɢɛɨ ɮɨɪɦɚɥɢɡɨɜɚɬɶ ɢɥɢ ɭɧɢɮɢɰɢɪɨɜɚɬɶ ɩɨɞɯɨɞɵ ɤ ɩɪɟɞɨɛɪɚɛɨɬɤɟ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ. ȼ ɞɚɧɧɨɣ ɝɥɚɜɟ ɞɚɧ ɨɞɢɧ ɢɡ ɜɨɡɦɨɠɧɵɯ ɮɨɪɦɚɥɢɡɦɨɜ ɷɬɨɣ ɡɚɞɚɱɢ. Ɂɚ ɪɚɦɤɚɦɢ ɪɚɫɫɦɨɬɪɟɧɢɹ ɨɫɬɚɥɚɫɶ ɩɪɟɞɨɛɪɚɛɨɬɤɚ ɝɪɚɮɢɱɟɫɤɨɣ ɢɧɮɨɪɦɚɰɢɢ. ɇɚɢɛɨɥɟɟ ɦɨɳɧɵɟ ɢ ɢɧɬɟɪɟɫɧɵɟ ɫɩɨɫɨɛɵ ɨɛɪɚɛɨɬɤɢ ɝɪɚɮɢɱɟɫɤɨɣ ɢɧɮɨɪɦɚɰɢɢ ɨɩɢɫɚɧɵ ɜ [90]
4.1 Ɉɩɢɫɚɧɢɟ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ ȼ ɷɬɨɣ ɱɚɫɬɢ ɝɥɚɜɵ ɛɭɞɭɬ ɨɩɢɫɚɧɵ ɪɚɡɥɢɱɧɵɟ ɜɢɞɵ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɢ ɫɩɨɫɨɛɵ ɢɯ ɩɪɟɞɨɛɪɚɛɨɬɤɢ. ȼ ɤɚɱɟɫɬɜɟ ɩɪɢɦɟɪɚ ɛɭɞɭɬ ɪɚɫɫɦɨɬɪɟɧɵ ɫɟɬɢ ɫ ɫɢɝɦɨɢɞɧɵɦɢ ɧɟɥɢɧɟɣɧɵɦɢ ɩɪɟɨɛɪɚɡɨɜɚɬɟɥɹɦɢ. Ɉɞɧɚɤɨ, ɨɩɢɫɵɜɚɟɦɵɟ ɫɩɨɫɨɛɵ ɩɪɟɞɨɛɪɚɛɨɬɤɢ ɩɪɢɦɟɧɢɦɵ ɞɥɹ ɫɟɬɟɣ ɫ ɩɪɨɢɡɜɨɥɶɧɵɦɢ ɧɟɥɢɧɟɣɧɵɦɢ ɩɪɟɨɛɪɚɡɨɜɚɬɟɥɹɦɢ. ȿɞɢɧɫɬɜɟɧɧɵɦ ɢɫɤɥɸɱɟɧɢɟɦ ɹɜɥɹɟɬɫɹ ɪɚɡɞɟɥ «Ɉɰɟɧɤɚ ɫɩɨɫɨɛɧɨɫɬɢ ɫɟɬɢ ɪɟɲɢɬɶ ɡɚɞɚɱɭ», ɤɨɬɨɪɵɣ ɩɪɢɦɟɧɢɦ ɬɨɥɶɤɨ ɞɥɹ ɫɟɬɟɣ ɫ ɧɟɥɢɧɟɣɧɵɦɢ ɩɪɟɨɛɪɚɡɨɜɚɬɟɥɹɦɢ, ɧɟɩɪɟɪɵɜɧɨ ɡɚɜɢɫɹɳɢɦɢ ɨɬ ɫɜɨɢɯ ɚɪɝɭɦɟɧɬɨɜ.
4.1.1 ɇɟɣɪɨɧ ɇɟɣɪɨɧɵ, ɢɫɩɨɥɶɡɭɟɦɵɟ ɜ ɛɨɥɶɲɢɧɫɬɜɟ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ, ɢɦɟɸɬ ɫɬɪɭɤɬɭɪɭ, ɩɪɢɜɟɞɟɧɧɭɸ ɧɚ ɪɢɫ. 1. ɇɚ ɪɢɫ. 1 ɢɫɩɨɥɶɡɨɜɚɧɵ ɫɥɟɞɭɸɳɢɟ ɨɛɨɡɧɚɱɟɧɢɹ: x – ɜɟɤɬɨɪ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɧɟɣɪɨɧɚ; a a – ɜɟɤɬɨɪ ɫɢɧɚɩɬɢɱɟɫɤɢɯ ɜɟɫɨɜ ɧɟɣɪɨɧɚ; y p S – ɜɯɨɞɧɨɣ ɫɭɦɦɚɬɨɪ ɧɟɣɪɨɧɚ; s
p = (a, x ) – ɜɵɯɨɞɧɨɣ ɫɢɝɧɚɥ ɜɯɨɞɧɨɝɨ ɫɭɦɦɚɬɨɪɚ; s – ɮɭɧɤɰɢɨɧɚɥɶɧɵɣ ɩɪɟɨɛɪɚɡɨɜɚɬɟɥɶ; y – ɜɵɯɨɞɧɨɣ ɫɢɝɧɚɥ ɧɟɣɪɨɧɚ.
Ɉɛɵɱɧɨ ɧɟɣɪɨɧɧɵɟ ɫɟɬɢ ɧɚɡɵɜɚɸɬ ɩɨ ɜɢɞɭ ɮɭɧɤɰɢɢ
S
x
Ɋɢɫ. 1. ɉɪɢɦɟɪ ɧɟɣɪɨɧɚ
s ( p) . ɏɨ-
S1: s ( p) = 1 (1 + exp( -cp)),
ɪɨɲɨ ɢɡɜɟɫɬɧɵ ɢ ɧɚɢɛɨɥɟɟ ɱɚɫɬɨ ɢɫɩɨɥɶɡɭɸɬɫɹ ɞɜɚ ɜɢɞɚ ɫɢɝɦɨɢɞɧɵɯ ɫɟɬɟɣ:
S2:
s ( p) = p (c + p ),
ɝɞɟ c - ɩɚɪɚɦɟɬɪ, ɧɚɡɵɜɚɟɦɵɣ «ɯɚɪɚɤɬɟɪɢɫɬɢɤɨɣ ɧɟɣɪɨɧɚ». Ɉɛɟ ɮɭɧɤɰɢɢ ɢɦɟɸɬ ɩɨɯɨɠɢɟ ɝɪɚɮɢɤɢ. Ʉɚɠɞɨɦɭ ɬɢɩɭ ɧɟɣɪɨɧɚ ɫɨɨɬɜɟɬɫɬɜɭɟɬ ɫɜɨɣ ɢɧɬɟɪɜɚɥ ɩɪɢɟɦɥɟɦɵɯ ɜɯɨɞɧɵɯ ɞɚɧɧɵɯ. Ʉɚɤ ɩɪɚɜɢɥɨ, ɷɬɨɬ ɞɢɚɩɚɡɨɧ ɥɢɛɨ ɫɨɜɩɚɞɚɟɬ ɫ ɞɢɚɩɚɡɨɧɨɦ ɜɵɞɚɜɚɟɦɵɯ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ (ɧɚɩɪɢɦɟɪ ɞɥɹ ɫɢɝɦɨɢɞɧɵɯ ɧɟɣɪɨɧɨɜ ɫ ɮɭɧɤɰɢɟɣ S1 ), ɥɢɛɨ ɹɜɥɹɟɬɫɹ ɨɛɴɟɞɢɧɟɧɢɟɦ ɞɢɚɩɚɡɨɧɚ ɜɵɞɚɜɚɟɦɵɯ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɢ
ɨɬɪɟɡɤɚ, ɫɢɦɦɟɬɪɢɱɧɨɝɨ ɟɦɭ ɨɬɧɨɫɢɬɟɥɶɧɨ ɧɭɥɹ (ɧɚɩɪɢɦɟɪ, ɞɥɹ ɫɢɝɦɨɢɞɧɵɯ ɧɟɣɪɨɧɨɜ ɫ ɮɭɧɤɰɢɟɣ ɗɬɨɬ ɞɢɚɩɚɡɨɧ ɛɭɞɟɦ ɨɛɨɡɧɚɱɚɬɶ ɤɚɤ
[a, b]
S 2 ),
4.1.2 Ɋɚɡɥɢɱɢɦɨɫɬɶ ɜɯɨɞɧɵɯ ɞɚɧɧɵɯ Ɉɱɟɜɢɞɧɨ, ɱɬɨ ɜɯɨɞɧɵɟ ɞɚɧɧɵɟ ɞɨɥɠɧɵ ɛɵɬɶ ɪɚɡɥɢɱɢɦɵ. ȼ ɞɚɧɧɨɦ ɪɚɡɞɟɥɟ ɛɭɞɭɬ ɩɪɢɜɟɞɟɧɵ ɫɨɨɛɪɚɠɟɧɢɹ, ɢɫɯɨɞɹ ɢɡ ɤɨɬɨɪɵɯ, ɫɥɟɞɭɟɬ ɜɵɛɢɪɚɬɶ ɞɢɚɩɚɡɨɧ ɜɯɨɞɧɵɯ ɞɚɧɧɵɯ. ɉɭɫɬɶ ɨɞɧɢɦ ɢɡ ɜɯɨɞɧɵɯ ɩɚɪɚɦɟɬɪɨɜ ɧɟɣɪɨɧɧɨɣ ɫɟɬɢ ɹɜɥɹɟɬɫɹ ɬɟɦɩɟɪɚɬɭɪɚ ɜ ɝɪɚɞɭɫɚɯ Ʉɟɥɶɜɢɧɚ. ȿɫɥɢ ɪɟɱɶ ɢɞɟɬ ɨ ɬɟɦɩɟɪɚɬɭɪɚɯ Ɍɚɛɥɢɰɚ 1 ȼɟɥɢɱɢɧɚ ɜɯɨɞɧɨɝɨ ɫɢɝɧɚɥɚ c = 01 . 250 1.0 275 1.0 300 1.0
CHAP4.DOC
ɇɟɣɪɨɧ ɬɢɩɚ S1
c = 0.5
c =1
c=2
1.0 1.0 1.0
1.0 1.0 1.0
1.0 1.0 1.0
53
ɇɟɣɪɨɧ ɬɢɩɚ
c = 01 . 0.99960 0.99964 0.99967
c = 0.5 0.99800 0.99819 0.99834
S2 c =1
c=2
0.99602 0.99638 0.99668
0.99206 0.99278 0.99338
ɛɥɢɡɤɢɯ ɤ ɧɨɪɦɚɥɶɧɨɣ, ɬɨ ɜɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɢɡɦɟɧɹɸɬɫɹ ɨɬ 250 ɞɨ 300 ɝɪɚɞɭɫɨɜ. ɉɭɫɬɶ ɫɢɝɧɚɥ ɩɨɞɚɟɬɫɹ ɩɪɹɦɨ ɧɚ ɧɟɣɪɨɧ (ɫɢɧɚɩɬɢɱɟɫɤɢɣ ɜɟɫ ɪɚɜɟɧ ɟɞɢɧɢɰɟ). ȼɵɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɧɟɣɪɨɧɨɜ ɫ ɪɚɡɥɢɱɧɵɦɢ ɩɚɪɚɦɟɬɪɚɦɢ ɩɪɢɜɟɞɟɧɵ ɜ ɬɚɛɥ. 1. ɋɨɜɟɪɲɟɧɧɨ ɨɱɟɜɢɞɧɨ, ɱɬɨ ɧɟɣɪɨɧɧɚɹ ɫɟɬɶ ɩɪɨɫɬɨ ɧɟɫɩɨɫɨɛɧɚ ɧɚɭɱɢɬɶɫɹ ɧɚɞɟɠɧɨ ɪɚɡɥɢɱɚɬɶ ɷɬɢ ɫɢɝɧɚɥɵ (ɟɫɥɢ ɜɨɨɛɳɟ ɫɩɨɫɨɛɧɚ ɧɚɭɱɢɬɶɫɹ ɢɯ ɪɚɡɥɢɱɚɬɶ!). ȿɫɥɢ ɢɫɩɨɥɶɡɨɜɚɬɶ ɧɟɣɪɨɧɵ ɫ ɜɯɨɞɧɵɦɢ ɫɢɧɚɩɫɚɦɢ, ɧɟ ɪɚɜɧɵɦɢ ɟɞɢɧɢɰɟ, ɬɨ ɧɟɣɪɨɧɧɚɹ ɫɟɬɶ ɫɦɨɠɟɬ ɨɬɦɚɫɲɬɚɛɢɪɨɜɚɬɶ ɜɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɬɚɤ, ɱɬɨɛɵ ɨɧɢ ɫɬɚɥɢ ɪɚɡɥɢɱɢɦɵ, ɧɨ ɩɪɢ ɷɬɨɦ ɛɭɞɟɬ ɡɚɞɟɣɫɬɜɨɜɚɧɚ ɬɨɥɶɤɨ ɱɚɫɬɶ ɞɢɚɩɚɡɨɧɚ ɩɪɢɟɦɥɟɦɵɯ ɜɯɨɞɧɵɯ ɞɚɧɧɵɯ - ɜɫɟ ɜɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɛɭɞɭɬ ɢɦɟɬɶ ɨɞɢɧ ɡɧɚɤ. Ʉɪɨɦɟ ɬɨɝɨ, ɜɫɟ ɩɨɞɚɜɚɟɦɵɟ ɫɢɝɧɚɥɵ ɛɭɞɭɬ ɡɚɧɢɦɚɬɶ ɥɢɲɶ ɦɚɥɭɸ ɱɚɫɬɶ ɷɬɨɝɨ ɞɢɚɩɚɡɨɧɚ. ɇɚɩɪɢɦɟɪ, ɟɫɥɢ ɦɵ ɨɬɦɚɫɲɬɚɛɢɪɭɟɦ ɬɟɦɩɟɪɚɬɭɪɵ ɬɚɤ, ɱɬɨɛɵ 300 ɫɨɨɬɜɟɬɫɬɜɨɜɚɥɚ ɜɟɥɢɱɢɧɚ ɫɭɦɦɚɪɧɨɝɨ ɜɯɨɞɧɨɝɨ ɫɢɝɧɚɥɚ ɪɚɜɧɚɹ 1 (ɜɟɥɢɱɢɧɚ ɜɯɨɞɧɨɝɨ ɫɢɧɚɩɫɚ ɪɚɜɧɚ 1/300), ɬɨ ɪɟɚɥɶɧɨ ɩɨɞɚɜɚɟɦɵɟ ɫɢɝɧɚɥɵ ɡɚɣɦɭɬ ɥɢɲɶ ɨɞɧɭ ɲɟɫɬɭɸ ɱɚɫɬɶ ɢɧɬɟɪɜɚɥɚ [0,1] ɢ ɨɞɧɭ ɞɜɟɧɚɞɰɚɬɭɸ ɢɧɬɟɪɜɚɥɚ [-1,1]. ɉɨɥɭɱɚɟɦɵɟ ɩɪɢ ɷɬɨɦ ɩɪɢ ɷɬɨɦ ɜɟɥɢɱɢɧɵ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɧɟɣɪɨɧɨɜ ɩɪɢɜɟɞɟɧɵ ɜ ɬɚɛɥ. 2. Ɍɚɛɥɢɰɚ 2 ȼɟɥɢɱɢɧɚ ɜɯɨɞɧɨɝɨ ɫɢɝɧɚɥɚ 250 (0.83) 275 (0.91) 300 (1.0)
ɇɟɣɪɨɧ ɬɢɩɚ S1
c = 01 . c = 0.5
c =1
c=2
ɇɟɣɪɨɧ ɬɢɩɚ
c = 01 . c = 0.5
S2 c =1
c=2
0.52074 0.60229 0.69636 0.84024 0.89286 0.62500 0.45455 0.29412 0.52273 0.61183 0.71300 0.86057 0.90164 0.64706 0.47826 0.31429 0.52498 0.62246 0.73106 0.88080 0.90909 0.66667 0.50000 0.33333
ɋɢɝɧɚɥɵ, ɩɪɢɜɟɞɟɧɧɵɟ ɜ ɬɚɛɥ. 2 ɪɚɡɥɢɱɚɸɬɫɹ ɧɚɦɧɨɝɨ ɫɢɥɶɧɟɟ ɫɨɨɬɜɟɬɫɬɜɭɸɳɢɯ ɫɢɝɧɚɥɨɜ ɢɡ ɬɚɛɥ. 1. Ɍɚɤɢɦ ɨɛɪɚɡɨɦ, ɧɟɨɛɯɨɞɢɦɨ ɡɚɪɚɧɟɟ ɩɨɡɚɛɨɬɢɬɶɫɹ ɨ ɦɚɫɲɬɚɛɢɪɨɜɚɧɢɢ ɢ ɫɞɜɢɝɟ ɫɢɝɧɚɥɨɜ, ɱɬɨɛɵ ɦɚɤɫɢɦɚɥɶɧɨ ɩɨɥɧɨ ɢɫɩɨɥɶɡɨɜɚɬɶ ɞɢɚɩɚɡɨɧ ɩɪɢɟɦɥɟɦɵɯ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ. Ɉɩɵɬ ɢɫɩɨɥɶɡɨɜɚɧɢɹ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ɫ ɜɯɨɞɧɵɦɢ ɫɢɧɚɩɫɚɦɢ ɫɜɢɞɟɬɟɥɶɫɬɜɭɟɬ ɨ ɬɨɦ, ɱɬɨ ɜ ɩɨɞɚɜɥɹɸɳɟɦ ɛɨɥɶɲɢɧɫɬɜɟ ɫɥɭɱɚɟɜ ɩɪɟɞɜɚɪɢɬɟɥɶɧɨɟ ɦɚɫɲɬɚɛɢɪɨɜɚɧɢɟ ɢ ɫɞɜɢɝ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɫɢɥɶɧɨ ɨɛɥɟɝɱɚɟɬ ɨɛɭɱɟɧɢɟ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ. ȿɫɥɢ ɡɚɪɚɧɟɟ ɩɪɨɢɡɜɟɫɬɢ ɨɩɟɪɚɰɢɢ ɦɚɫɲɬɚɛɢɪɨɜɚɧɢɹ ɢ ɫɞɜɢɝɚ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ, ɬɨ ɜɟɥɢɱɢɧɵ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɧɟɣɪɨɧɨɜ ɞɚɠɟ ɩɪɢ ɨɬɫɭɬɫɬɜɢɢ ɜɯɨɞɧɵɯ ɫɢɧɚɩɫɨɜ ɛɭɞɭɬ ɪɚɡɥɢɱɚɬɶɫɹ ɟɳɟ ɫɢɥɶɧɟɟ (ɫɦ. ɬɚɛɥ. 3). Ɍɚɛɥɢɰɚ 3 ȼɟɥɢɱɢɧɚ ɜɯɨɞɧɨɝɨ ɫɢɝɧɚɥɚ 250 (-1) 275 (0) 300 (1)
ɇɟɣɪɨɧ ɬɢɩɚ S1
c = 01 . c = 0.5
c =1
c=2
ɇɟɣɪɨɧ ɬɢɩɚ
c = 01 . c = 0.5
0.47502 0.37754 0.26894 0.11920 -0.9091 0.50000 0.50000 0.50000 0.50000 0.0000 0.52498 0.62246 0.73106 0.88080 0.9091
-0.6667 0.0000 0.6667
S2 c =1
c=2
-0.5000 -0.3333 0.0000 0.0000 0.5000 0.3333
ȼɟɥɢɱɢɧɭ ɞɢɚɩɚɡɨɧɚ ɪɚɡɥɢɱɢɦɵɯ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɦɨɠɧɨ ɨɩɪɟɞɟɥɹɬɶ ɪɚɡɥɢɱɧɵɦɢ ɫɩɨɫɨɛɚɦɢ. ɇɚ ɩɪɚɤɬɢɤɟ ɜ ɤɚɱɟɫɬɜɟ ɞɢɚɩɚɡɨɧɚ ɪɚɡɥɢɱɢɦɵɯ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɨɛɵɱɧɨ ɢɫɩɨɥɶɡɭɟɬɫɹ ɞɢɚɩɚɡɨɧ ɩɪɢɟɦɥɟɦɵɯ ɜɯɨɞɧɵɯ ɞɚɧɧɵɯ, ɢɫɯɨɞɹ ɢɡ ɬɨɝɨ ɫɨɨɛɪɚɠɟɧɢɹ, ɱɬɨ ɟɫɥɢ ɞɚɧɧɵɟ ɢɡ ɷɬɨɝɨ ɢɧɬɟɪɜɚɥɚ ɯɨɪɨɲɢ ɞɥɹ ɩɪɨɦɟɠɭɬɨɱɧɵɯ ɧɟɣɪɨɧɨɜ, ɬɨ ɨɧɢ ɯɨɪɨɲɢ ɢ ɞɥɹ ɜɯɨɞɧɵɯ. Ⱦɪɭɝɨɣ ɫɩɨɫɨɛ ɨɩɪɟɞɟɥɟɧɢɹ ɪɚɡɥɢɱɢɦɨɫɬɢ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɩɪɢɜɟɞɟɧ ɜ ɪɚɡɞɟɥɟ «Ɉɰɟɧɤɚ ɫɩɨɫɨɛɧɨɫɬɢ ɫɟɬɢ ɪɟɲɢɬɶ ɡɚɞɚɱɭ».
4.1.3 Ʉɥɚɫɫɢɮɢɤɚɰɢɹ ɤɨɦɩɨɧɟɧɬɨɜ ɜɯɨɞɧɵɯ ɞɚɧɧɵɯ ɂɧɮɨɪɦɚɰɢɹ ɩɨɫɬɭɩɚɟɬ ɤ ɧɟɣɪɨɧɧɨɣ ɫɟɬɢ ɜ ɜɢɞɟ ɧɚɛɨɪɚ ɨɬɜɟɬɨɜ ɧɚ ɧɟɤɨɬɨɪɵɣ ɫɩɢɫɨɤ ɜɨɩɪɨɫɨɜ. Ɇɨɠɧɨ ɜɵɞɟɥɢɬɶ ɬɪɢ ɨɫɧɨɜɧɵɯ ɬɢɩɚ ɨɬɜɟɬɨɜ (ɜɨɩɪɨɫɨɜ). 1. Ȼɢɧɚɪɧɵɣ ɩɪɢɡɧɚɤ (ɜɨɡɦɨɠɟɧ ɬɨɥɶɤɨ ɨɞɢɧ ɢɡ ɨɬɜɟɬɨɜ – ɢɫɬɢɧɚ ɢɥɢ ɥɨɠɶ). 2. Ʉɚɱɟɫɬɜɟɧɧɵɣ ɩɪɢɡɧɚɤ (ɩɪɢɧɢɦɚɟɬ ɤɨɧɟɱɧɨɟ ɱɢɫɥɨ ɡɧɚɱɟɧɢɣ). 3. ɑɢɫɥɨ. Ɉɬɜɟɬ ɬɢɩɚ ɤɚɱɟɫɬɜɟɧɧɵɣ ɩɪɢɡɧɚɤ - ɷɬɨ ɨɬɜɟɬ ɫ ɤɨɧɟɱɧɵɦ ɱɢɫɥɨɦ ɫɨɫɬɨɹɧɢɣ. ɉɪɢɱɟɦ ɧɟɥɶɡɹ ɜɜɟɫɬɢ ɨɫɦɵɫɥɟɧɧɨɟ ɪɚɫɫɬɨɹɧɢɟ ɦɟɠɞɭ ɫɨɫɬɨɹɧɢɹɦɢ. ɉɪɢɦɟɪɨɦ ɤɚɱɟɫɬɜɟɧɧɨɝɨ ɩɪɢɡɧɚɤɚ ɦɨɠɟɬ ɫɥɭɠɢɬɶ ɫɨɫɬɨɹɧɢɟ ɛɨɥɶɧɨɝɨ - ɬɹɠɟɥɵɣ, ɫɪɟɞɧɢɣ, ɥɟɝɤɢɣ. Ⱦɟɣɫɬɜɢɬɟɥɶɧɨ, ɧɟɥɶɡɹ ɫɤɚɡɚɬɶ, ɱɬɨ ɪɚɫɫɬɨɹɧɢɟ ɨɬ ɥɟɝɤɨɝɨ ɛɨɥɶɧɨɝɨ ɞɨ ɫɪɟɞɧɟɝɨ ɛɨɥɶɲɟ, ɦɟɧɶɲɟ ɢɥɢ ɪɚɜɧɨ ɪɚɫɫɬɨɹɧɢɸ ɨɬ ɫɪɟɞɧɟɝɨ ɛɨɥɶɧɨɝɨ ɞɨ ɬɹɠɟɥɨɝɨ. ȼɫɟ ɤɚɱɟɫɬɜɟɧɧɵɟ ɩɪɢɡɧɚɤɢ ɦɨɠɧɨ ɜ ɫɜɨɸ ɨɱɟɪɟɞɶ ɪɚɡɛɢɬɶ ɧɚ ɬɪɢ ɤɥɚɫɫɚ. 1. ɍɩɨɪɹɞɨɱɟɧɧɵɟ ɩɪɢɡɧɚɤɢ. 2. ɇɟɭɩɨɪɹɞɨɱɟɧɧɵɟ ɩɪɢɡɧɚɤɢ. 3. ɑɚɫɬɢɱɧɨ ɭɩɨɪɹɞɨɱɟɧɧɵɟ ɩɪɢɡɧɚɤɢ.
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ɍɩɨɪɹɞɨɱɟɧɧɵɦ ɩɪɢɡɧɚɤɨɦ ɧɚɡɵɜɚɟɬɫɹ ɬɚɤɨɣ ɩɪɢɡɧɚɤ, ɞɥɹ ɥɸɛɵɯ ɞɜɭɯ ɫɨɫɬɨɹɧɢɣ ɤɨɬɨɪɨɝɨ ɦɨɠɧɨ ɫɤɚɡɚɬɶ, ɱɬɨ ɨɞɧɨ ɢɡ ɧɢɯ ɩɪɟɞɲɟɫɬɜɭɟɬ ɞɪɭɝɨɦɭ. Ɍɨɬ ɮɚɤɬ, ɱɬɨ ɫɨɫɬɨɹɧɢɟ x ɩɪɟɞɲɟɫɬɜɭɟɬ ɫɨɫɬɨɹɧɢɸ y , ɛɭɞɟɦ ɨɛɨɡɧɚɱɚɬɶ ɫɥɟɞɭɸɳɢɦ ɨɛɪɚɡɨɦ – x < y . ɉɪɢɦɟɪɨɦ ɭɩɨɪɹɞɨɱɟɧɧɨɝɨ ɩɪɢɡɧɚɤɚ ɦɨɠɟɬ ɫɥɭɠɢɬɶ ɫɨɫɬɨɹɧɢɟ ɛɨɥɶɧɨɝɨ. Ⱦɟɣɫɬɜɢɬɟɥɶɧɨ, ɜɫɟ ɫɨɫɬɨɹɧɢɹ ɦɨɠɧɨ ɭɩɨɪɹɞɨɱɢɬɶ ɩɨ ɬɹɠɟɫɬɢ ɡɚɛɨɥɟɜɚɧɢɹ: ɥɟɝɤɢɣ ɛɨɥɶɧɨɣ < ɫɪɟɞɧɢɣ ɛɨɥɶɧɨɣ < ɬɹɠɟɥɵɣ ɛɨɥɶɧɨɣ ɉɪɢɡɧɚɤ ɧɚɡɵɜɚɸɬ ɧɟɭɩɨɪɹɞɨɱɟɧɧɵɦ, ɟɫɥɢ ɧɢɤɚɤɢɟ ɞɜɚ ɫɨɫɬɨɹɧɢɹ ɧɟɥɶɡɹ ɫɜɹɡɚɬɶ ɟɫɬɟɫɬɜɟɧɧɵɦ ɜ ɤɨɧɬɟɤɫɬɟ ɡɚɞɚɱɢ ɨɬɧɨɲɟɧɢɟɦ ɩɨɪɹɞɤɚ. ɉɪɢɦɟɪɨɦ ɧɟɭɩɨɪɹɞɨɱɟɧɧɨɝɨ ɩɪɢɡɧɚɤɚ ɦɨɠɟɬ ɫɥɭɠɢɬɶ ɨɬɜɟɬ ɧɚ ɜɨɩɪɨɫ "ȼɚɲ ɥɸɛɢɦɵɣ ɰɜɟɬ?". ɉɪɢɡɧɚɤ ɧɚɡɵɜɚɟɬɫɹ ɱɚɫɬɢɱɧɨ ɭɩɨɪɹɞɨɱɟɧɧɵɦ, ɟɫɥɢ ɞɥɹ ɤɚɠɞɨɝɨ ɫɨɫɬɨɹɧɢɹ ɫɭɳɟɫɬɜɭɟɬ ɞɪɭɝɨɟ ɫɨɫɬɨɹɧɢɟ, ɫ ɤɨɬɨɪɵɦ ɨɧɨ ɫɜɹɡɚɧɨ ɨɬɧɨɲɟɧɢɟɦ ɩɨɪɹɞɤɚ. ɉɪɢɦɟɪɨɦ ɱɚɫɬɢɱɧɨ ɭɩɨɪɹɞɨɱɟɧɧɨɝɨ ɩɪɢɡɧɚɤɚ ɹɜɥɹɟɬɫɹ ɨɬɜɟɬ ɧɚ ɜɨɩɪɨɫ "Ʉɚɤɨɣ ɰɜɟɬ ȼɵ ɜɢɞɢɬɟ ɧɚ ɷɤɪɚɧɟ ɦɨɧɢɬɨɪɚ?", ɩɪɟɫɥɟɞɭɸɳɢɣ ɰɟɥɶ ɨɩɪɟɞɟɥɟɧɢɟ ɜɨɫɩɪɢɢɦɱɢɜɨɫɬɢ ɤ ɢɧɬɟɧɫɢɜɧɨɫɬɹɦ ɨɫɧɨɜɧɵɯ ɰɜɟɬɨɜ. Ⱦɟɣɫɬɜɢɬɟɥɶɧɨ, ɜɫɟ ɦɧɨɠɟɫɬɜɨ ɢɡ ɲɟɫɬɧɚɞɰɚɬɢ ɫɨɫɬɨɹɧɢɣ ɪɚɡɛɢɜɚɟɬɫɹ ɧɚ ɧɟɫɤɨɥɶɤɨ ɰɟɩɨɱɟɤ: ɑɟɪɧɵɣ < ɋɢɧɢɣ < Ƚɨɥɭɛɨɣ < Ȼɟɥɵɣ; ɑɟɪɧɵɣ < Ʉɪɚɫɧɵɣ < əɪɤɨ ɤɪɚɫɧɵɣ < Ȼɟɥɵɣ; ɑɟɪɧɵɣ < Ɂɟɥɟɧɵɣ < əɪɤɨ ɡɟɥɟɧɵɣ < Ȼɟɥɵɣ; ɑɟɪɧɵɣ < Ɏɢɨɥɟɬɨɜɵɣ < əɪɤɨ ɮɢɨɥɟɬɨɜɵɣ < Ȼɟɥɵɣ ɢ ɬ.ɞ. Ɉɞɧɚɤɨ, ɦɟɠɞɭ ɫɨɫɬɨɹɧɢɹɦɢ ɋɢɧɢɣ ɢ Ʉɪɚɫɧɵɣ ɨɬɧɨɲɟɧɢɹ ɩɨɪɹɞɤɚ ɧɟɬ. ɂɡɜɟɫɬɧɨ, ɱɬɨ ɥɸɛɨɣ ɱɚɫɬɢɱɧɨ ɭɩɨɪɹɞɨɱɟɧɧɵɣ ɩɪɢɡɧɚɤ ɦɨɠɧɨ ɩɪɟɞɫɬɚɜɢɬɶ ɜ ɜɢɞɟ ɤɨɦɛɢɧɚɰɢɢ ɧɟɫɤɨɥɶɤɢɯ ɭɩɨɪɹɞɨɱɟɧɧɵɯ ɢ ɧɟɭɩɨɪɹɞɨɱɟɧɧɵɯ ɩɪɢɡɧɚɤɨɜ. Ɍɚɤ, ɪɚɫɫɦɨɬɪɟɧɧɵɣ ɜɵɲɟ ɱɚɫɬɢɱɧɨ ɭɩɨɪɹɞɨɱɟɧɧɵɣ ɩɪɢɡɧɚɤ ɪɚɫɩɚɞɚɟɬɫɹ ɧɚ ɬɪɢ ɭɩɨɪɹɞɨɱɟɧɧɵɯ ɩɪɢɡɧɚɤɚ: ɢɧɬɟɧɫɢɜɧɨɫɬɶ ɫɢɧɟɝɨ, ɤɪɚɫɧɨɝɨ ɢ ɡɟɥɟɧɨɝɨ ɰɜɟɬɨɜ. Ʉɚɠɞɵɣ ɢɡ ɷɬɢɯ ɩɪɢɡɧɚɤɨɜ ɹɜɥɹɟɬɫɹ ɭɩɨɪɹɞɨɱɟɧɧɵɦ (ɰɟɩɨɱɤɢ ɩɨɪɹɞɤɚ ɞɥɹ ɷɬɢɯ ɩɪɢɡɧɚɤɨɜ ɩɪɢɜɟɞɟɧɵ ɜ ɩɟɪɜɵɯ ɬɪɟɯ ɫɬɪɨɱɤɚɯ ɪɚɫɫɦɨɬɪɟɧɢɹ ɩɪɢɦɟɪɚ). Ʉɚɠɞɨɟ ɫɨɫɬɨɹɧɢɟ ɢɫɯɨɞɧɨɝɨ ɤɚɱɟɫɬɜɟɧɧɨɝɨ ɩɪɢɡɧɚɤɚ ɨɩɢɫɵɜɚɟɬɫɹ ɬɪɨɣɤɨɣ ɫɨɫɬɨɹɧɢɣ ɩɨɥɭɱɟɧɧɵɯ ɤɚɱɟɫɬɜɟɧɧɵɯ ɩɪɢɡɧɚɤɨɜ. Ɍɚɤ, ɧɚɩɪɢɦɟɪ, ɫɨɫɬɨɹɧɢɟ Ɏɢɨɥɟɬɨɜɵɣ ɨɩɢɫɵɜɚɟɬɫɹ ɜ ɜɢɞɟ (ɋɢɧɢɣ, Ʉɪɚɫɧɵɣ, ɑɟɪɧɵɣ). ɂɫɯɨɞɹ ɢɡ ɜɵɲɟɫɤɚɡɚɧɧɨɝɨ, ɞɚɥɟɟ ɛɭɞɟɬ ɪɚɫɫɦɨɬɪɟɧɨ ɬɨɥɶɤɨ ɤɨɞɢɪɨɜɚɧɢɟ ɭɩɨɪɹɞɨɱɟɧɧɵɯ ɢ ɧɟɭɩɨɪɹɞɨɱɟɧɧɵɯ ɩɪɢɡɧɚɤɨɜ.
4.1.4 Ʉɨɞɢɪɨɜɚɧɢɟ ɛɢɧɚɪɧɵɯ ɩɪɢɡɧɚɤɨɜ Ȼɢɧɚɪɧɵɟ ɩɪɢɡɧɚɤɢ ɯɚɪɚɤɬɟɪɢɡɭɸɬɫɹ ɧɚɥɢɱɢɟɦ ɬɨɥɶɤɨ ɞɜɭɯ ɫɨɫɬɨɹɧɢɣ – ɢɫɬɢɧɚ ɢ ɥɨɠɶ. Ɉɞɧɚɤɨ ɞɚɠɟ ɬɚɤɢɟ ɩɪɨɫɬɵɟ ɞɚɧɧɵɟ ɦɨɝɭɬ ɢɦɟɬɶ ɞɜɚ ɪɚɡɧɵɯ ɫɦɵɫɥɚ. Ɂɧɚɱɟɧɢɟ ɢɫɬɢɧɚ ɨɡɧɚɱɚɟɬ ɧɚɥɢɱɢɟ ɭ ɨɩɢɫɵɜɚɟɦɨɝɨ ɨɛɴɟɤɬɚ ɤɚɤɨɝɨ-ɥɢɛɨ ɫɜɨɣɫɬɜɚ. Ⱥ ɨɬɜɟɬ ɥɨɠɶ ɦɨɠɟɬ ɨɡɧɚɱɚɬɶ ɥɢɛɨ (1) ɨɬɫɭɬɫɬɜɢɟ ɷɬɨɝɨ ɫɜɨɣɫɬɜɚ, ɥɢɛɨ (2) ɧɚɥɢɱɢɟ ɞɪɭɝɨɝɨ ɫɜɨɣɫɬɜɚ. ȼ ɡɚɜɢɫɢɦɨɫɬɢ ɨɬ ɫɦɵɫɥɨɜɨɣ ɧɚɝɪɭɡɤɢ ɡɧɚɱɟɧɢɹ ɥɨɠɶ, ɢ ɭɱɢɬɵɜɚɹ
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ɡɚɞɚɧɧɵɣ ɞɢɚɩɚɡɨɧ a, b , ɪɟɤɨɦɟɧɞɭɟɦɵɟ ɫɩɨɫɨɛɵ ɤɨɞɢɪɨɜɚɧɢɹ ɛɢɧɚɪɧɨɝɨ ɩɪɢɡɧɚɤɚ ɩɪɢɜɟɞɟɧɵ ɜ ɬɚɛɥ. 4. Ɍɚɛɥɢɰɚ 4 Ʉɨɞɢɪɨɜɚɧɢɟ ɛɢɧɚɪɧɨɝɨ ɩɪɢɡɧɚɤɚ ɋɦɵɫɥ ɡɧɚɱɟɧɢɹ ɥɨɠɶȼɟɥɢɱɢɧɚ ɜɯɨɞɧɨɝɨ ɫɢɝɧɚɥɚ ɞɥɹ ɡɧɚɱɟɧɢɹ ɩɪɢɡɧɚɤɚ ɂɫɬɢɧɚɅɨɠɶ a Ɉɬɫɭɬɫɬɜɢɟ ɡɚɞɚɧɧɨɝɨ ɫɜɨɣɫɬɜɚ ɩɪɢ b = 0 0 Ɉɬɫɭɬɫɬɜɢɟ ɡɚɞɚɧɧɨɝɨ ɫɜɨɣɫɬɜɚ ɩɪɢ b ¹ 0 b 0 ɇɚɥɢɱɢɟ ɞɪɭɝɨɝɨ ɫɜɨɣɫɬɜɚ a b
4.1.5 Ʉɨɞɢɪɨɜɚɧɢɟ ɧɟɭɩɨɪɹɞɨɱɟɧɧɵɯ ɤɚɱɟɫɬɜɟɧɧɵɯ ɩɪɢɡɧɚɤɨɜ ɉɨɫɤɨɥɶɤɭ ɧɢɤɚɤɢɟ ɞɜɚ ɫɨɫɬɨɹɧɢɹ ɧɟɭɩɨɪɹɞɨɌɚɛɥɢɰɚ 5. ɱɟɧɧɨɝɨ ɩɪɢɡɧɚɤɚ ɧɟ ɫɜɹɡɚɧɵ ɨɬɧɨɲɟɧɢɟɦ ɩɨɪɹɞɤɚ, ɬɨ Ʉɨɞɢɪɨɜɚɧɢɟ ɧɟɭɩɨɪɹɞɨɱɟɧɧɨɝɨ ɛɵɥɨ ɛɵ ɧɟɪɚɡɭɦɧɵɦ ɤɨɞɢɪɨɜɚɬɶ ɢɯ ɪɚɡɧɵɦɢ ɜɟɥɢɱɢɧɚɤɚɱɟɫɬɜɟɧɧɨɝɨ ɩɪɢɡɧɚɤɚ ɦɢ ɨɞɧɨɝɨ ɜɯɨɞɧɨɝɨ ɫɢɝɧɚɥɚ ɧɟɣɪɨɧɧɨɣ ɫɟɬɢ. ɉɨɷɬɨɦɭ, ɋɨɫɬɨɹɧɢɟȼɟɤɬɨɪ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɞɥɹ ɤɨɞɢɪɨɜɚɧɢɹ ɤɚɱɟɫɬɜɟɧɧɵɯ ɩɪɢɡɧɚɤɨɜ ɪɟɤɨɦɟɧɞɭɟɬa1 ɫɹ ɢɫɩɨɥɶɡɨɜɚɬɶ ɫɬɨɥɶɤɨ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ, ɫɤɨɥɶɤɨ (b, a , a ,K, a ) ɫɨɫɬɨɹɧɢɣ ɭ ɷɬɨɝɨ ɤɚɱɟɫɬɜɟɧɧɨɝɨ ɩɪɢɡɧɚɤɚ. Ʉɚɠɞɵɣ a2 ( a , b , a ,K , a ) ɜɯɨɞɧɨɣ ɫɢɝɧɚɥ ɫɨɨɬɜɟɬɫɬɜɭɟɬ ɨɩɪɟɞɟɥɟɧɧɨɦɭ ɫɨɫɬɨɹɧɢɸ. Ɍɚɤ ɟɫɥɢ ɧɚɛɨɪ ɜɫɟɯ ɫɨɫɬɨɹɧɢɣ ɪɚɫɫɦɚɬɪɢɜɚɟɦɨɝɨ an ( a , a ,K , a , b ) ɩɪɢɡɧɚɤɚ ɨɛɨɡɧɚɱɢɬɶ ɱɟɪɟɡ a 1 ,a 2 ,K,a n , ɬɨ ɪɟɤɨɦɟɧɞɭɟɦɚɹ ɬɚɛɥɢɰɚ ɤɨɞɢɪɨɜɤɢ ɢɦɟɟɬ ɜɢɞ, ɩɪɢɜɟɞɟɧɧɵɣ ɜ ɬɚɛɥ. 5.
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4.1.6 Ʉɨɞɢɪɨɜɚɧɢɟ ɭɩɨɪɹɞɨɱɟɧɧɵɯ ɱɚɫɬɧɵɯ ɩɪɢɡɧɚɤɨɜ ɍɩɨɪɹɞɨɱɟɧɧɵɟ ɱɚɫɬɧɵɟ ɩɪɢɡɧɚɤɢ, ɜ ɨɬɥɢɱɢɟ ɨɬ ɧɟɭɩɨɪɹɞɨɱɟɧɧɵɯ, ɢɦɟɸɬ ɨɬɧɨɲɟɧɢɟ ɩɨɪɹɞɤɚ ɦɟɠɞɭ ɫɨɫɬɨɹɧɢɹɦɢ. Ɉɞɧɚɤɨ ɤɨɞɢɪɨɜɚɧɢɟ ɢɯ ɪɚɡɧɵɦɢ ɡɧɚɱɟɧɢɹɦɢ ɨɞɧɨɝɨ ɜɯɨɞɧɨɝɨ ɫɢɝɧɚɥɚ ɧɟɪɚɡɭɦɧɨ ɢɡ-ɡɚ ɬɨɝɨ, ɱɬɨ ɪɚɫɫɬɨɹɧɢɟ ɦɟɠɞɭ ɫɨɫɬɨɹɧɢɹɦɢ ɧɟ ɨɩɪɟɞɟɥɟɧɨ, ɚ ɬɚɤɨɟ ɤɨɞɢɪɨɜɚɧɢɟ ɷɬɢ ɪɚɫɫɬɨɹɧɢɹ ɡɚɞɚɟɬ ɹɜɧɵɦ ɨɛɪɚɡɨɦ. ɉɨɷɬɨɦɭ, ɭɩɨɪɹɞɨɱɟɧɧɵɟ ɱɚɫɬɧɵɟ ɩɪɢɡɧɚɤɢ ɪɟɤɨɦɟɧɞɭɟɬɫɹ ɤɨɞɢɪɨɜɚɬɶ ɜ ɜɢɞɟ ɫɬɨɥɶɤɢɯ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ, ɫɤɨɥɶɤɨ ɫɨɫɬɨɹɧɢɣ ɭ ɩɪɢɡɧɚɤɚ. ɇɨ, ɜ ɨɬɥɢɱɢɟ ɨɬ ɧɟɭɩɨɪɹɞɨɱɟɧɧɵɯ ɩɪɢɡɧɚɤɨɜ, ɧɚɤɚɩɥɢɜɚɬɶ ɱɢɫɥɨ ɫɢɝɧɚɥɨɜ ɫ ɦɚɤɫɢɦɚɥɶɧɵɦ ɡɧɚɱɟɧɢɟɦ. Ⱦɥɹ ɫɥɭɱɚɹ, ɤɨɝɞɚ ɜɫɟ ɫɨɫɬɨɹɧɢɹ ɨɛɨɡɧɚɱɟɧɵ ɱɟɪɟɡ a 1 < a 2
Ɍɚɛɥɢɰɚ 6. Ʉɨɞɢɪɨɜɚɧɢɟ ɭɩɨɪɹɞɨɱɟɧɧɨɝɨ ɤɚɱɟɫɬɜɟɧɧɨɝɨ ɩɪɢɡɧɚɤɚ ɋɨɫɬɨɹɧɢɟȼɟɤɬɨɪ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ
a1 a2 an
(b, a , a ,K, a ) (b, b, a ,K, a ) (b,b,K, b, b)
4.1.7 ɑɢɫɥɨɜɵɟ ɩɪɢɡɧɚɤɢ ɉɪɢ ɩɪɟɞɨɛɪɚɛɨɬɤɟ ɱɢɫɥɟɧɧɵɯ ɫɢɝɧɚɥɨɜ ɧɟɨɛɯɨɞɢɦɨ ɭɱɢɬɵɜɚɬɶ ɫɨɞɟɪɠɚɬɟɥɶɧɨɟ ɡɧɚɱɟɧɢɟ ɩɪɢɡɧɚɤɚ, ɪɚɫɩɨɥɨɠɟɧɢɟ ɡɧɚɱɟɧɢɣ ɩɪɢɡɧɚɤɚ ɜ ɢɧɬɟɪɜɚɥɟ ɡɧɚɱɟɧɢɣ, ɬɨɱɧɨɫɬɶ ɢɡɦɟɪɟɧɢɹ ɡɧɚɱɟɧɢɣ ɩɪɢɡɧɚɤɚ. ɉɪɨɞɟɦɨɧɫɬɪɢɪɭɟɦ ɷɬɨ ɧɚ ɩɪɢɦɟɪɚɯ. ɋɨɞɟɪɠɚɬɟɥɶɧɨɟ ɡɧɚɱɟɧɢɟ ɩɪɢɡɧɚɤɚ. ȿɫɥɢ ɜɯɨɞɧɵɦɢ ɞɚɧɧɵɦɢ ɫɟɬɢ ɹɜɥɹɟɬɫɹ ɭɝɨɥ ɦɟɠɞɭ ɞɜɭɦɹ ɧɚɩɪɚɜɥɟɧɢɹɦɢ, ɧɚɩɪɢɦɟɪ, ɧɚɩɪɚɜɥɟɧɢɟ ɜɟɬɪɚ, ɬɨ ɧɢ ɜ ɤɨɟɦ ɫɥɭɱɚɟ ɧɟ ɫɥɟɞɭɟɬ ɩɨɞɚɜɚɬɶ ɧɚ ɜɯɨɞ ɫɟɬɢ ɡɧɚɱɟɧɢɟ ɭɝɥɚ (ɧɟ ɜɚɠɧɨ ɜ ɝɪɚɞɭɫɚɯ ɢɥɢ ɪɚɞɢɚɧɚɯ). Ɍɚɤɚɹ ɩɨɞɚɱɚ ɩɪɢɜɟɞɟɬ ɤ ɧɟɨɛɯɨɞɢɦɨɫɬɢ "ɭɹɫɧɟɧɢɹ" ɫɟɬɶɸ ɬɨɝɨ ɮɚɤɬɚ, ɱɬɨ 0 ɝɪɚɞɭɫɨɜ ɢ 360 ɝɪɚɞɭɫɨɜ ɨɞɧɨ ɢ ɬɨɠɟ. Ɋɚɡɭɦɧɟɟ ɜɵɝɥɹɞɢɬ ɩɨɞɚɱɚ ɜ ɤɚɱɟɫɬɜɟ ɜɯɨɞɧɵɯ ɞɚɧɧɵɯ ɫɢɧɭɫɚ ɢ ɤɨɫɢɧɭɫɚ ɷɬɨɝɨ ɭɝɥɚ. ɑɢɫɥɨ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɫɟɬɢ ɭɜɟɥɢɱɢɜɚɟɬɫɹ, ɧɨ ɡɚɬɨ ɛɥɢɡɤɢɟ ɡɧɚɱɟɧɢɹ ɩɪɢɡɧɚɤɚ ɤɨɞɢɪɭɸɬɫɹ ɛɥɢɡɤɢɦɢ ɜɯɨɞɧɵɦɢ ɫɢɝɧɚɥɚɦɢ. Ɍɨɱɧɨɫɬɶ ɢɡɦɟɪɟɧɢɹ ɩɪɢɡɧɚɤɚ. Ɍɚɤ ɜ ɦɟɬɟɨɪɨɥɨɝɢɢ ɢɫɩɨɥɶɡɭɟɬɫɹ ɜɫɟɝɨ ɜɨɫɟɦɶ ɧɚɩɪɚɜɥɟɧɢɣ ɜɟɬɪɚ. Ɂɧɚɱɢɬ, ɩɪɢ ɩɨɞɚɱɟ ɜɯɨɞɧɨɝɨ ɫɢɝɧɚɥɚ ɫɟɬɢ ɧɟɨɛɯɨɞɢɦɨ ɩɨɞɚɜɚɬɶ ɧɟ ɭɝɨɥ, ɚ ɜɫɟɝɨ ɥɢɲɶ ɢɧɮɨɪɦɚɰɢɸ ɨ ɬɨɦ, ɜ ɤɚɤɨɣ ɢɡ ɜɨɫɶɦɢ ɫɟɤɬɨɪɨɜ ɷɬɨɬ ɭɝɨɥ ɩɨɩɚɞɚɟɬ. ɇɨ ɬɨɝɞɚ ɢɦɟɟɬ ɫɦɵɫɥ ɪɚɫɫɦɚɬɪɢɜɚɬɶ ɧɚɩɪɚɜɥɟɧɢɟ ɜɟɬɪɚ ɧɟ ɤɚɤ ɱɢɫɥɨɜɨɣ ɩɚɪɚɦɟɬɪ, ɚ ɤɚɤ ɧɟɭɩɨɪɹɞɨɱɟɧɧɵɣ ɤɚɱɟɫɬɜɟɧɧɵɣ ɩɪɢɡɧɚɤ ɫ ɜɨɫɟɦɶɸ ɫɨɫɬɨɹɧɢɹɦɢ. Ɋɚɫɩɨɥɨɠɟɧɢɟ ɡɧɚɱɟɧɢɣ ɩɪɢɡɧɚɤɚ ɜ ɢɧɬɟɪɜɚɥɟ ɡɧɚɱɟɧɢɣ. ɋɥɟɞɭɟɬ ɪɚɫɫɦɨɬɪɟɬɶ ɜɨɩɪɨɫ ɨ ɪɚɜɧɨɡɧɚɱɧɨɫɬɢ ɢɡɦɟɧɟɧɢɹ ɡɧɚɱɟɧɢɹ ɩɪɢɡɧɚɤɚ ɧɚ ɧɟɤɨɬɨɪɭɸ ɜɟɥɢɱɢɧɭ ɜ ɪɚɡɧɵɯ ɱɚɫɬɹɯ ɢɧɬɟɪɜɚɥɚ ɡɧɚɱɟɧɢɣ ɩɪɢɡɧɚɤɚ. Ʉɚɤ ɩɪɚɜɢɥɨ, ɷɬɨ ɫɜɹɡɚɧɨ ɫ ɤɨɫɜɟɧɧɵɦɢ ɢɡɦɟɪɟɧɢɹɦɢ (ɜɦɟɫɬɨ ɨɞɧɨɣ ɜɟɥɢɱɢɧɵ ɢɡɦɟɪɹɟɬɫɹ ɞɪɭɝɚɹ). ɇɚɩɪɢɦɟɪ, ɫɢɥɚ ɩɪɢɬɹɠɟɧɢɹ ɞɜɭɯ ɧɟɛɟɫɧɵɯ ɬɟɥ ɩɪɢ ɭɫɥɨɜɢɢ ɩɨɫɬɨɹɧɫɬɜɚ ɦɚɫɫɵ ɨɞɧɨɡɧɚɱɧɨ ɯɚɪɚɤɬɟɪɢɡɭɟɬɫɹ ɪɚɫɫɬɨɹɧɢɟɦ ɦɟɠɞɭ ɧɢɦɢ. ɉɭɫɬɶ ɪɚɫɫɦɚɬɪɢɜɚɸɬɫɹ ɪɚɫɫɬɨɹɧɢɹ ɨɬ 1 ɞɨ 100 ɦɟɬɪɨɜ. Ʌɟɝɤɨ ɩɨɧɹɬɶ, ɱɬɨ ɩɪɢ ɢɡɦɟɧɟɧɢɢ ɪɚɫɫɬɨɹɧɢɹ ɫ 1 ɞɨ 2 ɦɟɬɪɨɜ, ɫɢɥɚ ɩɪɢɬɹɠɟɧɢɹ ɢɡɦɟɧɢɬɫɹ ɜ ɱɟɬɵɪɟ ɪɚɡɚ, ɚ ɩɪɢ ɢɡɦɟɧɟɧɢɢ ɫ 99 ɞɨ 100 ɦɟɬɪɨɜ – ɜ 1.02 ɪɚɡɚ. ɋɥɟɞɨɜɚɬɟɥɶɧɨ, ɜɦɟɫɬɨ ɩɨɞɚɱɢ ɪɚɫɫɬɨɹɧɢɹ ɫɥɟɞɭɟɬ ɩɨɞɚɜɚɬɶ 2 ɨɛɪɚɬɧɵɣ ɤɜɚɞɪɚɬ ɪɚɫɫɬɨɹɧɢɹ c¢ = 1 c .
4.1.8 ɉɪɨɫɬɟɣɲɚɹ ɩɪɟɞɨɛɪɚɛɨɬɤɚ ɱɢɫɥɨɜɵɯ ɩɪɢɡɧɚɤɨɜ Ʉɚɤ ɭɠɟ ɨɬɦɟɱɚɥɨɫɶ ɜ ɪɚɡɞɟɥɟ «Ɋɚɡɥɢɱɢɦɨɫɬɶ ɜɯɨɞɧɵɯ ɞɚɧɧɵɯ» ɱɢɫɥɨɜɵɟ ɫɢɝɧɚɥɵ ɪɟɤɨɦɟɧɞɭɟɬɫɹ ɦɚɫɲɬɚɛɢɪɨɜɚɬɶ ɢ ɫɞɜɢɝɚɬɶ ɬɚɤ, ɱɬɨɛɵ ɜɟɫɶ ɞɢɚɩɚɡɨɧ ɡɧɚɱɟɧɢɣ ɩɨɩɚɞɚɥ ɜ ɞɢɚɩɚɡɨɧ ɩɪɢɟɦɥɟɦɵɯ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ. ɗɬɚ ɩɪɟɞɨɛɪɚɛɨɬɤɚ ɩɪɨɫɬɚ ɢ ɡɚɞɚɟɬɫɹ ɫɥɟɞɭɸɳɟɣ ɮɨɪɦɭɥɨɣ:
(c - cmin )(b - a) + a , (1) (cmax - cmin ) ɝɞɟ [ a, b] - ɞɢɚɩɚɡɨɧ ɩɪɢɟɦɥɟɦɵɯ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ, [ cmin , cmax ] – ɞɢɚɩɚɡɨɧ ɡɧɚɱɟɧɢɣ ɩɪɢɡɧɚɤɚ c , c¢ c¢ =
– ɩɪɟɞɨɛɪɚɛɨɬɚɧɧɵɣ ɫɢɝɧɚɥ, ɤɨɬɨɪɵɣ ɛɭɞɟɬ ɩɨɞɚɧ ɧɚ ɜɯɨɞ ɫɟɬɢ. ɉɪɟɞɨɛɪɚɛɨɬɤɭ ɜɯɨɞɧɨɝɨ ɫɢɝɧɚɥɚ ɩɨ ɮɨɪɦɭɥɟ (1) ɛɭɞɟɦ ɧɚɡɵɜɚɬɶ ɩɪɨɫɬɟɣɲɟɣ ɩɪɟɞɨɛɪɚɛɨɬɤɨɣ.
4.1.9 Ɉɰɟɧɤɚ ɫɩɨɫɨɛɧɨɫɬɢ ɫɟɬɢ ɪɟɲɢɬɶ ɡɚɞɚɱɭ ȼ ɞɚɧɧɨɦ ɪɚɡɞɟɥɟ ɪɚɫɫɦɚɬɪɢɜɚɸɬɫɹ ɬɨɥɶɤɨ ɫɟɬɢ, ɜɫɟ ɷɥɟɦɟɧɬɵ ɤɨɬɨɪɵɯ ɧɟɩɪɟɪɵɜɧɨ ɡɚɜɢɫɹɬ ɨɬ ɫɜɨɢɯ ɚɪɝɭɦɟɧɬɨɜ (ɫɦ. ɝɥɚɜɭ «Ɉɩɢɫɚɧɢɟ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ»). ɉɪɟɞɩɨɥɚɝɚɟɬɫɹ, ɱɬɨ ɜɫɟ ɜɯɨɞɧɵɟ ɞɚɧɧɵɟ ɩɪɟɞɨɛɪɚɛɨɬɚɧɵ ɬɚɤ, ɱɬɨ ɜɫɟ ɜɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɫɟɬɢ ɥɟɠɚɬ ɜ ɞɢɚɩɚɡɨɧɟ ɩɪɢɟɦɥɟɦɵɯ ɜɯɨɞɧɵɯ ɫɢɝɧɚi i ɥɨɜ a, b . Ȼɭɞɟɦ ɨɛɨɡɧɚɱɚɬɶ ɜɟɤɬɨɪɚ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɱɟɪɟɡ x , ɚ ɬɪɟɛɭɟɦɵɟ ɨɬɜɟɬɵ ɫɟɬɢ ɱɟɪɟɡ f .
[ ]
Ʉɨɦɩɨɧɟɧɬɵ ɜɟɤɬɨɪɨɜ ɛɭɞɟɦ ɨɛɨɡɧɚɱɚɬɶ ɧɢɠɧɢɦ ɢɧɞɟɤɫɨɦ, ɧɚɩɪɢɦɟɪ, ɤɨɦɩɨɧɟɧɬɵ ɜɯɨɞɧɨɝɨ ɜɟɤɬɨɪɚ
CHAP4.DOC
56
i ɱɟɪɟɡ x j . Ȼɭɞɟɦ ɩɨɥɚɝɚɬɶ, ɱɬɨ ɜ ɤɚɠɞɨɦ ɩɪɢɦɟɪɟ ɨɬɜɟɬ ɹɜɥɹɟɬɫɹ ɜɟɤɬɨɪɨɦ ɱɢɫɟɥ ɢɡ ɞɢɚɩɚɡɨɧɚ ɩɪɢɟɦɥɟ-
[ ]
ɦɵɯ ɫɢɝɧɚɥɨɜ a, b . ȼ ɫɥɭɱɚɟ ɨɛɭɱɟɧɢɹ ɫɟɬɢ ɡɚɞɚɱɟ ɤɥɚɫɫɢɮɢɤɚɰɢɢ ɬɪɟɛɭɟɦɵɣ ɨɬɜɟɬ ɡɚɜɢɫɢɬ ɨɬ ɜɢɞɚ ɢɫɩɨɥɶɡɭɟɦɨɝɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ ɨɬɜɟɬɚ (ɫɦ. ɝɥɚɜɭ «Ɉɰɟɧɤɚ ɢ ɂɧɬɟɪɩɪɟɬɚɬɨɪ ɨɬɜɟɬɚ»). ɇɟɣɪɨɧɧɚɹ ɫɟɬɶ ɜɵɱɢɫɥɹɟɬ ɧɟɤɨɬɨɪɭɸ ɜɟɤɬɨɪ-ɮɭɧɤɰɢɸ F ɨɬ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ. ɗɬɚ ɮɭɧɤɰɢɹ ɡɚɜɢɫɢɬ ɨɬ ɩɚɪɚɦɟɬɪɨɜ ɫɟɬɢ. Ɉɛɭɱɟɧɢɟ ɫɟɬɢ ɫɨɫɬɨɢɬ ɜ ɩɨɞɛɨɪɟ ɬɚɤɨɝɨ ɧɚɛɨɪɚ ɩɚɪɚɦɟɬɪɨɜ ɫɟɬɢ, ɱɬɨɛɵ 2 çæ F j x i - f ji ÷ö ɛɵɥɚ ɦɢɧɢɦɚɥɶɧɨɣ (ɜ ɢɞɟɚɥɟ ɪɚɜɧɚ ɧɭɥɸ). Ⱦɥɹ ɬɨɝɨ ɱɬɨɛɵ ɧɟɣɪɨɧɧɚɹ ɜɟɥɢɱɢɧɚ è ø i, j
å
( )
ɫɟɬɶ ɦɨɝɥɚ ɯɨɪɨɲɨ ɩɪɢɛɥɢɡɢɬɶ ɡɚɞɚɧɧɭɸ ɬɚɛɥɢɱɧɨ ɮɭɧɤɰɢɸ f ɧɟɨɛɯɨɞɢɦɨ, ɱɬɨɛɵ ɪɟɚɥɢɡɭɟɦɚɹ ɫɟɬɶɸ i j i j ɮɭɧɤɰɢɹ F ɩɪɢ ɢɡɦɟɧɟɧɢɢ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɫ x ɧɚ x ɦɨɝɥɚ ɢɡɦɟɧɢɬɶ ɡɧɚɱɟɧɢɟ ɫ f ɧɚ f . Ɉɱɟɜɢɞɧɨ, ɱɬɨ ɧɚɢɛɨɥɟɟ ɬɪɭɞɧɵɦ ɞɥɹ ɫɟɬɢ ɞɨɥɠɧɨ ɛɵɬɶ ɩɪɢɛɥɢɠɟɧɢɟ ɮɭɧɤɰɢɢ ɜ ɬɨɱɤɚɯ, ɜ ɤɨɬɨɪɵɯ ɩɪɢ ɦɚɥɨɦ ɢɡɦɟɧɟɧɢɢ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɩɪɨɢɫɯɨɞɢɬ ɛɨɥɶɲɨɟ ɢɡɦɟɧɟɧɢɟ ɡɧɚɱɟɧɢɹ ɮɭɧɤɰɢɢ. Ɍɚɤɢɦ ɨɛɪɚɡɨɦ, ɧɚɢɛɨɥɶɲɭɸ ɫɥɨɠɧɨɫɬɶ ɛɭɞɟɬ ɩɪɟɞɫɬɚɜɥɹɬɶ ɩɪɢɛɥɢɠɟɧɢɟ ɮɭɧɤɰɢɢ f ɜ ɬɨɱɤɚɯ, ɜ ɤɨɬɨɪɵɯ ɞɨɫɬɢɝɚɟɬ ɦɚɤɫɢɦɭɦɚ ɜɵɪɚɠɟɧɢɟ
fi- f j
. Ⱦɥɹ ɚɧɚɥɢɬɢɱɟɫɤɢ ɡɚɞɚɧɧɵɯ ɮɭɧɤɰɢɣ ɜɟɥɢɱɢɧɚ sup
xi -x j
x, y
f ( x ) - f ( y) x-y
ɧɚɡɵɜɚɟɬɫɹ ɤɨɧɫɬɚɧɬɨɣ Ʌɢɩɲɢɰɚ. ɂɫɯɨɞɹ ɢɡ ɷɬɢɯ ɫɨɨɛɪɚɠɟɧɢɹ ɦɨɠɧɨ ɞɚɬɶ ɫɥɟɞɭɸɳɟɟ ɨɩɪɟɞɟɥɟɧɢɟ ɫɥɨɠɧɨɫɬɢ ɡɚɞɚɱɢ. i ɋɥɨɠɧɨɫɬɶ ɚɩɩɪɨɤɫɢɦɚɰɢɢ ɬɚɛɥɢɱɧɨ ɡɚɞɚɧɧɨɣ ɮɭɧɤɰɢɢ f , ɤɨɬɨɪɚɹ ɜ ɬɨɱɤɚɯ x ɩɪɢɧɢɦɚɟɬ ɡɧɚi ɱɟɧɢɹ f , ɡɚɞɚɟɬɫɹ ɜɵɛɨɪɨɱɧɨɣ ɨɰɟɧɤɨɣ ɤɨɧɫɬɚɧɬɵ Ʌɢɩɲɢɰɚ, ɜɵɱɢɫɥɹɟɦɨɣ ɩɨ ɫɥɟɞɭɸɳɟɣ ɮɨɪɦɭɥɟ:
L t = max i¹ j
fi- f j xi - x j
(2)
Ɉɰɟɧɤɚ (2) ɹɜɥɹɟɬɫɹ ɨɰɟɧɤɨɣ ɤɨɧɫɬɚɧɬɵ Ʌɢɩɲɢɰɚ ɚɩɩɪɨɤɫɢɦɢɪɭɟɦɨɣ ɮɭɧɤɰɢɢ ɫɧɢɡɭ. Ⱦɥɹ ɬɨɝɨ, ɱɬɨɛɵ ɨɰɟɧɢɬɶ ɫɩɨɫɨɛɧɨɫɬɶ ɫɟɬɢ ɡɚɞɚɧɧɨɣ ɤɨɧɮɢɝɭɪɚɰɢɢ ɪɟɲɢɬɶ ɡɚɞɚɱɭ, ɧɟɨɛɯɨɞɢɦɨ ɨɰɟɧɢɬɶ ɤɨɧɫɬɚɧɬɭ Ʌɢɩɲɢɰɚ ɫɟɬɢ ɢ ɫɪɚɜɧɢɬɶ ɟɟ ɫ ɜɵɛɨɪɨɱɧɨɣ ɨɰɟɧɤɨɣ (2). Ʉɨɧɫɬɚɧɬɚ Ʌɢɩɲɢɰɚ ɫɟɬɢ ɜɵɱɢɫɥɹɟɬɫɹ ɩɨ ɫɥɟɞɭɸɳɟɣ ɮɨɪɦɭɥɟ:
L n = sup x, y
F ( x ) - F ( y) x-y
(3)
ȼ ɮɨɪɦɭɥɚɯ (2) ɢ (3) ɦɨɠɧɨ ɢɫɩɨɥɶɡɨɜɚɬɶ ɩɪɨɢɡɜɨɥɶɧɵɟ ɧɨɪɦɵ. Ɉɞɧɚɤɨ ɞɥɹ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ɧɚɢɛɨɥɟɟ ɭɞɨɛɧɨɣ ɹɜɥɹɟɬɫɹ ɟɜɤɥɢɞɨɜɚ ɧɨɪɦɚ. Ⱦɚɥɟɟ ɜɟɡɞɟ ɢɫɩɨɥɶɡɭɟɬɫɹ ɟɜɤɥɢɞɨɜɚ ɧɨɪɦɚ. ȼ ɫɥɟɞɭɸɳɟɦ ɪɚɡɞɟɥɟ ɨɩɢɫɚɧ ɫɩɨɫɨɛ ɜɵɱɢɫɥɟɧɢɹ ɨɰɟɧɤɢ ɤɨɧɫɬɚɧɬɵ Ʌɢɩɲɢɰɚ ɫɟɬɢ (3) ɫɜɟɪɯɭ. Ɉɱɟɜɢɞɧɨ, ɱɬɨ ɜ ɫɥɭɱɚɟ L n < L t ɫɟɬɶ ɩɪɢɧɰɢɩɢɚɥɶɧɨ ɧɟ ɫɩɨɫɨɛɧɚ ɪɟɲɢɬɶ ɡɚɞɚɱɭ ɚɩɩɪɨɤɫɢɦɚɰɢɢ ɮɭɧɤɰɢɢ f .
4.1.9.1 Ɉɰɟɧɤɚ ɤɨɧɫɬɚɧɬɵ Ʌɢɩɲɢɰɚ ɫɟɬɢ Ɉɰɟɧɤɭ ɤɨɧɫɬɚɧɬɵ Ʌɢɩɲɢɰɚ ɫɟɬɢ ɛɭɞɟɦ ɫɬɪɨɢɬɶ ɜ ɫɨɨɬɜɟɬɫɬɜɢɢ ɫ ɩɪɢɧɰɢɩɨɦ ɢɟɪɚɪɯɢɱɟɫɤɨɝɨ ɭɫɬɪɨɣɫɬɜɚ ɫɟɬɢ, ɨɩɢɫɚɧɧɵɦ ɜ ɝɥɚɜɟ «Ɉɩɢɫɚɧɢɟ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ». ɉɪɢ ɷɬɨɦ ɩɨɬɪɟɛɭɸɬɫɹ ɫɥɟɞɭɸɳɢɟ ɩɪɚɜɢɥɚ. 1.
( ( )) ɤɨɧɫɬɚɧɬɚ Ʌɢɩɲɢɰɚ ɨɰɟɧɢɜɚɟɬɫɹ ɤɚɤ ɩɪɨɢɡɜɟɞɟɧɢɟ
Ⱦɥɹ ɤɨɦɩɨɡɢɰɢɢ ɮɭɧɤɰɢɣ f o g = f g x ɤɨɧɫɬɚɧɬ Ʌɢɩɲɢɰɚ:
2.
(
L f og £ L f L g .
Ⱦɥɹ ɜɟɤɬɨɪ-ɮɭɧɤɰɢɢ f = f 1 , f 2 ,K, f n
CHAP4.DOC
) ɤɨɧɫɬɚɧɬɚ Ʌɢɩɲɢɰɚ ɪɚɜɧɚ:
57
(4)
Lf =
å L2f i n
.
(5)
i =1
4.1.9.2 ɋɩɨɫɨɛ ɜɵɱɢɫɥɟɧɢɹ ɤɨɧɫɬɚɧɬɵ Ʌɢɩɲɢɰɚ Ⱦɥɹ ɧɟɩɪɟɪɵɜɧɵɯ ɮɭɧɤɰɢɣ ɤɨɧɫɬɚɧɬɚ Ʌɢɩɲɢɰɚ ɹɜɥɹɟɬɫɹ ɦɚɤɫɢɦɭɦɨɦ ɩɪɨɢɡɜɨɞɧɨɣ ɜ ɧɚɩɪɚɜɥɟ-
(
)
ɧɢɢ r = r1 ,K, rn ɩɨ ɜɫɟɦ ɬɨɱɤɚɦ ɢ ɜɫɟɦ ɧɚɩɪɚɜɥɟɧɢɹɦ. ɉɪɢ ɷɬɨɦ ɜɟɤɬɨɪ ɧɚɩɪɚɜɥɟɧɢɹ ɢɦɟɟɬ ɟɞɢɧɢɱn ɧɭɸ ɞɥɢɧɭ: ri2 = 1 . ɇɚɩɨɦɧɢɦ ɮɨɪɦɭɥɭ ɩɪɨɢɡɜɨɞɧɨɣ ɮɭɧɤɰɢɢ f x1 , K , xn ɜ ɧɚɩɪɚɜɥɟɧɢɢ r : i =1 n ¶f ¶f = ri (6) ¶r i =1 ¶x i
å
(
)
å
4.1.9.3 ɋɢɧɚɩɫ Ɉɛɨɡɧɚɱɢɦ ɜɯɨɞɧɨɣ ɫɢɝɧɚɥ ɫɢɧɚɩɫɚ ɱɟɪɟɡ x , ɚ ɫɢɧɚɩɬɢɱɟɫɤɢɣ ɜɟɫ ɱɟɪɟɡ a . Ɍɨɝɞɚ ɜɵɯɨɞɧɨɣ ɫɢɝɧɚɥ ɫɢɧɚɩɫɚ ɪɚɜɟɧ ax . ɉɨɫɤɨɥɶɤɭ ɫɢɧɚɩɫ ɹɜɥɹɟɬɫɹ ɮɭɧɤɰɢɟɣ ɨɞɧɨɣ ɩɟɪɟɦɟɧɧɨɣ, ɤɨɧɫɬɚɧɬɚ Ʌɢɩɲɢɰɚ ɪɚɜɧɚ ɦɚɤɫɢɦɭɦɭ ɦɨɞɭɥɹ ɩɪɨɢɡɜɨɞɧɨɣ – ɦɨɞɭɥɸ ɫɢɧɚɩɬɢɱɟɫɤɨɝɨ ɜɟɫɚ:
Ls = a
(7).
4.1.9.4 ɍɦɧɨɠɢɬɟɥɶ Ɉɛɨɡɧɚɱɢɦ ɜɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɭɦɧɨɠɢɬɟɥɹ ɱɟɪɟɡ x1, x 2 . Ɍɨɝɞɚ ɜɵɯɨɞɧɨɣ ɫɢɝɧɚɥ ɭɦɧɨɠɢɬɟɥɹ ɪɚf *= x1x 2 . ɂɫɩɨɥɶɡɭɹ (6) ɩɨɥɭɱɚɟɦ L f * = sup r1x 2 + r2 x1 . ȼɵɪɚɠɟɧɢɟ r1x 2 + r2 x1 ɹɜɥɹɟɬɫɹ ɫɤɚx ,r ɥɹɪɧɵɦ ɩɪɨɢɡɜɟɞɟɧɢɟɦ ɜɟɤɬɨɪɨɜ ( r1, r2 ) ɢ, ɭɱɢɬɵɜɚɹ ɟɞɢɧɢɱɧɭɸ ɞɥɢɧɭ ɜɟɤɬɨɪɚ r , ɞɨɫɬɢɝɚɟɬ ɦɚɤɫɢɦɭɦɚ, ɜɟɧ
ɤɨɝɞɚ ɷɬɢ ɜɟɤɬɨɪɵ ɫɨɧɚɩɪɚɜɥɟɧɵ. Ɍɨ ɟɫɬɶ ɩɪɢ ɜɟɤɬɨɪɟ
æx x ö r = ç 2 , 1 ÷ , x = x12 + x12 . è x x ø
ɂɫɩɨɥɶɡɭɹ ɷɬɨ ɜɵɪɚɠɟɧɢɟ, ɦɨɠɧɨ ɡɚɩɢɫɚɬɶ ɤɨɧɫɬɚɧɬɭ Ʌɢɩɲɢɰɚ ɞɥɹ ɭɦɧɨɠɢɬɟɥɹ: x 22 + x12 L f = sup r1x 2 + r2 x1 = sup = sup x . *
x ,r
x
x
ȿɫɥɢ ɜɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɭɦɧɨɠɢɬɟɥɹ ɩɪɢɧɚɞɥɟɠɚɬ ɢɧɬɟɪɜɚɥɭ
{
ɭɦɧɨɠɢɬɟɥɹ ɦɨɠɟɬ ɛɵɬɶ ɡɚɩɢɫɚɧɚ ɜ ɫɥɟɞɭɸɳɟɦ ɜɢɞɟ:
}
L f * = 2 max a , b .
(8)
x
[a, b] , ɬɨ ɤɨɧɫɬɚɧɬɚ Ʌɢɩɲɢɰɚ ɞɥɹ (9)
4.1.9.5 Ɍɨɱɤɚ ɜɟɬɜɥɟɧɢɹ ɉɨɫɤɨɥɶɤɭ ɜ ɬɨɱɤɟ ɜɟɬɜɥɟɧɢɹ ɧɟ ɩɪɨɢɫɯɨɞɢɬ ɩɪɟɨɛɪɚɡɨɜɚɧɢɹ ɫɢɝɧɚɥɚ, ɬɨ ɤɨɧɫɬɚɧɬɚ Ʌɢɩɲɢɰɚ ɞɥɹ ɧɟɟ ɪɚɜɧɚ ɟɞɢɧɢɰɟ.
4.1.9.6 ɋɭɦɦɚɬɨɪ ɉɪɨɢɡɜɨɞɧɚɹ ɫɭɦɦɵ ɩɨ ɥɸɛɨɦɭ ɢɡ ɫɥɚɝɚɟɦɵɯ ɪɚɜɧɚ ɟɞɢɧɢɰɟ. ȼ ɫɨɨɬɜɟɬɫɬɜɢɢ ɫ (6) ɩɨɥɭɱɚɟɦ: n L S = sup ri = n , (10) r i =1 ɩɨɫɤɨɥɶɤɭ ɦɚɤɫɢɦɭɦ ɫɭɦɦɵ ɩɪɢ ɨɝɪɚɧɢɱɟɧɢɢ ɧɚ ɫɭɦɦɭ ɤɜɚɞɪɚɬɨɜ ɞɨɫɬɢɝɚɟɬɫɹ ɩɪɢ ɨɞɢɧɚɤɨɜɵɯ ɫɥɚɝɚɟɦɵɯ.
å
CHAP4.DOC
58
4.1.9.7 ɇɟɥɢɧɟɣɧɵɣ ɉɚɞɟ ɩɪɟɨɛɪɚɡɨɜɚɬɟɥɶ ɇɟɥɢɧɟɣɧɵɣ ɉɚɞɟ ɩɪɟɨɛɪɚɡɨɜɚɬɟɥɶ ɢɥɢ ɉɚɞɟ ɷɥɟɦɟɧɬ ɢɦɟɟɬ ɞɜɚ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɚ ɢ ɨɞɢɧ ɜɵɯɨɞɧɨɣ. Ɉɛɨɡɧɚɱɢɦ ɜɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɱɟɪɟɡ x1, x 2 . ɂɫɩɨɥɶɡɭɹ (6) ɦɨɠɧɨ ɡɚɩɢɫɚɬɶ ɤɨɧɫɬɚɧɬɭ Ʌɢɩɲɢɰɚ ɜ ɫɥɟɞɭɸɳɟɦ ɜɢɞɟ:
rx - r x rx rx L p = sup 1 2 - 2 1 = sup 1 2 2 1 . 2 2 x 22 x2 r, x r, x x 2 Ɂɧɚɦɟɧɚɬɟɥɶ ɜɵɪɚɠɟɧɢɹ ɩɨɞ ɡɧɚɤɨɦ ɦɨɞɭɥɹ ɧɟ ɡɚɜɢɫɢɬ ɨɬ ɧɚɩɪɚɜɥɟɧɢɹ, ɚ ɱɢɫɥɢɬɟɥɶ ɦɨɠɧɨ ɩɪɟɨɛɪɚɡɨɜɚɬɶ ɬɚɤ ɠɟ, ɤɚɤ ɢ ɞɥɹ ɭɦɧɨɠɢɬɟɥɹ. ɉɨɫɥɟ ɩɪɟɨɛɪɚɡɨɜɚɧɢɹ ɩɨɥɭɱɚɟɦ:
x L p = sup 2 x x2
(11)
4.1.9.8 ɇɟɥɢɧɟɣɧɵɣ ɫɢɝɦɨɢɞɧɵɣ ɩɪɟɨɛɪɚɡɨɜɚɬɟɥɶ ɇɟɥɢɧɟɣɧɵɣ ɫɢɝɦɨɢɞɧɵɣ ɩɪɟɨɛɪɚɡɨɜɚɬɟɥɶ, ɤɚɤ ɢ ɥɸɛɨɣ ɞɪɭɝɨɣ ɧɟɥɢɧɟɣɧɵɣ ɩɪɟɨɛɪɚɡɨɜɚɬɟɥɶ, ɢɦɟɸɳɢɣ ɨɞɢɧ ɜɯɨɞɧɨɣ ɫɢɝɧɚɥ x , ɢɦɟɟɬ ɤɨɧɫɬɚɧɬɭ Ʌɢɩɲɢɰɚ ɪɚɜɧɭɸ ɦɚɤɫɢɦɭɦɭ ɦɨɞɭɥɹ ɩɪɨɢɡɜɨɞɧɨɣ:
Lj = max j ¢( x ) . x
(12)
4.1.9.9 Ⱥɞɚɩɬɢɜɧɵɣ ɫɭɦɦɚɬɨɪ Ⱦɥɹ ɚɞɚɩɬɢɜɧɨɝɨ ɫɭɦɦɚɬɨɪɚ ɧɚ n ɜɯɨɞɨɜ ɨɰɟɧɤɚ ɤɨɧɫɬɚɧɬɵ Ʌɢɩɲɢɰɚ, ɩɨɥɭɱɚɟɦɚɹ ɱɟɪɟɡ ɩɪɟɞɫɬɚɜɥɟɧɢɟ ɟɝɨ ɜ ɜɢɞɟ ɫɭɩɟɪɩɨɡɢɰɢɢ ɫɥɨɹ ɫɢɧɚɩɫɨɜ ɢ ɩɪɨɫɬɨɝɨ ɫɭɦɦɚɬɨɪɚ, ɜɵɱɢɫɥɹɟɬɫɹ ɫɥɟɞɭɸɳɢɦ ɨɛɪɚɡɨɦ. ɂɫɩɨɥɶɡɭɹ ɮɨɪɦɭɥɭ (7) ɞɥɹ ɫɢɧɚɩɫɨɜ ɢ ɩɪɚɜɢɥɨ (5) ɞɥɹ ɜɟɤɬɨɪ-ɮɭɧɤɰɢɢ ɩɨɥɭɱɚɟɦ ɫɥɟɞɭɸɳɭɸ ɨɰɟɧɤɭ ɤɨɧɫɬɚɧɬɵ Ʌɢɩɲɢɰɚ ɫɥɨɹ ɫɢɧɚɩɫɨɜ:
LL =
å L2S i n
=
åai n
= a . i =1 i =1 ɂɫɩɨɥɶɡɭɹ ɩɪɚɜɢɥɨ (4) ɞɥɹ ɫɭɩɟɪɩɨɡɢɰɢɢ ɮɭɧɤɰɢɣ ɢ ɨɰɟɧɤɭ ɤɨɧɫɬɚɧɬɵ Ʌɢɩɲɢɰɚ ɞɥɹ ɩɪɨɫɬɨɝɨ ɫɭɦɦɚɬɨɪɚ (10) ɩɨɥɭɱɚɟɦ: 2
L A £ LSL L = n a . (13) Ɉɞɧɚɤɨ, ɟɫɥɢ ɨɰɟɧɢɬɶ ɤɨɧɫɬɚɧɬɭ Ʌɢɩɲɢɰɚ ɚɞɚɩɬɢɜɧɨɝɨ ɫɭɦɦɚɬɨɪɚ ɧɚɩɪɹɦɭɸ, ɬɨ, ɢɫɩɨɥɶɡɭɹ (6) ɢ ɬɨɬ ɮɚɤɬ, ɱɬɨ ɩɪɢ ɮɢɤɫɢɪɨɜɚɧɧɵɯ ɞɥɢɧɚɯ ɜɟɤɬɨɪɨɜ ɫɤɚɥɹɪɧɨɟ ɩɪɨɢɡɜɟɞɟɧɢɟ ɞɨɫɬɢɝɚɟɬ ɦɚɤɫɢɦɭɦɚ ɞɥɹ ɫɨɧɚɩɪɚɜɥɟɧɧɵɯ ɜɟɤɬɨɪɨɜ ɩɨɥɭɱɚɟɦ: n n a L A = sup ria i = (14) ai i = a . a x , r i =1 i =1 Ɉɱɟɜɢɞɧɨ, ɱɬɨ ɨɰɟɧɤɚ (14) ɬɨɱɧɟɟ, ɱɟɦ ɨɰɟɧɤɚ (13).
å
å
4.1.9.10 Ʉɨɧɫɬɚɧɬɚ Ʌɢɩɲɢɰɚ ɫɢɝɦɨɢɞɧɨɣ ɫɟɬɢ Ɋɚɫɫɦɨɬɪɢɦ ɫɥɨɢɫɬɭɸ ɫɢɝɦɨɢɞɧɭɸ ɫɟɬɶ ɫɨ ɫɥɟɞɭɸɳɢɦɢ ɫɜɨɣɫɬɜɚɦɢ: 1. ɑɢɫɥɨ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ – n0 . ɑɢɫɥɨ ɧɟɣɪɨɧɨɜ ɜ i -ɦ ɫɥɨɟ – ni . Ʉɚɠɞɵɣ ɧɟɣɪɨɧ ɩɟɪɜɨɝɨ ɫɥɨɹ ɩɨɥɭɱɚɟɬ ɜɫɟ ɜɯɨɞɧɵɟ ɫɢɝɧɚɥɵ, ɚ ɤɚɠɞɵɣ ɧɟɣɪɨɧ ɥɸɛɨɝɨ ɞɪɭɝɨɝɨ ɫɥɨɹ ɩɨɥɭɱɚɟɬ ɫɢɝɧɚɥɵ ɜɫɟɯ ɧɟɣɪɨɧɨɜ ɩɪɟɞɵɞɭɳɟɝɨ ɫɥɨɹ. 4. ȼɫɟ ɧɟɣɪɨɧɵ ɜɫɟɯ ɫɥɨɟɜ ɢɦɟɸɬ ɜɢɞ, ɩɪɢɜɟɞɟɧɧɵɣ ɧɚ ɪɢɫ. 1 ɢ ɢɦɟɸɬ ɨɞɢɧɚɤɨɜɭɸ ɯɚɪɚɤɬɟɪɢɫɬɢɤɭ. 5. ȼɫɟ ɫɢɧɚɩɬɢɱɟɫɤɢɟ ɜɟɫɚ ɨɝɪɚɧɢɱɟɧɵ ɩɨ ɦɨɞɭɥɸ ɟɞɢɧɢɰɟɣ. 6. ȼ ɫɟɬɢ m ɫɥɨɟɜ. ȼ ɷɬɨɦ ɫɥɭɱɚɟ, ɭɱɢɬɵɜɚɹ ɮɨɪɦɭɥɵ (4), (5), (12) ɢ (14) ɤɨɧɫɬɚɧɬɭ Ʌɢɩɲɢɰɚ i -ɨ ɫɥɨɹ ɦɨɠɧɨ ɨɰɟɧɢɬɶ ɫɥɟɞɭɸɳɟɣ ɜɟɥɢɱɢɧɨɣ: ni ni 2 2 Li £ Lj L A j = Lj a j £ Lj ni -1ni . j =1 j =1 2. 3.
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ɂɫɩɨɥɶɡɭɹ ɮɨɪɦɭɥɭ (4) ɩɨɥɭɱɚɟɦ ɨɰɟɧɤɭ ɤɨɧɫɬɚɧɬɵ Ʌɢɩɲɢɰɚ ɜɫɟɣ ɫɟɬɢ:
CHAP4.DOC
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L n £ Õ L i £ Lm n0 nm j m
i =1
ȿɫɥɢ ɢɫɩɨɥɶɡɭɟɬɫɹ ɧɟɣɪɨɧɵ ɬɢɩɚ S1 , ɬɨ
i =1
L j = c ɢ ɨɰɟɧɤɚ ɤɨɧɫɬɚɧɬɵ Ʌɢɩɲɢɰɚ ɫɟɬɢ ɪɚɜɧɚ:
L S1 £ c m n0 nm Ⱦɥɹ ɧɟɣɪɨɧɨɜ ɬɢɩɚ
Õ ni .
m -1
Õ ni
m -1 i =1
S 2 , ɬɨ Lj = 1 ɫ ɢ ɨɰɟɧɤɚ ɤɨɧɫɬɚɧɬɵ Ʌɢɩɲɢɰɚ ɫɟɬɢ ɪɚɜɧɚ: L S1 £ c- m n0 nm
Õ ni
m -1
i =1 Ɉɛɟ ɮɨɪɦɭɥɵ ɩɨɞɬɜɟɪɠɞɚɸɬ ɷɤɫɩɟɪɢɦɟɧɬɚɥɶɧɨ ɭɫɬɚɧɨɜɥɟɧɧɵɣ ɮɚɤɬ, ɱɬɨ ɱɟɦ ɤɪɭɱɟ ɯɚɪɚɤɬɟɪɢɫɬɢɱɟɫɤɚɹ ɮɭɧɤɰɢɹ ɧɟɣɪɨɧɚ, ɬɟɦ ɛɨɥɟɟ ɫɥɨɠɧɵɟ ɮɭɧɤɰɢɢ (ɮɭɧɤɰɢɢ ɫ ɛɨɥɶɲɟɣ ɤɨɧɫɬɚɧɬɨɣ Ʌɢɩɲɢɰɚ) ɦɨɠɟɬ ɚɩɩɪɨɤɫɢɦɢɪɨɜɚɬɶ ɫɟɬɶ ɫ ɬɚɤɢɦɢ ɧɟɣɪɨɧɚɦɢ.
4.1.10 ɉɪɟɞɨɛɪɚɛɨɬɤɚ, ɨɛɥɟɝɱɚɸɳɚɹ ɨɛɭɱɟɧɢɟ ɉɪɢ ɨɛɭɱɟɧɢɢ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ɢɧɨɝɞɚ ɜɨɡɧɢɤɚɸɬ ɫɢɬɭɚɰɢɢ, ɤɨɝɞɚ ɞɚɥɶɧɟɣɲɟɟ ɨɛɭɱɟɧɢɟ ɧɟɣɪɨɧɧɨɣ ɫɟɬɢ ɧɟɜɨɡɦɨɠɧɨ. ȼ ɷɬɨɦ ɫɥɭɱɚɟ ɧɟɨɛɯɨɞɢɦɨ ɩɪɨɚɧɚɥɢɡɢɪɨɜɚɬɶ ɩɪɢɱɢɧɵ. ȼɨɡɦɨɠɧɨ ɧɟɫɤɨɥɶɤɨ ɜɢɞɨɜ ɚɧɚɥɢɡɚ. Ɉɞɧɨɣ ɢɡ ɜɨɡɦɨɠɧɵɯ ɩɪɢɱɢɧ ɹɜɥɹɟɬɫɹ ɜɵɫɨɤɚɹ ɫɥɨɠɧɨɫɬɶ ɡɚɞɚɱɢ, ɨɩɪɟɞɟɥɹɟɦɚɹ ɤɚɤ ɜɵɛɨɪɨɱɧɚɹ ɨɰɟɧɤɚ ɤɨɧɫɬɚɧɬɵ Ʌɢɩɲɢɰɚ. Ⱦɥɹ ɭɩɪɨɳɟɧɢɹ ɡɚɞɚɱɢ ɧɟɨɛɯɨɞɢɦɨ ɭɦɟɧɶɲɢɬɶ ɜɵɛɨɪɨɱɧɭɸ ɨɰɟɧɤɭ ɤɨɧɫɬɚɧɬɵ Ʌɢɩɲɢɰɚ. ɇɚɢɛɨɥɟɟ ɩɪɨɫɬɨɣ ɫɩɨɫɨɛ ɞɨɛɢɬɶɫɹ ɷɬɨɝɨ – ɭɜɟɥɢɱɢɬɶ ɪɚɫɫɬɨɹɧɢɟ ɦɟɠɞɭ ɜɯɨɞɧɵɦɢ ɫɢɝɧɚɥɚɦɢ. Ɋɚɫɫɦɨɬɪɢɦ fi- f j i j i ɩɚɪɭ ɩɪɢɦɟɪɨɜ – x , x – ɬɚɤɢɯ, ɱɬɨ L t = . Ɉɩɪɟɞɟɥɢɦ ɫɪɟɞɢ ɤɨɨɪɞɢɧɚɬ ɜɟɤɬɨɪɨɜ x ɢ i j
x -x
x j ɤɨɨɪɞɢɧɚɬɭ, ɜ ɤɨɬɨɪɨɣ ɞɨɫɬɢɝɚɟɬ ɦɢɧɢɦɭɦɚ ɜɟɥɢɱɢɧɚ x li - x lj , ɢɫɤɥɸɱɢɜ ɢɡ ɪɚɫɫɦɨɬɪɟɧɢɹ ɫɨɜɩɚɞɚɸɳɢɟ ɤɨɨɪɞɢɧɚɬɵ. Ɉɱɟɜɢɞɧɨ, ɱɬɨ ɷɬɚ ɤɨɨɪɞɢɧɚɬɚ ɹɜɥɹɟɬɫɹ «ɭɡɤɢɦ ɦɟɫɬɨɦ», ɨɩɪɟɞɟɥɹɸɳɢɦ ɫɥɨɠɧɨɫɬɶ ɡɚɞɚɱɢ. ɋɥɟɞɨɜɚɬɟɥɶɧɨ, ɞɥɹ ɭɦɟɧɶɲɟɧɢɹ ɫɥɨɠɧɨɫɬɢ ɡɚɞɚɱɢ ɬɪɟɛɭɟɬɫɹ ɭɜɟɥɢɱɢɬɶ ɪɚɫɫɬɨɹɧɢɟ ɦɟɠɞɭ ɜɟɤɬɨi j ɪɚɦɢ x ɢ x , ɚ ɧɚɢɛɨɥɟɟ ɩɟɪɫɩɟɤɬɢɜɧɨɣ ɤɨɨɪɞɢɧɚɬɨɣ ɞɥɹ ɷɬɨɝɨ ɹɜɥɹɟɬɫɹ l -ɹ. Ɉɞɧɚɤɨ ɭɜɟɥɢɱɟɧɢɟ ɪɚɫj i ɫɬɨɹɧɢɟ ɦɟɠɞɭ x l ɢ x l ɧɟ ɜɫɟɝɞɚ ɨɫɦɵɫɥɟɧɨ. Ⱦɟɥɨ ɜ ɬɨɦ, ɱɬɨ ɜɫɟ ɩɚɪɚɦɟɬɪɵ, ɤɚɤ ɩɪɚɜɢɥɨ, ɢɡɦɟɪɹɸɬɫɹ j i ɫ ɤɨɧɟɱɧɨɣ ɬɨɱɧɨɫɬɶɸ. ɉɨɷɬɨɦɭ, ɟɫɥɢ ɜɟɥɢɱɢɧɚ x l - x l ɦɟɧɶɲɟ ɱɟɦ ɬɨɱɧɨɫɬɶ ɢɡɦɟɪɟɧɢɹ l -ɝɨ ɩɚɪɚɦɟɬɪɚ, ɡɧɚɱɟɧɢɹ
x li ɢ x lj ɦɨɠɧɨ ɫɱɢɬɚɬɶ ɫɨɜɩɚɞɚɸɳɢɦɢ. Ɍɚɤɢɦ ɨɛɪɚɡɨɦ, ɞɥɹ ɢɡɦɟɧɟɧɢɹ ɦɚɫɲɬɚɛɚ ɧɚɞɨ
j i ɜɵɛɢɪɚɬɶ ɬɨɬ ɢɡ ɜɯɨɞɧɵɯ ɩɚɪɚɦɟɬɪɨɜ, ɞɥɹ ɤɨɬɨɪɨɝɨ ɡɧɚɱɟɧɢɟ x l - x l ɦɢɧɢɦɚɥɶɧɨ, ɧɨ ɩɪɟɜɵɲɚɟɬ ɬɨɱ-
ɧɨɫɬɶ ɢɡɦɟɪɟɧɢɹ ɷɬɨɝɨ ɩɚɪɚɦɟɬɪɚ. ɉɪɟɞɩɨɥɨɠɢɦ, ɱɬɨ ɜɫɟ ɜɯɨɞɧɵɟ ɩɚɪɚɦɟɬɪɵ ɩɪɟɞɨɛɪɚɛɨɬɚɧɵ ɜ ɫɨɨɬɜɟɬɫɬɜɢɢ ɫ ɮɨɪɦɭɥɨɣ (1). ɉɟɪɟɧɭɦɟɪɭɟɦ ɩɪɢɦɟɪɵ ɨɛɭɱɚɸɳɟɝɨ ɦɧɨɠɟɫɬɜɚ ɬɚɤ, ɱɬɨɛɵ ɛɵɥɢ ɜɟɪɧɵ ɫɥɟɞɭɸɳɢɟ ɧɟɪɚɜɟɧɫɬɜɚ: xl1 < xl2 <,K, < xlN , ɝɞɟ N – ɱɢɫɥɨ Ɍɚɛɥɢɰɚ 7. ɩɪɢɦɟɪɨɜ ɜ ɨɛɭɱɚɸɳɟɦ ɦɧɨɠɟɫɬɜɟ. ɉɪɢ Ʉɨɞɢɪɨɜɚɧɢɟ ɩɚɪɚɦɟɬɪɚ ɩɨɫɥɟ ɪɚɡɛɢɟɧɢɹ ɧɚ ɞɜɚ ɫɢɝɧɚɥɚ ɷɬɨɦ, ɜɨɡɦɨɠɧɨ, ɩɪɢɞɟɬɫɹ ɢɫɤɥɸɱɢɬɶ ɪɹɞ Ɂɧɚɱɟɧɢɟɉ ɟɪɜɵɣ ɫɢɝɧɚɥȼɬɨɪɨɣ ɫɢɝɧɚɥ ɩɚɪ ɩɚɪɚɦɟɬɪ-ɨɬɜɟɬ ɫ ɫɨɜɩɚɞɚɸɳɢɦɢ i ɡɧɚɱɟɧɢɹɦɢ ɩɚɪɚɦɟɬɪɚ. ȿɫɥɢ ɜ ɤɚɤɨɣxl - a b- a a ɥɢɛɨ ɢɡ ɬɚɤɢɯ ɩɚɪ ɡɧɚɱɟɧɢɹ ɨɬɜɟɬɨɜ ɪɚɡx li < x +a ɥɢɱɚɸɬɫɹ, ɬɨ ɷɬɨ ɫɧɢɠɚɟɬ ɜɨɡɦɨɠɧɭɸ x -a ɩɨɥɟɡɧɨɫɬɶ ɞɚɧɧɨɣ ɩɪɨɰɟɞɭɪɵ. ɇɚɢɛɨɥɟɟ ɩɪɨɫɬɨɣ ɩɭɬɶ – ɪɚɡɛɢɬɶ x li - x b - a b x li > x +a ɞɢɚɩɚɡɨɧ l -ɝɨ ɩɚɪɚɦɟɬɪɚ ɧɚ ɞɜɚ. Ɂɚɞɚb- x ɞɢɦɫɹ ɬɨɱɤɨɣ x . Ȼɭɞɟɦ ɤɨɞɢɪɨɜɚɬɶ l -ɣ
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ɩɚɪɚɦɟɬɪ ɞɜɭɦɹ ɜɯɨɞɧɵɦɢ ɫɢɝɧɚɥɚɦɢ ɜ ɫɨɨɬɜɟɬɫɬɜɢɢ ɫ ɬɚɛɥ. 7. ɉɪɢ ɬɚɤɨɦ ɤɨɞɢɪɨɜɚɧɢɢ ɤɪɢɬɟɪɢɣ Ʌɢɩɲɢɰɚ, ɨɱɟɜɢɞɧɨ, ɭɦɟɧɶɲɢɬɫɹ. ȼɨɩɪɨɫ ɨ ɜɵɛɨɪɟ ɬɨɱɤɢ x ɦɨɠɟɬ ɪɟɲɚɬɶɫɹ ɩɨ-ɪɚɡɧɨɦɭ. ɉɪɨɫɬɟɣɲɢɣ ɩɭɬɶ – ɩɨɥɨɠɢɬɶ
x = (a - b) 2 . Ȼɨɥɟɟ ɫɥɨɠɧɵɣ, ɧɨ ɱɚɫɬɨ ɛɨɥɟɟ ɷɮɮɟɤɬɢɜɧɵɣ – ɩɨɞɛɨɪ x ɢɫɯɨɞɹ ɢɡ ɬɪɟɛɨ-
ɜɚɧɢɹ ɦɢɧɢɦɚɥɶɧɨɫɬɢ ɤɪɢɬɟɪɢɹ Ʌɢɩɲɢɰɚ. ɉɪɢɜɟɞɟɧɧɵɣ ɜɵɲɟ ɫɩɨɫɨɛ ɭɦɟɧɶɲɟɧɢɹ ɤɪɢɬɟɪɢɹ Ʌɢɩɲɢɰɚ ɧɟ ɟɞɢɧɫɬɜɟɧɧɵɣ. ȼ ɫɥɟɞɭɸɳɟɦ ɪɚɡɞɟɥɟ ɪɚɫɫɦɨɬɪɟɧ ɪɹɞ ɫɩɨɫɨɛɨɜ ɩɪɟɞɨɛɪɚɛɨɬɤɢ, ɪɟɲɚɸɳɢɯ ɬɭ ɠɟ ɡɚɞɚɱɭ.
4.1.11 Ⱦɪɭɝɢɟ ɫɩɨɫɨɛɵ ɩɪɟɞɨɛɪɚɛɨɬɤɢ ɱɢɫɥɨɜɵɯ ɩɪɢɡɧɚɤɨɜ ȼ ɞɚɧɧɨɦ ɪɚɡɞɟɥɟ ɛɭɞɟɬ ɪɚɫɫɦɨɬɪɟɧɨ ɬɪɢ ɜɢɞɚ ɩɪɟɞɨɛɪɚɛɨɬɤɢ ɱɢɫɥɨɜɵɯ ɩɪɢɡɧɚɤɨɜ – ɦɨɞɭɥɹɪɧɵɣ, ɩɨɡɢɰɢɨɧɧɵɣ ɢ ɮɭɧɤɰɢɨɧɚɥɶɧɵɣ. Ɉɫɧɨɜɧɚɹ ɢɞɟɹ ɷɬɢɯ ɦɟɬɨɞɨɜ ɩɪɟɞɨɛɪɚɛɨɬɤɢ ɫɨɫɬɨɢɬ ɜ ɬɨɦ, ɱɬɨɛɵ ɫɞɟɥɚɬɶ ɡɧɚɱɢɦɵɦɢ ɦɚɥɵɟ ɨɬɥɢɱɢɹ ɛɨɥɶɲɢɯ ɜɟɥɢɱɢɧ. Ⱦɟɣɫɬɜɢɬɟɥɶɧɨ, ɩɭɫɬɶ ɞɥɹ ɨɬɜɟɬɚ ɫɭɳɟɫɬɜɟɧɧɨ ɢɡɦɟɧɟɧɢɟ ɜɟɥɢɱɢɧɵ ɩɪɢɡɧɚɤɚ ɧɚ ɟɞɢɧɢɰɭ ɩɪɢ ɡɧɚɱɟɧɢɢ ɩɪɢɡɧɚɤɚ ɩɨɪɹɞɤɚ ɦɢɥɥɢɨɧɚ. Ɉɱɟɜɢɞɧɨ, ɱɬɨ ɩɪɨɫɬɟɣɲɚɹ ɩɪɟɞɨɛɪɚɛɨɬɤɚ (1) ɫɞɟɥɚɟɬ ɨɬɥɢɱɢɟ ɜ ɟɞɢɧɢɰɭ ɧɟɪɚɡɥɢɱɢɦɵɦ ɞɥɹ ɧɟɣɪɨɧɧɨɣ ɫɟɬɢ ɩɪɢ ɚɛɫɨɥɸɬɧɵɯ ɡɧɚɱɟɧɢɹɯ ɩɨɪɹɞɤɚ ɦɢɥɥɢɨɧɚ. ȼɫɟ ɷɬɢ ɜɢɞɵ ɩɪɟɞɨɛɪɚɛɨɬɤɢ ɨɛɥɚɞɚɸɬ ɨɞɧɢɦ ɨɛɳɢɦ ɫɜɨɣɫɬɜɨɦ – ɡɚ ɫɱɟɬ ɤɨɞɢɪɨɜɚɧɢɹ ɜɯɨɞɧɨɝɨ ɩɪɢɡɧɚɤɚ ɧɟɫɤɨɥɶɤɢɦɢ ɫɢɝɧɚɥɚɦɢ ɨɧɢ ɭɦɟɧɶɲɚɸɬ ɫɥɨɠɧɨɫɬɶ ɡɚɞɚɱɢ (ɤɪɢɬɟɪɢɣ Ʌɢɩɲɢɰɚ).
4.1.11.1 Ɇɨɞɭɥɹɪɧɚɹ ɩɪɟɞɨɛɪɚɛɨɬɤɚ Ɂɚɞɚɞɢɦɫɹ ɧɟɤɨɬɨɪɵɦ ɧɚɛɨɪɨɦ ɩɨɥɨɠɢɬɟɥɶɧɵɯ ɱɢɫɟɥ y1 ,K, y k . Ɉɩɪɟɞɟɥɢɦ ɫɪɚɜɧɟɧɢɟ ɩɨ ɦɨɞɭɥɸ ɞɥɹ ɞɟɣɫɬɜɢɬɟɥɶɧɵɯ ɱɢɫɟɥ ɫɥɟɞɭɸɳɢɦ ɨɛɪɚɡɨɦ:
x mod y = x - y × Int ( x y) ,
ɝɞɟ
(15)
Int ( x ) – ɮɭɧɤɰɢɹ, ɜɵɱɢɫɥɹɸɳɚɹ ɰɟɥɭɸ ɱɚɫɬɶ ɜɟɥɢɱɢɧɵ x ɩɭɬɟɦ ɨɬɛɪɚɫɵɜɚɧɢɹ ɞɪɨɛɧɨɣ ɱɚɫɬɢ.
Ɉɱɟɜɢɞɧɨ, ɱɬɨ ɜɟɥɢɱɢɧɚ x
mod y ɥɟɠɢɬ ɜ ɢɧɬɟɪɜɚɥɟ ( - y, y) . Ʉɨɞɢɪɨɜɚɧɢɟ ɜɯɨɞɧɨɝɨ ɩɪɢɡɧɚɤɚ x ɩɪɢ
(( x
mod yi ) + yi )(b - a)
ɦɨɞɭɥɹɪɧɨɣ ɩɪɟɞɨɛɪɚɛɨɬɤɟ ɜɟɤɬɨɪɨɦ z ɩɪɨɢɡɜɨɞɢɬɫɹ ɩɨ ɫɥɟɞɭɸɳɟɣ ɮɨɪɦɭɥɟ:
zi =
2 yi
+a.
(16)
Ɉɞɧɚɤɨ ɦɨɞɭɥɹɪɧɚɹ ɩɪɟɞɨɛɪɚɛɨɬɤɚ ɨɛɥɚɞɚɟɬ ɨɞɧɢɦ ɨɬɪɢɰɚɬɟɥɶɧɵɦ ɫɜɨɣɫɬɜɨɦ – ɜɨ ɜɫɟɯ ɫɥɭɱɚɹɯ, r ɤɨɝɞɚ yi ¹ y1 , ɩɪɢ ɰɟɥɨɦ r , ɪɚɡɪɭɲɚɟɬɫɹ ɨɬɧɨɲɟɧɢɟ ɩɪɟɞɲɟɫɬɜɨɜɚɧɢɹ ɱɢɫɟɥ. ȼ ɬɚɛɥ. 8 ɩɪɢɜɟɞɟɧ ɩɪɢɦɟɪ ɜɟɤɬɨɪɨɜ. ɉɨɷɬɨɦɭ, ɦɨɞɭɥɶɧɚɹ Ɍɚɛɥɢɰɚ 8. ɩɪɟɞɨɛɪɚɛɨɬɤɚ ɩɪɢɝɨɞɧɚ ɩɪɢ ɩɪɟɞɨɛɉɪɢɦɟɪ ɫɢɝɧɚɥɨɜ ɩɪɢ ɦɨɞɭɥɹɪɧɨɦ ɜɜɨɞɟ ɪɚɛɨɬɤɟ ɬɟɯ ɩɪɢɡɧɚɤɨɜ, ɭ ɤɨɬɨɪɵɯ ɜɚɠx ɧɚ ɧɟ ɚɛɫɨɥɸɬɧɚɹ ɜɟɥɢɱɢɧɚ, ɚ ɜɡɚɢɦɨx mod 3 x mod 5 x mod 7 x mod 11 ɨɬɧɨɲɟɧɢɟ ɷɬɨɣ ɜɟɥɢɱɢɧɵ ɫ ɜɟɥɢɱɢɧɚ5 2 0 5 5 10 1 0 3 10 ɦɢ y1,K , y k . ɉɪɢɦɟɪɨɦ ɬɚɤɨɝɨ ɩɪɢ15 0 0 1 3 ɡɧɚɤɚ ɦɨɠɟɬ ɫɥɭɠɢɬɶ ɭɝɨɥ ɦɟɠɞɭ ɜɟɤɬɨɪɚɦɢ, ɟɫɥɢ ɜ ɤɚɱɟɫɬɜɟ ɜɟɥɢɱɢɧ y ɜɵɛɪɚɬɶ
yi = p i . 4.1.11.2 Ɏɭɧɤɰɢɨɧɚɥɶɧɚɹ ɩɪɟɞɨɛɪɚɛɨɬɤɚ
Ɏɭɧɤɰɢɨɧɚɥɶɧɚɹ ɩɪɟɞɨɛɪɚɛɨɬɤɚ ɩɪɟɫɥɟɞɭɟɬ ɟɞɢɧɫɬɜɟɧɧɭɸ ɰɟɥɶ – ɫɧɢɠɟɧɢɟ ɤɨɧɫɬɚɧɬɵ Ʌɢɩɲɢɰɚ ɡɚɞɚɱɢ. ȼ ɪɚɡɞɟɥɟ «ɉɪɟɞɨɛɪɚɛɨɬɤɚ, ɨɛɥɟɝɱɚɸɳɚɹ ɨɛɭɱɟɧɢɟ», ɛɵɥ ɩɪɢɜɟɞɟɧ ɩɪɢɦɟɪ ɬɚɤɨɣ ɩɪɟɞɨɛɪɚɛɨɬɤɢ. Ɋɚɫɫɦɨɬɪɢɦ ɨɛɳɢɣ ɫɥɭɱɚɣ ɮɭɧɤɰɢɨɧɚɥɶɧɨɣ ɩɪɟɞɨɛɪɚɛɨɬɤɢ, ɨɬɨɛɪɚɠɚɸɳɢɯ ɜɯɨɞɧɨɣ ɩɪɢɡɧɚɤ x ɜ k ɦɟɪɧɵɣ ɜɟɤɬɨɪ z . Ɂɚɞɚɞɢɦɫɹ ɧɚɛɨɪɨɦ ɢɡ k ɱɢɫɟɥ, ɭɞɨɜɥɟɬɜɨɪɹɸɳɢɯ ɫɥɟɞɭɸɳɢɦ ɭɫɥɨɜɢɹɦ: xmin < y1
[
]
xmin - y k , xmax - y1 , ɚ j min ,j max – ɦɢɧɢɦɚɥɶɧɨɟ ɢ ɦɚɤɫɢɦɚɥɶɧɨɟ ɡɧɚɱɟɧɢɹ ɮɭɧɤɰɢɢ j ɧɚ ɷɬɨɦ ɢɧɬɟɪɜɚɥɟ. Ɍɨɝɞɚ i -ɹ ɤɨɨɪɞɢɧɚɬɚ ɜɟɤɬɨɪɚ z ɜɵɱɢɫɥɹɟɬɫɹ ɩɨ ɫɥɟɞɭɸɳɟɣ ɮɨɪɦɭɥɟ:
zi =
CHAP4.DOC
(j ( x - yi ) - j min )(b - a ) + a j max - j min
61
(17)
ɚ
ɛ
1
1
0.5
0.5
0
0 0
ɜ
1
2
3
4
5
0
-0.5
-0.5
-1
-1
1
2
3
4
5
1 0.5 Ɋɢɫ. 2. Ƚɪɚɮɢɤɢ ɤɨɨɪɞɢɧɚɬɧɵɯ ɮɭɧɤɰɢɣ ɞɥɹ ɬɪɟɯ ɜɢɞɨɜ ɮɭɧɤɰɢɨɧɚɥɶɧɨɣɩɪɟɞɨɛɪɚɛɨɬɤɢ: ɚ) ɱɟɬɵɪɟɯɤɨɨɪɞɢɧɚɬɧɚɹ ɥɢɧɟɣɧɚɹ; ɛ) ɱɟɬɵɪɟɯɤɨɨɪɞɢɧɚɬɧɚɹ ɫɢɝɦɨɢɞɧɚɹ; ɜ) ɱɟɬɵɪɟɯɤɨɨɪɞɢɧɚɬɧɚɹ ɲɚɩɨɱɧɚɹ.
0 0
1
2
3
4
5
-0.5 -1
Ɍɚɛɥɢɰɚ 9 ɉɪɢɦɟɪ ɮɭɧɤɰɢɨɧɚɥɶɧɨɣ ɩɪɟɞɨɛɪɚɛɨɬɤɢ ɱɢɫɥɨɜɨɝɨ ɩɪɢɡɧɚ-
Ʌɢɧɟɣɧɚɹ ɩɪɟɞɨɛɪɚɛɨɬɤɚ . ȼ ɥɢɧɟɣɧɨɣ ɩɪɟɞɨɛɪɚɛɨɬɤɟ ɢɫɩɨɥɶɡɭɟɬɫɹ ɤɭɫɨɱɧɨ ɥɢɧɟɣɧɚɹ ɮɭɧɤɰɢɹ:
ɤɚ
ìa , x < a, ï zi = í x , a £ x £ b, (18) ïb, b < x. î Ƚɪɚɮɢɤɢ
x Î[0,5] , ɩɪɢ ɭɫɥɨɜɢɢ, ɱɬɨ ɫɢɝɧɚɥɵ ɧɟɣɪɨɧɨɜ ɩɪɢɧɚɞɥɟɠɚɬ ɢɧ-
ɬɟɪɜɚɥɭ
[ -11,] . ȼ ɫɢɝɦɨɢɞɧɨɣ ɩɪɟɞɨɛɪɚɛɨɬɤɟ ɢɫɩɨɥɶɡɨɜɚɧɚ ɮɨɪɦɭɥɚ
(
)
j ( x ) = x (1 + x ) , ɚ ɜ ɲɚɩɨɱɧɨɣ – j ( x) = 2 1 + x 2 - 1 . Ȼɵɥɢ
ɜɵɛɪɚɧɵ ɱɟɬɵɪɟ ɬɨɱɤɢ yi = i .
ɮɭɧɤɰɢɣ
z i ( x ) ɩɪɟɞɫɬɚɜɥɟɧɵ ɧɚ ɪɢɫ.
x
( )
( )
z1 x z2 x Ʌɢɧɟɣɧɚɹ ɩɪɟɞɨɛɪɚɛɨɬɤɚ 0.5 -0.5 1 1 ɋɢɝɦɨɢɞɧɚɹ ɩɪɟɞɨɛɪɚɛɨɬɤɚ 0.3333 -0.3333 0.7142 0.6 ɒɚɩɨɱɧɚɹ ɩɪɟɞɨɛɪɚɛɨɬɤɚ 0.6 0.6 -0.7241 -0.3846
z3 ( x )
z4 ( x )
2ɚ. ȼɢɞɧɨ, ɱɬɨ ɫ ɭɜɟɥɢɱɟɧɢ1.5 -1 -1 ɟɦ ɡɧɚɱɟɧɢɹ ɩɪɢɡɧɚɤɚ x ɧɢ 3.5 0.5 -0.5 ɨɞɧɚ ɮɭɧɤɰɢɹ ɧɟ ɭɛɵɜɚɟɬ, ɚ ɢɯ ɫɭɦɦɚ ɜɨɡɪɚɫɬɚɟɬ. ȼ ɬɚɛɥ. 1.5 -0.6 -0.7142 9 ɩɪɟɞɫɬɚɜɥɟɧɵ ɡɧɚɱɟɧɢɹ 3.5 0.3333 -0.3333 ɷɬɢɯ ɮɭɧɤɰɢɣ ɞɥɹ ɞɜɭɯ ɬɨɱɟɤ – x1 = 15 . ɢ x2 = 35 . . 1.5 -0.3846 -0.7241 ɋɢɝɦɨɢɞɧɚɹ ɩɪɟ3.5 0.6 0.6 ɞɨɛɪɚɛɨɬɤɚ . ȼ ɫɢɝɦɨɢɞɧɨɣ ɩɪɟɞɨɛɪɚɛɨɬɤɟ ɦɨɠɟɬ ɢɫɩɨɥɶɡɨɜɚɬɶɫɹ ɥɸɛɚɹ ɫɢɝɦɨɢɞɧɚɹ ɮɭɧɤɰɢɹ. ȿɫɥɢ ɜ ɤɚɱɟɫɬɜɟ ɫɢɝɦɨɢɞɧɨɣ ɮɭɧɤɰɢɢ ɢɫɩɨɥɶɡɨɜɚɬɶ ɮɭɧɤɰɢɸ S 2 , ɩɪɢɜɟɞɟɧɧɭɸ ɜ ɪɚɡɞɟɥɟ «ɇɟɣɪɨɧ» ɷɬɨɣ ɝɥɚɜɵ, ɬɨ ɮɨɪɦɭɥɚ (17) ɩɪɢɦɟɬ ɫɥɟɞɭɸɳɢɣ ɜɢɞ:
( )
zi =
( x ( c + x ) - 1)(b - a) + a . 2
Ƚɪɚɮɢɤɢ ɮɭɧɤɰɢɣ z i x ɩɪɟɞɫɬɚɜɥɟɧɵ ɧɚ ɪɢɫ. 2ɛ. ȼɢɞɧɨ, ɱɬɨ ɫ ɭɜɟɥɢɱɟɧɢɟɦ ɡɧɚɱɟɧɢɹ ɩɪɢɡɧɚɤɚ x ɧɢ ɨɞɧɚ ɮɭɧɤɰɢɹ ɧɟ ɭɛɵɜɚɟɬ, ɚ ɢɯ ɫɭɦɦɚ ɜɨɡɪɚɫɬɚɟɬ. ȼ ɬɚɛɥ. 9 ɩɪɟɞɫɬɚɜɥɟɧɵ ɡɧɚɱɟɧɢɹ ɷɬɢɯ ɮɭɧɤɰɢɣ ɞɥɹ ɞɜɭɯ ɬɨɱɟɤ x1 = 15 . , x 2 = 3.5 . ɒɚɩɨɱɧɚɹ ɩɪɟɞɨɛɪɚɛɨɬɤɚ . Ⱦɥɹ ɲɚɩɨɱɧɨɣ ɩɪɟɞɨɛɪɚɛɨɬɤɢ ɢɫɩɨɥɶɡɭɸɬɫɹ ɥɸɛɵɟ ɮɭɧɤɰɢɢ, 2 ɢɦɟɸɳɢɟ ɝɪɚɮɢɤ ɜ ɜɢɞɟ «ɲɚɩɨɱɤɢ». ɇɚɩɪɢɦɟɪ, ɮɭɧɤɰɢɹ j x = 1 1 + x . Ƚɪɚɮɢɤɢ ɮɭɧɤɰɢɣ z i x
( )
(
)
( )
( )
ɩɪɟɞɫɬɚɜɥɟɧɵ ɧɚ ɪɢɫ. 2ɜ. ȼɢɞɧɨ, ɱɬɨ ɫ ɭɜɟɥɢɱɟɧɢɟɦ ɡɧɚɱɟɧɢɹ ɩɪɢɡɧɚɤɚ x ɧɢ ɨɞɧɚ ɢɡ ɮɭɧɤɰɢɣ z i x , ɧɢ ɢɯ ɫɭɦɦɚ ɧɟ ɜɟɞɭɬ ɫɟɛɹ ɦɨɧɨɬɨɧɧɨ. ȼ ɬɚɛɥ. 9 ɩɪɟɞɫɬɚɜɥɟɧɵ ɡɧɚɱɟɧɢɹ ɷɬɢɯ ɮɭɧɤɰɢɣ ɞɥɹ ɞɜɭɯ ɬɨɱɟɤ x1 = 15 . , x 2 = 3.5 .
CHAP4.DOC
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4.1.11.3 ɉɨɡɢɰɢɨɧɧɚɹ ɩɪɟɞɨɛɪɚɛɨɬɤɚ Ɉɫɧɨɜɧɚɹ ɢɞɟɹ ɩɨɡɢɰɢɨɧɧɨɣ ɩɪɟɞɨɛɪɚɛɨɬɤɢ ɫɨɜɩɚɞɚɟɬ ɫ ɩɪɢɧɰɢɩɨɦ ɩɨɫɬɪɨɟɧɢɹ ɩɨɡɢɰɢɨɧɧɵɯ k ɫɢɫɬɟɦ ɫɱɢɫɥɟɧɢɹ. Ɂɚɞɚɞɢɦɫɹ ɩɨɥɨɠɢɬɟɥɶɧɨɣ ɜɟɥɢɱɢɧɨɣ y ɬɚɤɨɣ, ɱɬɨ y ³ x min - x max . ɋɞɜɢɧɟɦ ɩɪɢɡɧɚɤ x ɬɚɤ, ɱɬɨɛɵ ɨɧ ɩɪɢɧɢɦɚɥ ɬɨɥɶɤɨ ɧɟɨɬɪɢɰɚɬɟɥɶɧɵɟ ɡɧɚɱɟɧɢɹ. ȼ ɤɚɱɟɫɬɜɟ ɫɢɝɧɚɥɨɜ ɫɟɬɢ ɛɭɞɟɦ ɢɫɩɨɥɶɡɨɜɚɬɶ ɪɟɡɭɥɶɬɚɬ ɩɪɨɫɬɟɣɲɟɣ ɩɪɟɞɨɛɪɚɛɨɬɤɢ y -ɢɱɧɵɯ ɰɢɮɪ ɩɪɟɞɫɬɚɜɥɟɧɢɹ ɫɞɜɢɧɭɬɨɝɨ ɩɪɢɡɧɚɤɚ x . Ɏɨɪɦɭɥɵ ɜɵɱɢɫɥɟɧɢɹ ɰɢɮɪ ɩɪɢɜɟɞɟɧɵ ɧɢɠɟ:
(
)
z0 = ( x - x min ) mod y ,
z1 = Int ( ( x - x min ) y ) mod y , L
(
zi = Int ( x - xmin ) y
i
) mod y,
(19)
ɝɞɟ ɨɩɟɪɚɰɢɹ ɫɪɚɜɧɟɧɢɹ ɩɨ ɦɨɞɭɥɸ ɞɟɣɫɬɜɢɬɟɥɶɧɨɝɨ ɱɢɫɥɚ ɨɩɪɟɞɟɥɟɧɚ ɜ (15). ȼɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɫɟɬɢ ɩɨɥɭɱɚɸɬɫɹ ɢɡ ɤɨɦɩɨɧɟɧɬɨɜ ɜɟɤɬɨɪɚ z ɩɭɬɟɦ ɩɪɨɫɬɟɣɲɟɣ ɩɪɟɞɨɛɪɚɛɨɬɤɢ.
4.1.12 ɋɨɫɬɚɜɧɨɣ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ ɉɨɫɤɨɥɶɤɭ ɧɚ ɜɯɨɞ ɧɟɣɪɨɧɧɨɣ ɫɟɬɢ ɨɛɵɱɧɨ ɩɨɞɚɟɬɫɹ ɧɟɫɤɨɥɶɤɨ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ, ɤɚɠɞɵɣ ɢɡ ɤɨɬɨɪɵɯ ɨɛɪɚɛɚɬɵɜɚɟɬɫɹ ɫɜɨɢɦ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɨɦ, ɬɨ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ ɞɨɥɠɟɧ ɛɵɬɶ ɫɨɫɬɚɜɧɵɦ. ɉɪɟɞɫɬɚɜɢɦ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ ɜ ɜɢɞɟ ɫɨɜɨɤɭɩɧɨɫɬɢ ɧɟɡɚɜɢɫɢɦɵɯ ɱɚɫɬɧɵɯ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɨɜ. Ʉɚɠɞɵɣ ɱɚɫɬɧɵɣ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ ɨɛɪɚɛɚɬɵɜɚɟɬ ɨɞɧɨ ɢɥɢ ɧɟɫɤɨɥɶɤɨ ɬɟɫɧɨ ɫɜɹɡɚɧɧɵɯ ɜɯɨɞɧɵɯ ɞɚɧɧɵɯ. Ʉɚɤ ɭɠɟ ɨɬɦɟɱɚɥɨɫɶ ɪɚɧɟɟ, ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ ɦɨɠɟɬ ɢɦɟɬɶ ɨɞɢɧ ɢɡ ɱɟɬɵɪɟɯ ɬɢɩɨɜ, ɩɪɢɜɟɞɟɧɧɵɯ ɜ ɬɚɛɥ. 10. ɇɚ ɜɯɨɞɟ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ ɩɨɥɭɱɚɟɬ Ɍɚɛɥɢɰɚ 10. ɜɟɤɬɨɪ ɜɯɨɞɧɵɯ ɞɚɧɧɵɯ Ɍɢɩɵ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɨɜ (ɜɨɡɦɨɠɧɨ, ɫɨɫɬɨɹɳɢɣ ɢɡ ɌɢɩɈɩɢɫɚɧɢɟ ɨɞɧɨɝɨ ɷɥɟɦɟɧɬɚ), ɚ ɧɚ Number ɉɪɟɞɨɛɪɚɛɚɬɵɜɚɟɬ ɱɢɫɥɨɜɵɟ ɜɯɨɞɧɵɟ ɞɚɧɧɵɟ ɜɵɯɨɞɟ ɜɵɞɚɟɬ ɜɟɤɬɨɪ Unordered ɉɪɟɞɨɛɪɚɛɚɬɵɜɚɟɬ ɧɟɭɩɨɪɹɞɨɱɟɧɧɵɟ ɤɚɱɟɫɬɜɟɧɧɵɟ ɩɪɢɡɧɚɤɢ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɫɟɬɢ Ordered ɉɪɟɞɨɛɪɚɛɚɬɵɜɚɟɬ ɭɩɨɪɹɞɨɱɟɧɧɵɟ ɤɚɱɟɫɬɜɟɧɧɵɟ ɩɪɢɡɧɚɤɢ (ɬɚɤ ɠɟ ɜɨɡɦɨɠɧɨ ɫɨɫɬɨɹBinary Ɉɛɪɚɛɚɬɵɜɚɟɬ ɛɢɧɚɪɧɵɟ ɩɪɢɡɧɚɤɢ ɳɢɣ ɢɡ ɨɞɧɨɝɨ ɷɥɟɦɟɧɬɚ). ɇɟɨɛɯɨɞɢɦɨɫɬɶ ɩɟɪɟɞɚɱɢ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɭ ɜɟɤɬɨɪɚ ɜɯɨɞɧɵɯ ɞɚɧɧɵɯ ɢ ɩɨɥɭɱɟɧɢɹ ɨɬ ɧɟɝɨ ɜɟɤɬɨɪɚ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɫɜɹɡɚɧɚ ɫ ɬɟɦ, ɱɬɨ ɫɭɳɟɫɬɜɭɸɬ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɢ ɩɨɥɭɱɚɸɳɢɟ ɧɟɫɤɨɥɶɤɨ ɜɯɨɞɧɵɯ ɞɚɧɧɵɯ ɢ ɜɵɞɚɸɳɢɟ ɧɟɫɤɨɥɶɤɨ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ. ɉɪɢɦɟɪɨɦ ɬɚɤɨɝɨ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ ɦɨɠɟɬ ɫɥɭɠɢɬɶ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ, ɩɟɪɟɜɨɞɹɳɢɣ ɧɚɛɨɪ ɤɨɨɪɞɢɧɚɬ ɩɥɚɧɟɬɵ ɢɡ ɫɮɟɪɢɱɟɫɤɨɣ ɜ ɞɟɤɚɪɬɨɜɭ. Ⱦɥɹ ɤɚɱɟɫɬɜɟɧɧɵɯ ɩɪɢɡɧɚɤɨɜ ɩɪɢɧɹɬɨ ɤɨɞɢɪɨɜɚɧɢɟ ɞɥɢɧɧɵɦɢ ɰɟɥɵɦɢ ɱɢɫɥɚɦɢ. ɉɟɪɜɨɟ ɡɧɚɱɟɧɢɟ ɪɚɜɧɨ 1, ɜɬɨɪɨɟ – 2 ɢ ɬ.ɞ. ɑɢɫɥɨɜɵɟ ɩɪɢɡɧɚɤɢ ɤɨɞɢɪɭɸɬɫɹ ɞɟɣɫɬɜɢɬɟɥɶɧɵɦɢ ɱɢɫɥɚɦɢ.
4.2 ɋɬɚɧɞɚɪɬ ɩɟɪɜɨɝɨ ɭɪɨɜɧɹ ɤɨɦɩɨɧɟɧɬɚ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ Ⱦɚɧɧɵɣ ɪɚɡɞɟɥ ɩɨɫɜɹɳɟɧ ɨɩɢɫɚɧɢɸ ɫɬɚɧɞɚɪɬɚ ɹɡɵɤɚ ɨɩɢɫɚɧɢɹ ɢ ɯɪɚɧɟɧɢɹ ɧɚ ɜɧɟɲɧɟɦ ɧɨɫɢɬɟɥɟ ɤɨɦɩɨɧɟɧɬɚ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ. ɉɨɫɤɨɥɶɤɭ ɤɪɚɣɧɟ ɪɟɞɤɨ ɜɫɬɪɟɱɚɸɬɫɹ ɫɥɭɱɚɢ, ɤɨɝɞɚ ɫɟɬɶ ɩɨɥɭɱɚɟɬ ɨɞɢɧ ɜɯɨɞɧɨɣ ɫɢɝɧɚɥ, ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ ɜɫɟɝɞɚ ɹɜɥɹɟɬɫɹ ɫɨɫɬɚɜɧɵɦ. ɉɨɫɬɪɨɟɧɢɟ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ ɩɪɨɢɫɯɨɞɢɬ ɜ ɪɟɞɚɤɬɨɪɟ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ. Ⱦɥɹ ɨɩɢɫɚɧɢɹ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ ɩɪɟɞɥɚɝɚɟɬɫɹ ɢɫɩɨɥɶɡɨɜɚɬɶ ɫɩɟɰɢɚɥɶɧɵɣ ɹɡɵɤ.
4.2.1 ɇɟɨɩɪɟɞɟɥɟɧɧɵɟ ɡɧɚɱɟɧɢɹ ȼ ɩɪɚɤɬɢɤɟ ɪɚɛɨɬɵ ɛɨɥɶɲɢɧɫɬɜɨ ɬɚɛɥɢɰ ɞɚɧɧɵɯ ɧɟ ɩɨɥɧɵ. Ɍɨ ɟɫɬɶ, ɱɚɫɬɶ ɞɚɧɧɵɯ ɜ ɩɪɢɦɟɪɚɯ ɡɚɞɚɱɧɢɤɚ ɧɟɢɡɜɟɫɬɧɚ. Ɂɚɞɚɱɧɢɤ ɞɨɥɠɟɧ ɨɞɧɨɡɧɚɱɧɨ ɭɤɚɡɚɬɶ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɭ ɧɟɢɡɜɟɫɬɧɵɟ ɞɚɧɧɵɟ. Ⱦɥɹ ɷɬɢɯ ɰɟɥɟɣ ɞɥɹ ɤɚɠɞɨɝɨ ɬɢɩɚ ɜɯɨɞɧɵɯ ɞɚɧɧɵɯ ɨɩɪɟɞɟɥɟɧɨ ɫɩɟɰɢɚɥɶɧɨɟ ɡɧɚɱɟɧɢɟ - ɧɟɨɩɪɟɞɟɥɟɧɧɨɟ. Ⱦɥɹ ɩɟɪɟɞɚɱɢ ɧɟɢɡɜɟɫɬɧɵɯ ɡɧɚɱɟɧɢɣ ɢɫɩɨɥɶɡɭɸɬɫɹ ɫɥɟɞɭɸɳɢɟ ɜɟɥɢɱɢɧɵ: 10-40 ɞɥɹ ɞɟɣɫɬɜɢɬɟɥɶɧɵɯ ɱɢɫɟɥ ɢ 0 ɞɥɹ ɜɫɟɯ ɬɢɩɨɜ ɤɚɱɟɫɬɜɟɧɧɵɯ ɩɪɢɡɧɚɤɨɜ.
4.2.2 ɋɬɚɧɞɚɪɬɧɵɟ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɢ ȼ ɛɨɥɶɲɢɧɫɬɜɟ ɫɥɭɱɚɟɜ ɞɨɫɬɚɬɨɱɧɨ ɢɫɩɨɥɶɡɨɜɚɬɶ ɫɬɚɧɞɚɪɬɧɵɟ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɢ, ɫɩɢɫɨɤ ɤɨɬɨɪɵɯ ɩɪɢɜɟɞɟɧ ɜ ɬɚɛɥ. 11. ɇɢɠɟ ɜ ɞɚɧɧɨɦ ɪɚɡɞɟɥɟ ɩɪɢɜɟɞɟɧɨ ɨɩɢɫɚɧɢɟ ɩɚɪɚɦɟɬɪɨɜ ɫɬɚɧɞɚɪɬɧɵɯ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɨɜ.
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Ɍɚɛɥɢɰɚ 11 ɋɬɚɧɞɚɪɬɧɵɟ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɢ ɉɚɪɚɦɟɬɪɵɌɢɩɈɩɢɫɚɧɢɟ
ɂɞɟɧɬɢɮɢɤɚɬɨɪ BinaryPrep MinSignals, MaxSignals : Real; Binary Ȼɢɧɚɪɧɵɣ ɩɪɢɡɧɚɤ. ɉɪɟɞɨɛɪɚɛɨɬɤɚ ɜ ɫɨUnknown: Real; Type : Logic. ɨɬɜɟɬɫɬɜɢɢ ɫ ɬɚɛɥ. 4. UnOrdered MinSignals, MaxSignals : Real; Unordered ɇɟɭɩɨɪɹɞɨɱɟɧɧɵɣ ɤɚɱɟɫɬɜɟɧɧɵɣ ɩɪɢɡɧɚɤ. Unknown: Real; Num : Long ɉɪɟɞɨɛɪɚɛɨɬɤɚ ɜ ɫɨɨɬɜɟɬɫɬɜɢɢ ɫ ɬɚɛɥ. 5. Ordered MinSignals, MaxSignals : Real; Ordered ɍɩɨɪɹɞɨɱɟɧɧɵɣ ɤɚɱɟɫɬɜɟɧɧɵɣ ɩɪɢɡɧɚɤ. Unknown: Real; Num : Long ɉɪɟɞɨɛɪɚɛɨɬɤɚ ɜ ɫɨɨɬɜɟɬɫɬɜɢɢ ɫ ɬɚɛɥ. 6. EmptyPrep MinData, MaxData, Unnown, Number ɉɪɨɫɬɟɣɲɚɹ ɩɪɟɞɨɛɪɚɛɨɬɤɚ ɜ ɫɨɨɬɜɟɬɫɬMinSignals, MaxSignals : Real ɜɢɢ ɫ ɮɨɪɦɭɥɨɣ (1). ModPrep MinSignals, MaxSignals : Real; Number Ɇɨɞɭɥɹɪɧɚɹ ɩɪɟɞɨɛɪɚɛɨɬɤɚ ɜ ɫɨɨɬɜɟɬɫɬUnknown: Real; Y : RealArray ɜɢɢ ɫ ɮɨɪɦɭɥɨɣ (16). FuncPrep MinSignals, MaxSignals, Unknown: Number Ɏɭɧɤɰɢɨɧɚɥɶɧɚɹ ɩɪɟɞɨɛɪɚɛɨɬɤɚ ɜ ɫɨɨɬReal; Y : RealArray; F : FuncType ɜɟɬɫɬɜɢɢ ɫ ɮɨɪɦɭɥɨɣ (17). PositPrep MinSignals, MaxSignals, Unnown, Y : Number ɉɨɡɢɰɢɨɧɧɚɹ ɩɪɟɞɨɛɪɚɛɨɬɤɚ ɜ ɫɨɨɬɜɟɬɫɬReal; Num : Long ɜɢɢ ɫ ɮɨɪɦɭɥɨɣ (19). ȼɫɟ ɫɬɚɧɞɚɪɬɧɵɟ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɢ ɩɨɥɭɱɚɸɬ ɜ ɤɚɱɟɫɬɜɟ ɚɪɝɭɦɟɧɬɨɜ ɦɚɫɫɢɜɵ ɜɯɨɞɧɨɣ ɢɧɮɨɪɦɚɰɢɢ ɢ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ. Ʉɪɨɦɟ ɬɨɝɨ, ɨɧɢ ɫɨɞɟɪɠɚɬ ɪɚɡɥɢɱɧɵɟ ɧɚɛɨɪɵ ɩɚɪɚɦɟɬɪɨɜ. Ⱥɥɝɨɪɢɬɦɵ ɜɵɩɨɥɧɟɧɢɹ ɫɬɚɧɞɚɪɬɧɵɯ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɨɜ ɩɪɢɜɟɞɟɧɵ ɜ ɪɚɡɞɟɥɟ «ɉɪɢɦɟɪ ɨɩɢɫɚɧɢɹ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ». Ⱦɚɥɟɟ ɨɩɢɫɚɧɵ ɧɚɛɨɪɵ ɩɚɪɚɦɟɬɪɨɜ ɫɬɚɧɞɚɪɬɧɵɯ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɨɜ. ȼɫɟ ɩɚɪɚɦɟɬɪɵ ɞɨɥɠɧɵ ɛɵɬɶ ɨɩɢɫɚɧɵ ɤɚɤ ɫɬɚɬɢɱɟɫɤɢɟ ɩɟɪɟɦɟɧɧɵɟ. ɉɪɟɞɨɛɪɚɛɨɬɤɚ ɛɢɧɚɪɧɨɝɨ ɩɪɢɡɧɚɤɚ (BinaryPrep). ɉɪɟɞɨɛɪɚɛɨɬɤɚ ɩɪɨɢɡɜɨɞɢɬɫɹ ɜ ɫɨɨɬɜɟɬɫɬɜɢɢ ɫ ɬɚɛɥ. 4. ɉɪɢɧɢɦɚɟɬ ɨɞɧɨ ɜɯɨɞɧɨɟ ɞɚɧɧɨɟ ɢ ɝɟɧɟɪɢɪɭɟɬ ɨɞɢɧ ɜɯɨɞɧɨɣ ɫɢɝɧɚɥ. ɉɪɟɞɨɛɪɚɛɨɬɱɢɤ ɫɨɞɟɪɠɢɬ ɫɥɟɞɭɸɳɢɟ ɩɚɪɚɦɟɬɪɵ. MinSignals, MaxSignals – ɡɧɚɱɟɧɢɹ ɧɢɠɧɟɣ ɢ ɜɟɪɯɧɟɣ ɝɪɚɧɢɰ ɢɧɬɟɪɜɚɥɚ ɩɪɢɟɦɥɟɦɵɯ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ, ɫɨɨɬɜɟɬɫɬɜɟɧɧɨ. ɉɨ ɭɦɨɥɱɚɧɢɸ ɷɬɢ ɜɟɥɢɱɢɧɵ ɪɚɜɧɵ -1 ɢ 1, ɫɨɨɬɜɟɬɫɬɜɟɧɧɨ. Unknown– ɡɧɚɱɟɧɢɟ ɫɢɝɧɚɥɚ, ɤɨɬɨɪɵɣ ɛɭɞɟɬ ɜɵɞɚɧ, ɟɫɥɢ ɡɧɚɱɟɧɢɟ ɜɯɨɞɧɨɝɨ ɩɪɢɡɧɚɤɚ ɧɟ ɨɩɪɟɞɟɥɟɧɨ (0). ɉɨ ɭɦɨɥɱɚɧɢɸ ɷɬɚ ɜɟɥɢɱɢɧɚ ɪɚɜɧɚ MinSignals + MaxSignals 2 .
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Type – ɬɢɩ ɩɪɟɞɨɛɪɚɛɨɬɤɢ ɛɢɧɚɪɧɨɝɨ ɩɪɢɡɧɚɤɚ. ȿɫɥɢ ɡɧɚɱɟɧɢɟ ɩɚɪɚɦɟɬɪɚ Type – ɢɫɬɢɧɚ, ɬɨ ɩɪɨɢɡɜɨɞɢɬɫɹ ɩɪɟɞɨɛɪɚɛɨɬɤɚ ɩɨ ɬɢɩɭ «ɇɚɥɢɱɢɟ ɞɪɭɝɨɝɨ ɫɜɨɣɫɬɜɚ», ɟɫɥɢ ɥɨɠɶ, ɬɨ ɩɨ ɬɢɩɭ «Ɉɬɫɭɬɫɬɜɢɟ ɡɚɞɚɧɧɨɝɨ ɫɜɨɣɫɬɜɚ». ɉɨ ɭɦɨɥɱɚɧɢɸ ɡɧɚɱɟɧɢɟ ɷɬɨɝɨ ɩɚɪɚɦɟɬɪɚ ɪɚɜɧɨ ɢɫɬɢɧɚ. ɉɪɟɞɨɛɪɚɛɨɬɤɚ ɧɟɭɩɨɪɹɞɨɱɟɧɧɨɝɨ ɤɚɱɟɫɬɜɟɧɧɨɝɨ ɩɪɢɡɧɚɤɚ (UnOrdered). ɉɪɟɞɨɛɪɚɛɨɬɤɚ ɩɪɨɢɡɜɨɞɢɬɫɹ ɜ ɫɨɨɬɜɟɬɫɬɜɢɢ ɫ ɬɚɛɥ. 5. ɉɪɢɧɢɦɚɟɬ ɨɞɧɨ ɜɯɨɞɧɨɟ ɞɚɧɧɨɟ ɢ ɝɟɧɟɪɢɪɭɟɬ Num ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ. ɉɪɟɞɨɛɪɚɛɨɬɱɢɤ ɫɨɞɟɪɠɢɬ ɫɥɟɞɭɸɳɢɟ ɩɚɪɚɦɟɬɪɵ. MinSignals, MaxSignals – ɡɧɚɱɟɧɢɹ ɧɢɠɧɟɣ ɢ ɜɟɪɯɧɟɣ ɝɪɚɧɢɰ ɢɧɬɟɪɜɚɥɚ ɩɪɢɟɦɥɟɦɵɯ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ, ɫɨɨɬɜɟɬɫɬɜɟɧɧɨ. ɉɨ ɭɦɨɥɱɚɧɢɸ ɷɬɢ ɜɟɥɢɱɢɧɵ ɪɚɜɧɵ -1 ɢ 1, ɫɨɨɬɜɟɬɫɬɜɟɧɧɨ. Unknown– ɡɧɚɱɟɧɢɟ ɫɢɝɧɚɥɨɜ, ɤɨɬɨɪɵɟ ɛɭɞɭɬ ɜɵɞɚɧɵ, ɟɫɥɢ ɡɧɚɱɟɧɢɟ ɜɯɨɞɧɨɝɨ ɩɪɢɡɧɚɤɚ ɧɟ ɨɩɪɟɞɟɥɟɧɨ (0). ɉɨ ɭɦɨɥɱɚɧɢɸ ɷɬɚ ɜɟɥɢɱɢɧɚ ɪɚɜɧɚ MinSignals + MaxSignals 2 .
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Num – ɱɢɫɥɨ ɫɨɫɬɨɹɧɢɣ ɤɚɱɟɫɬɜɟɧɧɨɝɨ ɩɪɢɡɧɚɤɚ (ɱɢɫɥɨ ɝɟɧɟɪɢɪɭɟɦɵɯ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ). ɉɨ ɭɦɨɥɱɚɧɢɸ ɡɧɚɱɟɧɢɟ ɷɬɨɝɨ ɩɚɪɚɦɟɬɪɚ ɪɚɜɧɨ 2. ɉɪɟɞɨɛɪɚɛɨɬɤɚ ɭɩɨɪɹɞɨɱɟɧɧɨɝɨ ɤɚɱɟɫɬɜɟɧɧɨɝɨ ɩɪɢɡɧɚɤɚ (Ordered). ɉɪɟɞɨɛɪɚɛɨɬɤɚ ɩɪɨɢɡɜɨɞɢɬɫɹ ɜ ɫɨɨɬɜɟɬɫɬɜɢɢ ɫ ɬɚɛɥ. 6. ɉɪɢɧɢɦɚɟɬ ɨɞɧɨ ɜɯɨɞɧɨɟ ɞɚɧɧɨɟ ɢ ɝɟɧɟɪɢɪɭɟɬ Num ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ. ɉɪɟɞɨɛɪɚɛɨɬɱɢɤ ɫɨɞɟɪɠɢɬ ɫɥɟɞɭɸɳɢɟ ɩɚɪɚɦɟɬɪɵ. MinSignals, MaxSignals – ɡɧɚɱɟɧɢɹ ɧɢɠɧɟɣ ɢ ɜɟɪɯɧɟɣ ɝɪɚɧɢɰ ɢɧɬɟɪɜɚɥɚ ɩɪɢɟɦɥɟɦɵɯ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ, ɫɨɨɬɜɟɬɫɬɜɟɧɧɨ. ɉɨ ɭɦɨɥɱɚɧɢɸ ɷɬɢ ɜɟɥɢɱɢɧɵ ɪɚɜɧɵ -1 ɢ 1, ɫɨɨɬɜɟɬɫɬɜɟɧɧɨ. Unknown– ɡɧɚɱɟɧɢɟ ɫɢɝɧɚɥɨɜ, ɤɨɬɨɪɵɟ ɛɭɞɭɬ ɜɵɞɚɧɵ, ɟɫɥɢ ɡɧɚɱɟɧɢɟ ɜɯɨɞɧɨɝɨ ɩɪɢɡɧɚɤɚ ɧɟ ɨɩɪɟɞɟɥɟɧɨ (0). ɉɨ ɭɦɨɥɱɚɧɢɸ ɷɬɚ ɜɟɥɢɱɢɧɚ ɪɚɜɧɚ MinSignals + MaxSignals 2 Num – ɱɢɫɥɨ ɫɨɫɬɨɹɧɢɣ ɤɚɱɟɫɬɜɟɧɧɨɝɨ ɩɪɢɡɧɚɤɚ (ɱɢɫɥɨ ɝɟɧɟɪɢɪɭɟɦɵɯ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ). ɉɨ ɭɦɨɥɱɚɧɢɸ ɡɧɚɱɟɧɢɟ ɷɬɨɝɨ ɩɚɪɚɦɟɬɪɚ ɪɚɜɧɨ 2. ɉɪɨɫɬɟɣɲɢɣ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ (EmptyPrep). ɉɪɟɞɨɛɪɚɛɨɬɤɚ ɩɪɨɢɡɜɨɞɢɬɫɹ ɜ ɫɨɨɬɜɟɬɫɬɜɢɢ ɫ ɮɨɪɦɭɥɨɣ (1). ɉɪɢɧɢɦɚɟɬ ɨɞɧɨ ɜɯɨɞɧɨɟ ɞɚɧɧɨɟ ɢ ɝɟɧɟɪɢɪɭɟɬ ɨɞɢɧ ɜɯɨɞɧɨɣ ɫɢɝɧɚɥ. ɉɪɟɞɨɛɪɚɛɨɬɱɢɤ ɫɨɞɟɪɠɢɬ ɫɥɟɞɭɸɳɢɟ ɩɚɪɚɦɟɬɪɵ. MinSignals, MaxSignals – ɡɧɚɱɟɧɢɹ ɧɢɠɧɟɣ ɢ ɜɟɪɯɧɟɣ ɝɪɚɧɢɰ ɢɧɬɟɪɜɚɥɚ ɩɪɢɟɦɥɟɦɵɯ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ, ɫɨɨɬɜɟɬɫɬɜɟɧɧɨ. ɉɨ ɭɦɨɥɱɚɧɢɸ ɷɬɢ ɜɟɥɢɱɢɧɵ ɪɚɜɧɵ -1 ɢ 1, ɫɨɨɬɜɟɬɫɬɜɟɧɧɨ.
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Unknown– ɡɧɚɱɟɧɢɟ ɫɢɝɧɚɥɚ, ɤɨɬɨɪɵɣ ɛɭɞɟɬ ɜɵɞɚɧ, ɟɫɥɢ ɡɧɚɱɟɧɢɟ ɜɯɨɞɧɨɝɨ ɩɪɢɡɧɚɤɚ ɧɟ ɨɩɪɟɞɟɥɟɧɨ (10-40). ɉɨ ɭɦɨɥɱɚɧɢɸ ɷɬɚ ɜɟɥɢɱɢɧɚ ɪɚɜɧɚ 0. MinData, MaxData – ɡɧɚɱɟɧɢɹ ɧɢɠɧɟɣ ɢ ɜɟɪɯɧɟɣ ɝɪɚɧɢɰ ɢɧɬɟɪɜɚɥɚ ɢɡɦɟɧɟɧɢɹ ɜɯɨɞɧɵɯ ɞɚɧɧɵɯ, ɫɨɨɬɜɟɬɫɬɜɟɧɧɨ. ɉɨ ɭɦɨɥɱɚɧɢɸ ɷɬɢ ɜɟɥɢɱɢɧɵ ɪɚɜɧɵ -1 ɢ 1, ɫɨɨɬɜɟɬɫɬɜɟɧɧɨ. ɗɬɢ ɡɧɚɱɟɧɢɹ ɦɨɝɭɬ ɛɵɬɶ ɨɩɪɟɞɟɥɟɧɵ ɩɨɢɫɤɨɦ ɦɢɧɢɦɚɥɶɧɨɝɨ ɢ ɦɚɤɫɢɦɚɥɶɧɨɝɨ ɡɧɚɱɟɧɢɣ ɩɨ ɡɚɞɚɱɧɢɤɭ, ɨɞɧɚɤɨ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ ɧɟ ɦɨɠɟɬ ɜɵɩɨɥɧɢɬɶ ɷɬɭ ɩɪɨɰɟɞɭɪɭ. Ɇɨɞɭɥɹɪɧɵɣ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ (ModPrep). ɉɪɟɞɨɛɪɚɛɨɬɤɚ ɩɪɨɢɡɜɨɞɢɬɫɹ ɜ ɫɨɨɬɜɟɬɫɬɜɢɢ ɫ ɮɨɪɦɭɥɨɣ (16). ɉɪɢɧɢɦɚɟɬ ɨɞɧɨ ɜɯɨɞɧɨɟ ɞɚɧɧɨɟ ɢ ɝɟɧɟɪɢɪɭɟɬ ɫɬɨɥɶɤɨ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ, ɫɤɨɥɶɤɨ ɷɥɟɦɟɧɬɨɜ ɜ ɦɚɫɫɢɜɟ Y (ɧɭɥɟɜɨɣ ɷɥɟɦɟɧɬ ɦɚɫɫɢɜɚ ɫɨɞɟɪɠɢɬ ɱɢɫɥɨ ɷɥɟɦɟɧɬɨɜ). ɉɪɟɞɨɛɪɚɛɨɬɱɢɤ ɫɨɞɟɪɠɢɬ ɫɥɟɞɭɸɳɢɟ ɩɚɪɚɦɟɬɪɵ. MinSignals, MaxSignals – ɡɧɚɱɟɧɢɹ ɧɢɠɧɟɣ ɢ ɜɟɪɯɧɟɣ ɝɪɚɧɢɰ ɢɧɬɟɪɜɚɥɚ ɩɪɢɟɦɥɟɦɵɯ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ, ɫɨɨɬɜɟɬɫɬɜɟɧɧɨ. ɉɨ ɭɦɨɥɱɚɧɢɸ ɷɬɢ ɜɟɥɢɱɢɧɵ ɪɚɜɧɵ -1 ɢ 1, ɫɨɨɬɜɟɬɫɬɜɟɧɧɨ. Unknown– ɡɧɚɱɟɧɢɟ ɫɢɝɧɚɥɨɜ, ɤɨɬɨɪɵɟ ɛɭɞɭɬ ɜɵɞɚɧɵ, ɟɫɥɢ ɡɧɚɱɟɧɢɟ ɜɯɨɞɧɨɝɨ ɩɪɢɡɧɚɤɚ ɧɟ ɨɩɪɟɞɟɥɟɧɨ (10-40). ɉɨ ɭɦɨɥɱɚɧɢɸ ɷɬɚ ɜɟɥɢɱɢɧɚ ɪɚɜɧɚ 0. Y – ɦɚɫɫɢɜ ɜɟɥɢɱɢɧ, ɢɫɩɨɥɶɡɭɟɦɵɯ ɞɥɹ ɩɪɟɞɨɛɪɚɛɨɬɤɢ (ɫɦ. ɪɚɡɞɟɥ «Ɇɨɞɭɥɹɪɧɚɹ ɩɪɟɞɨɛɪɚɛɨɬɤɚ»). Ɏɭɧɤɰɢɨɧɚɥɶɧɵɣ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ (FuncPrep). ɉɪɟɞɨɛɪɚɛɨɬɤɚ ɩɪɨɢɡɜɨɞɢɬɫɹ ɜ ɫɨɨɬɜɟɬɫɬɜɢɢ ɫ ɮɨɪɦɭɥɨɣ (17). ɉɪɢɧɢɦɚɟɬ ɨɞɧɨ ɜɯɨɞɧɨɟ ɞɚɧɧɨɟ ɢ ɝɟɧɟɪɢɪɭɟɬ ɫɬɨɥɶɤɨ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ, ɫɤɨɥɶɤɨ ɷɥɟɦɟɧɬɨɜ ɜ ɦɚɫɫɢɜɟ Y (ɧɭɥɟɜɨɣ ɷɥɟɦɟɧɬ ɦɚɫɫɢɜɚ ɫɨɞɟɪɠɢɬ ɱɢɫɥɨ ɷɥɟɦɟɧɬɨɜ). ɉɪɟɞɨɛɪɚɛɨɬɱɢɤ ɫɨɞɟɪɠɢɬ ɫɥɟɞɭɸɳɢɟ ɩɚɪɚɦɟɬɪɵ. MinSignals, MaxSignals – ɡɧɚɱɟɧɢɹ ɧɢɠɧɟɣ ɢ ɜɟɪɯɧɟɣ ɝɪɚɧɢɰ ɢɧɬɟɪɜɚɥɚ ɩɪɢɟɦɥɟɦɵɯ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ, ɫɨɨɬɜɟɬɫɬɜɟɧɧɨ. ɉɨ ɭɦɨɥɱɚɧɢɸ ɷɬɢ ɜɟɥɢɱɢɧɵ ɪɚɜɧɵ -1 ɢ 1, ɫɨɨɬɜɟɬɫɬɜɟɧɧɨ. Unknown– ɡɧɚɱɟɧɢɟ ɫɢɝɧɚɥɨɜ, ɤɨɬɨɪɵɟ ɛɭɞɭɬ ɜɵɞɚɧɵ, ɟɫɥɢ ɡɧɚɱɟɧɢɟ ɜɯɨɞɧɨɝɨ ɩɪɢɡɧɚɤɚ ɧɟ ɨɩɪɟɞɟɥɟɧɨ (10-40). ɉɨ ɭɦɨɥɱɚɧɢɸ ɷɬɚ ɜɟɥɢɱɢɧɚ ɪɚɜɧɚ 0. MinData, MaxData – ɡɧɚɱɟɧɢɹ ɧɢɠɧɟɣ ɢ ɜɟɪɯɧɟɣ ɝɪɚɧɢɰ ɢɧɬɟɪɜɚɥɚ ɢɡɦɟɧɟɧɢɹ ɮɭɧɤɰɢɢ F ɨɬ ɜɯɨɞɧɵɯ ɞɚɧɧɵɯ, ɫɨɨɬɜɟɬɫɬɜɟɧɧɨ. ɉɨ ɭɦɨɥɱɚɧɢɸ ɷɬɢ ɜɟɥɢɱɢɧɵ ɪɚɜɧɵ -1 ɢ 1, ɫɨɨɬɜɟɬɫɬɜɟɧɧɨ. ɗɬɢ ɡɧɚɱɟɧɢɹ ɦɨɝɭɬ ɛɵɬɶ ɨɩɪɟɞɟɥɟɧɵ ɩɨɢɫɤɨɦ ɦɢɧɢɦɚɥɶɧɨɝɨ ɢ ɦɚɤɫɢɦɚɥɶɧɨɝɨ ɡɧɚɱɟɧɢɣ ɮɭɧɤɰɢɢ ɩɨ ɡɚɞɚɱɧɢɤɭ, ɨɞɧɚɤɨ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ ɧɟ ɦɨɠɟɬ ɜɵɩɨɥɧɢɬɶ ɷɬɭ ɩɪɨɰɟɞɭɪɭ. Y – ɦɚɫɫɢɜ ɜɟɥɢɱɢɧ, ɢɫɩɨɥɶɡɭɟɦɵɯ ɞɥɹ ɩɪɟɞɨɛɪɚɛɨɬɤɢ (ɫɦ. ɪɚɡɞɟɥ «Ɏɭɧɤɰɢɨɧɚɥɶɧɚɹ ɩɪɟɞɨɛɪɚɛɨɬɤɚ»). F – ɢɦɹ ɨɞɧɨɩɚɪɚɦɟɬɪɢɱɟɫɤɨɣ ɮɭɧɤɰɢɢ ɞɟɣɫɬɜɢɬɟɥɶɧɨɝɨ ɬɢɩɚ (ɟɟ ɚɞɪɟɫ) ɢɫɩɨɥɶɡɭɟɦɨɣ ɞɥɹ ɩɪɟɞɨɛɪɚɛɨɬɤɢ. ɉɨɡɢɰɢɨɧɧɵɣ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ (PositPrep). ɉɪɟɞɨɛɪɚɛɨɬɤɚ ɩɪɨɢɡɜɨɞɢɬɫɹ ɜ ɫɨɨɬɜɟɬɫɬɜɢɢ ɫ ɮɨɪɦɭɥɨɣ (19). ɉɪɢɧɢɦɚɟɬ ɨɞɧɨ ɜɯɨɞɧɨɟ ɞɚɧɧɨɟ ɢ ɝɟɧɟɪɢɪɭɟɬ Num ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ. ɉɪɟɞɨɛɪɚɛɨɬɱɢɤ ɫɨɞɟɪɠɢɬ ɫɥɟɞɭɸɳɢɟ ɩɚɪɚɦɟɬɪɵ. MinSignals, MaxSignals – ɡɧɚɱɟɧɢɹ ɧɢɠɧɟɣ ɢ ɜɟɪɯɧɟɣ ɝɪɚɧɢɰ ɢɧɬɟɪɜɚɥɚ ɩɪɢɟɦɥɟɦɵɯ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ, ɫɨɨɬɜɟɬɫɬɜɟɧɧɨ. ɉɨ ɭɦɨɥɱɚɧɢɸ ɷɬɢ ɜɟɥɢɱɢɧɵ ɪɚɜɧɵ -1 ɢ 1, ɫɨɨɬɜɟɬɫɬɜɟɧɧɨ. Unknown– ɡɧɚɱɟɧɢɟ ɫɢɝɧɚɥɨɜ, ɤɨɬɨɪɵɟ ɛɭɞɭɬ ɜɵɞɚɧɵ, ɟɫɥɢ ɡɧɚɱɟɧɢɟ ɜɯɨɞɧɨɝɨ ɩɪɢɡɧɚɤɚ ɧɟ ɨɩɪɟɞɟɥɟɧɨ (10-40). ɉɨ ɭɦɨɥɱɚɧɢɸ ɷɬɚ ɜɟɥɢɱɢɧɚ ɪɚɜɧɚ 0. Y – ɨɫɧɨɜɚɧɢɟ ɫɢɫɬɟɦɵ ɫɱɢɫɥɟɧɢɹ (ɫɦ. ɪɚɡɞɟɥ «Ɏɭɧɤɰɢɨɧɚɥɶɧɚɹ ɩɪɟɞɨɛɪɚɛɨɬɤɚ»). ɉɨ ɭɦɨɥɱɚɧɢɸ ɷɬɚ ɜɟɥɢɱɢɧɚ ɪɚɜɧɚ 2. Num – ɱɢɫɥɨ ɰɢɮɪ ɜ ɩɪɟɞɫɬɚɜɥɟɧɢɢ ɜɯɨɞɧɨɝɨ ɫɢɝɧɚɥɚ. ɉɨ ɭɦɨɥɱɚɧɢɸ ɷɬɚ ɜɟɥɢɱɢɧɚ ɪɚɜɧɚ 2.
4.2.3 əɡɵɤ ɨɩɢɫɚɧɢɹ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ ɉɪɟɞɨɛɪɚɛɨɬɱɢɤ ɹɜɥɹɟɬɫɹ ɫɨɫɬɚɜɧɵɦ ɨɛɴɟɤɬɨɦ. ȼ ɫɨɫɬɚɜ ɷɬɨɝɨ ɨɛɴɟɤɬɚ ɜɯɨɞɹɬ ɱɚɫɬɧɵɟ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɢ, ɩɪɚɜɢɥɚ ɪɚɫɩɪɟɞɟɥɟɧɢɹ ɜɯɨɞɧɵɯ ɞɚɧɧɵɯ ɢ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɫɟɬɢ ɦɟɠɞɭ ɱɚɫɬɧɵɦɢ ɩɪɟɞɨɛɌɚɛɥɢɰɚ 12 Ʉɥɸɱɟɜɵɟ ɫɥɨɜɚ ɹɡɵɤɚ ɨɩɢɫɚɧɢɹ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ. ɂɞɟɧɬɢɮɢɤɚɬɨɪɄɪɚɬɤɨɟ ɨɩɢɫɚɧɢɟ Connections ɇɚɱɚɥɨ ɛɥɨɤɚ ɨɩɢɫɚɧɢɹ ɪɚɫɩɪɟɞɟɥɟɧɢɹ ɜɯɨɞɧɵɯ ɞɚɧɧɵɯ ɢ ɫɢɝɧɚɥɨɜ. Contents ɇɚɱɚɥɨ ɛɥɨɤɚ ɨɩɢɫɚɧɢɹ ɫɨɫɬɚɜɚ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ. Data ɂɦɹ, ɩɨ ɤɨɬɨɪɨɦɭ ɚɞɪɟɫɭɸɬɫɹ ɜɯɨɞɧɵɟ ɞɚɧɧɵɟ, ɧɚɱɚɥɨ ɛɥɨɤɚ ɨɩɢɫɚɧɢɹ ɜɯɨɞɧɵɯ ɞɚɧɧɵɯ Include ɉɪɟɞɲɟɫɬɜɭɟɬ ɢɦɟɧɢ ɮɚɣɥɚ, ɰɟɥɢɤɨɦ ɜɫɬɚɜɥɹɟɦɨɝɨ ɜ ɷɬɨ ɦɟɫɬɨ ɨɩɢɫɚɧɢɹ. NumberOf Ɏɭɧɤɰɢɹ. ȼɨɡɜɪɚɳɚɟɬ ɱɢɫɥɨ ɨɛɪɚɛɚɬɵɜɚɟɦɵɯ ɱɚɫɬɧɵɦ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɨɦ ɜɯɨɞɧɵɯ ɞɚɧɧɵɯ ɢɥɢ ɫɢɝɧɚɥɨɜ. Prep ɇɚɱɚɥɨ ɡɚɝɨɥɨɜɤɚ ɨɩɢɫɚɧɢɹ ɱɚɫɬɧɨɝɨ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ. Preparator Ɂɚɝɨɥɨɜɨɤ ɪɚɡɞɟɥɚ ɮɚɣɥɚ, ɫɨɞɟɪɠɚɳɢɣ ɨɩɢɫɚɧɢɟ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ. Signals ɂɦɹ, ɩɨ ɤɨɬɨɪɨɦɭ ɚɞɪɟɫɭɸɬɫɹ ɜɯɨɞɧɵɟ ɫɢɝɧɚɥɵ; ɧɚɱɚɥɨ ɛɥɨɤɚ ɨɩɢɫɚɧɢɹ ɫɢɝɧɚɥɨɜ.
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ɪɚɛɨɬɱɢɤɚɦɢ. ɉɪɟɞɨɛɪɚɛɨɬɱɢɤ ɩɪɢ ɜɵɩɨɥɧɟɧɢɢ ɡɚɩɪɨɫɚ ɧɚ ɩɪɟɞɨɛɪɚɛɨɬɤɭ ɜɟɤɬɨɪɚ ɜɯɨɞɧɵɯ ɞɚɧɧɵɯ ɩɨɥɭɱɚɟɬ ɧɚ ɜɯɨɞɟ ɜɟɤɬɨɪ ɢɫɯɨɞɧɵɯ ɞɚɧɧɵɯ, ɚ ɜɨɡɜɪɚɳɚɟɬ ɜɟɤɬɨɪ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɫɟɬɢ. Ʉɚɠɞɵɣ ɱɚɫɬɧɵɣ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɨɬɜɟɬɚ ɩɨɥɭɱɚɟɬ ɧɚ ɜɯɨɞɟ ɜɟɤɬɨɪ ɜɯɨɞɧɵɯ ɞɚɧɧɵɯ, ɤɨɬɨɪɵɟ ɨɧ ɩɪɟɞɨɛɪɚɛɚɬɵɜɚɟɬ, ɚ ɧɚ ɜɵɯɨɞɟ ɞɚɟɬ ɜɟɤɬɨɪ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɫɟɬɢ. Ʉɚɠɞɵɣ ɱɚɫɬɧɵɣ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɨɩɢɫɵɜɚɟɬɫɹ ɜ ɜɢɞɟ ɩɪɨɰɟɞɭɪɧɨɝɨ ɛɥɨɤɚ. ȼ ɬɚɛɥ. 12 ɩɪɢɜɟɞɟɧ ɫɩɢɫɨɤ ɤɥɸɱɟɜɵɯ ɫɥɨɜ ɹɡɵɤɚ ɨɩɢɫɚɧɢɹ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ, ɞɨɩɨɥɧɹɸɳɢɣ ɫɩɢɫɨɤ ɤɥɸɱɟɜɵɯ ɫɥɨɜ, ɩɪɢɜɟɞɟɧɧɵɯ ɜ ɝɥɚɜɟ «Ɉɛɳɢɣ ɫɬɚɧɞɚɪɬ». Ʉɪɨɦɟ ɬɨɝɨ, ɤɥɸɱɟɜɵɦɢ ɫɥɨɜɚɦɢ ɹɜɥɹɸɬɫɹ ɢɦɟɧɚ ɫɬɚɧɞɚɪɬɧɵɯ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɨɜ, ɩɪɢɜɟɞɟɧɧɵɟ ɜ ɬɚɛɥ. 11.
4.2.3.1 ȻɇɎ ɹɡɵɤɚ ɨɩɢɫɚɧɢɹ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ Ɉɛɨɡɧɚɱɟɧɢɹ, ɩɪɢɧɹɬɵɟ ɜ ɞɚɧɧɨɦ ɪɚɫɲɢɪɟɧɢɢ ȻɇɎ ɢ ɨɩɢɫɚɧɢɟ ɪɹɞɚ ɤɨɧɫɬɪɭɤɰɢɣ ɩɪɢɜɟɞɟɧɵ ɜ ɝɥɚɜɟ «Ɉɛɳɢɣ ɫɬɚɧɞɚɪɬ» ɜ ɪɚɡɞɟɥɟ «Ɉɩɢɫɚɧɢɟ ɹɡɵɤɚ ɨɩɢɫɚɧɢɹ ɤɨɦɩɨɧɟɧɬɨɜ». <Ɉɩɢɫɚɧɢɟ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ> ::= <Ɂɚɝɨɥɨɜɨɤ> [<Ɉɩɢɫɚɧɢɟ ɮɭɧɤɰɢɣ>] [<Ɉɩɢɫɚɧɢɟ ɱɚɫɬɧɵɯ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɨɜ>] <Ɉɩɢɫɚɧɢɟ ɫɨɫɬɚɜɚ> [<ɍɫɬɚɧɨɜɥɟɧɢɟ ɩɚɪɚɦɟɬɪɨɜ>] [<Ɉɩɢɫɚɧɢɟ ɫɢɝɧɚɥɨɜ>] [<Ɉɩɢɫɚɧɢɟ ɞɚɧɧɵɯ>] [<Ɉɩɢɫɚɧɢɟ ɪɚɫɩɪɟɞɟɥɟɧɢɹ ɫɢɝɧɚɥɨɜ>] [<Ɉɩɢɫɚɧɢɟ ɪɚɫɩɪɟɞɟɥɟɧɢɹ ɞɚɧɧɵɯ>] <Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ> <Ɂɚɝɨɥɨɜɨɤ> ::= Preparator <ɂɦɹ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ> ( <ɋɩɢɫɨɤ ɮɨɪɦɚɥɶɧɵɯ ɚɪɝɭɦɟɧɬɨɜ> ) <ɂɦɹ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ> ::= <ɂɞɟɧɬɢɮɢɤɚɬɨɪ> <Ɉɩɢɫɚɧɢɟ ɱɚɫɬɧɵɯ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɨɜ> ::= <Ɉɩɢɫɚɧɢɟ ɱɚɫɬɧɨɝɨ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ> [<Ɉɩɢɫɚɧɢɟ ɱɚɫɬɧɵɯ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɨɜ>] <Ɉɩɢɫɚɧɢɟ ɱɚɫɬɧɨɝɨ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ> ::= <Ɂɚɝɨɥɨɜɨɤ ɨɩɢɫɚɧɢɹ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ> [<Ɉɩɢɫɚɧɢɟ ɫɬɚɬɢɱɟɫɤɢɯ ɩɟɪɟɦɟɧɧɵɯ>] [<Ɉɩɢɫɚɧɢɟ ɩɟɪɟɦɟɧɧɵɯ>] <Ɍɟɥɨ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ> <Ɂɚɝɨɥɨɜɨɤ ɨɩɢɫɚɧɢɹ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ> ::= Prep <ɂɦɹ ɱɚɫɬɧɨɝɨ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ> ([(<ɋɩɢɫɨɤ ɮɨɪɦɚɥɶɧɵɯ ɚɪɝɭɦɟɧɬɨɜ>)]) <ɂɦɹ ɱɚɫɬɧɨɝɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ> ::= <ɂɞɟɧɬɢɮɢɤɚɬɨɪ> <Ɍɟɥɨ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ> ::= Begin <ɋɨɫɬɚɜɧɨɣ ɨɩɟɪɚɬɨɪ> End <Ɉɩɢɫɚɧɢɟ ɫɨɫɬɚɜɚ> ::= Contents <ɋɩɢɫɨɤ ɢɦɟɧ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɨɜ> ; <ɋɩɢɫɨɤ ɢɦɟɧ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɨɜ> ::= <ɂɦɹ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ> [,<ɋɩɢɫɨɤ ɢɦɟɧ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɨɜ>] <ɂɦɹ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ> ::= <ɉɫɟɜɞɨɧɢɦ>: {<ɂɦɹ ɪɚɧɟɟ ɨɩɢɫɚɧɧɨɝɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ> ½ <ɂɦɹ ɫɬɚɧɞɚɪɬɧɨɝɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ>} [[<ɑɢɫɥɨ ɷɤɡɟɦɩɥɹɪɨɜ >] ] [(<ɋɩɢɫɨɤ ɮɚɤɬɢɱɟɫɤɢɯ ɚɪɝɭɦɟɧɬɨɜ>)] <ɉɫɟɜɞɨɧɢɦ> ::= <ɂɞɟɧɬɢɮɢɤɚɬɨɪ> <ɑɢɫɥɨ ɷɤɡɟɦɩɥɹɪɨɜ > ::= <ɐɟɥɨɟ ɱɢɫɥɨ> <ɂɦɹ ɪɚɧɟɟ ɨɩɢɫɚɧɧɨɝɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ> ::= <ɂɞɟɧɬɢɮɢɤɚɬɨɪ> <ɂɦɹ ɫɬɚɧɞɚɪɬɧɨɝɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ> ::= <ɂɞɟɧɬɢɮɢɤɚɬɨɪ> <ɍɫɬɚɧɨɜɥɟɧɢɟ ɩɚɪɚɦɟɬɪɨɜ> ::= <ɍɫɬɚɧɨɜɥɟɧɢɟ ɩɚɪɚɦɟɬɪɨɜ ɑɚɫɬɧɨɝɨ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ > [;<ɍɫɬɚɧɨɜɥɟɧɢɟ ɩɚɪɚɦɟɬɪɨɜ>] <Ɉɩɢɫɚɧɢɟ ɫɢɝɧɚɥɨɜ> ::= Signals <Ʉɨɧɫɬɚɧɬɧɨɟ ɜɵɪɚɠɟɧɢɟ ɬɢɩɚ Long> <Ɉɩɢɫɚɧɢɟ ɞɚɧɧɵɯ> ::= Data <Ʉɨɧɫɬɚɧɬɧɨɟ ɜɵɪɚɠɟɧɢɟ ɬɢɩɚ Long> <Ɉɩɢɫɚɧɢɟ ɪɚɫɩɪɟɞɟɥɟɧɢɹ ɫɢɝɧɚɥɨɜ> ::= <Ɉɩɢɫɚɧɢɟ ɪɚɫɩɪɟɞɟɥɟɧɢɹ ɋɢɝɧɚɥɨɜ, ɉɪɟɞɨɛɪɚɛɨɬɱɢɤɚ, ɑɚɫɬɧɨɝɨ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ, Signals> <Ɉɩɢɫɚɧɢɟ ɪɚɫɩɪɟɞɟɥɟɧɢɹ ɞɚɧɧɵɯ> ::= <Ɉɩɢɫɚɧɢɟ ɪɚɫɩɪɟɞɟɥɟɧɢɹ Ⱦɚɧɧɵɯ, ɉɪɟɞɨɛɪɚɛɨɬɱɢɤɚ, ɑɚɫɬɧɨɝɨ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ, Data> <Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ> ::= End Preparator
4.2.3.2 Ɉɩɢɫɚɧɢɟ ɹɡɵɤɚ ɨɩɢɫɚɧɢɹ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ ɋɬɪɭɤɬɭɪɚ ɨɩɢɫɚɧɢɹ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ ɢɦɟɟɬ ɜɢɞ: ɡɚɝɨɥɨɜɨɤ; ɨɩɢɫɚɧɢɟ ɮɭɧɤɰɢɣ; ɨɩɢɫɚɧɢɟ ɱɚɫɬɧɵɯ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɨɜ; ɨɩɢɫɚɧɢɟ ɫɨɫɬɚɜɚ; ɭɫɬɚɧɨɜɥɟɧɢɟ ɩɚɪɚɦɟɬɪɨɜ; ɨɩɢɫɚɧɢɟ ɫɢɝɧɚɥɨɜ; ɨɩɢɫɚɧɢɟ ɞɚɧɧɵɯ; ɨɩɢɫɚɧɢɟ ɪɚɫɩɪɟɞɟɥɟɧɢɹ ɫɢɝɧɚɥɨɜ; ɨɩɢɫɚɧɢɟ ɪɚɫɩɪɟɞɟɥɟɧɢɹ ɞɚɧɧɵɯ; ɤɨɧɟɰ ɨɩɢɫɚɧɢɹ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ. Ɂɚɝɨɥɨɜɨɤ ɫɨɫɬɨɢɬ ɢɡ ɤɥɸɱɟɜɨɝɨ ɫɥɨɜɚ Preparator ɢ ɢɦɟɧɢ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ ɢ ɫɥɭɠɢɬ ɞɥɹ ɨɛɨɡɧɚɱɟɧɢɹ ɧɚɱɚɥɚ ɨɩɢɫɚɧɢɹ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ ɜ ɮɚɣɥɟ, ɫɨɞɟɪɠɚɳɟɦ ɧɟɫɤɨɥɶɤɨ ɤɨɦɩɨɧɟɧɬɨɜ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚ. Ɉɩɢɫɚɧɢɟ ɮɭɧɤɰɢɣ – ɮɪɚɝɦɟɧɬ ɨɩɢɫɚɧɢɹ, ɜ ɤɨɬɨɪɨɦ ɨɩɢɫɚɧɵ ɮɭɧɤɰɢɢ, ɧɟɨɛɯɨɞɢɦɵɟ ɞɥɹ ɪɚɛɨɬɵ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɨɜ. Ɉɩɢɫɚɧɢɟ ɱɚɫɬɧɨɝɨ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ – ɷɬɨ ɨɩɢɫɚɧɢɟ ɩɪɨɰɟɞɭɪɵ, ɜɵɱɢɫɥɹɸɳɟɣ ɜɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɧɟɣɪɨɧɧɨɣ ɫɟɬɢ ɩɨ ɜɯɨɞɧɵɦ ɞɚɧɧɵɦ. Ɏɨɪɦɚɥɶɧɵɟ ɚɪɝɭɦɟɧɬɵ ɫɥɭɠɚɬ ɞɥɹ ɡɚɞɚɧɢɹ ɪɚɡɦɟɪɧɨɫɬɟɣ ɨɛɪɚɛɚɬɵɜɚɟɦɵɯ ɜɟɤɬɨɪɨɜ. ɉɪɢ ɜɵɩɨɥɧɟɧɢɢ ɱɚɫɬɧɵɣ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ ɩɨɥɭɱɚɟɬ ɜ ɤɚɱɟɫɬɜɟ ɚɪɝɭɦɟɧɬɨɜ ɞɜɚ ɜɟɤɬɨɪɚ – ɜɯɨɞɧɵɯ ɞɚɧɧɵɯ ɢ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ. Ɏɨɪɦɚɥɶɧɨ, ɩɪɢ ɢɫɩɨɥɧɟɧɢɢ ɱɚɫɬɧɵɣ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ ɢɦɟɟɬ ɨɩɢɫɚɧɢɟ ɫɥɟɞɭɸɳɟɝɨ ɜɢɞɚ:
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Pascal: Procedure Preparator(Data, Signals : PRealArray); C: void Preparator(PRealArray Data; PRealArray Signals); ȼ ɪɚɡɞɟɥɟ ɨɩɢɫɚɧɢɹ ɫɨɫɬɚɜɚ ɩɟɪɟɱɢɫɥɹɸɬɫɹ ɱɚɫɬɧɵɟ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɢ, ɜɯɨɞɹɳɢɟ ɜ ɫɨɫɬɚɜ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ. ɉɪɢɡɧɚɤɨɦ ɤɨɧɰɚ ɪɚɡɞɟɥɚ ɫɥɭɠɢɬ ɫɢɦɜɨɥ «;». ȼ ɧɟɨɛɹɡɚɬɟɥɶɧɨɦ ɪɚɡɞɟɥɟ ɭɫɬɚɧɨɜɥɟɧɢɹ ɩɚɪɚɦɟɬɪɨɜ ɩɪɨɢɡɜɨɞɢɬɫɹ ɡɚɞɚɧɢɟ ɡɧɚɱɟɧɢɣ ɩɚɪɚɦɟɬɪɨɜ (ɫɬɚɬɢɱɟɫɤɢɯ ɩɟɪɟɦɟɧɧɵɯ) ɱɚɫɬɧɵɯ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɨɜ. ɉɨɫɥɟ ɤɥɸɱɟɜɨɝɨ ɫɥɨɜɚ SetParameters ɫɥɟɞɭɟɬ ɫɩɢɫɨɤ ɡɧɚɱɟɧɢɣ ɩɚɪɚɦɟɬɪɨɜ ɜ ɬɨɦ ɩɨɪɹɞɤɟ, ɜ ɤɚɤɨɦ ɩɚɪɚɦɟɬɪɵ ɛɵɥɢ ɨɛɴɹɜɥɟɧɵ ɩɪɢ ɨɩɢɫɚɧɢɢ ɱɚɫɬɧɨɝɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ (ɞɥɹ ɫɬɚɧɞɚɪɬɧɵɯ ɢɧɬɟɪɩɪɟɬɚɬɨɪɨɜ ɩɨɪɹɞɨɤ ɩɚɪɚɦɟɬɪɨɜ ɫɨɨɬɜɟɬɫɬɜɭɟɬ ɩɨɪɹɞɤɭ, ɩɪɢɜɟɞɟɧɧɨɦɭ ɜ ɨɩɢɫɚɧɢɢ ɫɬɚɧɞɚɪɬɧɵɯ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɨɜ ɜ ɪɚɡɞɟɥɟ «ɋɬɚɧɞɚɪɬɧɵɟ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɢ»). ɉɪɢ ɢɫɩɨɥɶɡɨɜɚɧɢɢ ɨɞɧɨɝɨ ɨɩɟɪɚɬɨɪɚ ɡɚɞɚɧɢɹ ɩɚɪɚɦɟɬɪɨɜ ɞɥɹ ɡɚɞɚɧɢɹ ɩɚɪɚɦɟɬɪɨɜ ɧɟɫɤɨɥɶɤɢɦ ɷɤɡɟɦɩɥɹɪɚɦ ɨɞɧɨɝɨ ɱɚɫɬɧɨɝɨ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ ɩɨɫɥɟ ɤɥɸɱɟɜɨɝɨ ɫɥɨɜɚ SetParameters ɭɤɚɡɵɜɚɟɬɫɹ ɫɬɨɥɶɤɨ ɜɵɪɚɠɟɧɢɣ, ɡɚɞɚɸɳɢɯ ɡɧɚɱɟɧɢɹ ɩɚɪɚɦɟɬɪɨɜ, ɫɤɨɥɶɤɨ ɧɟɨɛɯɨɞɢɦɨ ɞɥɹ ɨɞɧɨɝɨ ɷɤɡɟɦɩɥɹɪɚ. ȼ ɧɟɨɛɹɡɚɬɟɥɶɧɨɦ ɪɚɡɞɟɥɟ "ɨɩɢɫɚɧɢɟ ɫɢɝɧɚɥɨɜ" ɭɤɚɡɵɜɚɟɬɫɹ ɱɢɫɥɨ ɫɢɝɧɚɥɨɜ, ɜɵɱɢɫɥɹɟɦɵɯ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɨɦ. ȿɫɥɢ ɷɬɨɬ ɪɚɡɞɟɥ ɨɩɭɳɟɧ, ɬɨ ɩɨɥɚɝɚɟɬɫɹ, ɱɬɨ ɱɢɫɥɨ ɜɵɱɢɫɥɹɟɦɵɯ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɨɦ ɫɢɝɧɚɥɨɜ ɪɚɜɧɨ ɫɭɦɦɟ ɫɢɝɧɚɥɨɜ, ɜɵɱɢɫɥɹɟɦɵɯ ɜɫɟɦɢ ɱɚɫɬɧɵɦɢ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚɦɢ. ȼ ɤɨɧɫɬɚɧɬɧɨɦ ɜɵɪɚɠɟɧɢɢ ɜɨɡɦɨɠɧɨ ɢɫɩɨɥɶɡɨɜɚɧɢɟ ɮɭɧɤɰɢɢ NumberOf, ɚɪɝɭɦɟɧɬɨɦ ɤɨɬɨɪɨɣ ɹɜɥɹɟɬɫɹ ɢɦɹ ɱɚɫɬɧɨɝɨ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ (ɢɥɢ ɟɝɨ ɩɫɟɜɞɨɧɢɦ) ɢ ɤɥɸɱɟɜɨɟ ɫɥɨɜɨ Signals, ɜ ɤɚɱɟɫɬɜɟ ɜɬɨɪɨɝɨ ɚɪɝɭɦɟɧɬɚ. ȼ ɧɟɨɛɹɡɚɬɟɥɶɧɨɦ ɪɚɡɞɟɥɟ "ɨɩɢɫɚɧɢɟ ɞɚɧɧɵɯ" ɭɤɚɡɵɜɚɟɬɫɹ ɱɢɫɥɨ ɜɯɨɞɧɵɯ ɞɚɧɧɵɯ, ɩɪɟɞɨɛɪɚɛɚɬɵɜɚɟɦɵɯ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɨɦ. ȿɫɥɢ ɷɬɨɬ ɪɚɡɞɟɥ ɨɩɭɳɟɧ, ɬɨ ɩɨɥɚɝɚɟɬɫɹ, ɱɬɨ ɱɢɫɥɨ ɩɪɟɞɨɛɪɚɛɚɬɵɜɚɟɦɵɯ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɨɦ ɞɚɧɧɵɯ ɪɚɜɧɨ ɫɭɦɦɟ ɞɚɧɧɵɯ, ɩɪɟɞɨɛɪɚɛɚɬɵɜɚɟɦɵɯ ɜɫɟɦɢ ɱɚɫɬɧɵɦɢ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚɦɢ. ȼ ɤɨɧɫɬɚɧɬɧɨɦ ɜɵɪɚɠɟɧɢɢ ɜɨɡɦɨɠɧɨ ɢɫɩɨɥɶɡɨɜɚɧɢɟ ɮɭɧɤɰɢɢ NumberOf, ɚɪɝɭɦɟɧɬɨɦ ɤɨɬɨɪɨɣ ɹɜɥɹɟɬɫɹ ɢɦɹ ɱɚɫɬɧɨɝɨ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ (ɢɥɢ ɟɝɨ ɩɫɟɜɞɨɧɢɦ) ɢ ɤɥɸɱɟɜɨɟ ɫɥɨɜɨ Data, ɜ ɤɚɱɟɫɬɜɟ ɜɬɨɪɨɝɨ ɚɪɝɭɦɟɧɬɚ. ȼ ɧɟɨɛɹɡɚɬɟɥɶɧɨɦ ɪɚɡɞɟɥɟ ɨɩɢɫɚɧɢɹ ɪɚɫɩɪɟɞɟɥɟɧɢɹ ɫɢɝɧɚɥɨɜ (ɞɚɧɧɵɯ) ɭɤɚɡɵɜɚɟɬɫɹ ɞɥɹ ɤɚɠɞɨɝɨ ɱɚɫɬɧɨɝɨ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ ɤɚɤɢɟ ɫɢɝɧɚɥɵ (ɜɯɨɞɧɵɟ ɞɚɧɧɵɟ) ɢɡ ɨɛɳɟɝɨ ɜɟɤɬɨɪɚ ɫɢɝɧɚɥɨɜ (ɞɚɧɧɵɯ) ɩɟɪɟɞɚɸɬɫɹ ɟɦɭ ɞɥɹ ɨɛɪɚɛɨɬɤɢ. ɇɚɢɛɨɥɟɟ ɱɚɫɬɨ ɜɫɬɪɟɱɚɸɳɢɟɫɹ ɢɧɬɟɪɩɪɟɬɚɬɨɪɵ ɨɛɴɹɜɥɟɧɵ ɫɬɚɧɞɚɪɬɧɵɦɢ. Ⱦɥɹ ɫɬɚɧɞɚɪɬɧɵɯ ɢɧɬɟɪɩɪɟɬɚɬɨɪɨɜ ɨɩɢɫɚɧɢɟ ɱɚɫɬɧɵɯ ɢɧɬɟɪɩɪɟɬɚɬɨɪɨɜ ɨɬɫɭɬɫɬɜɭɟɬ. Ʉɪɨɦɟ ɬɨɝɨ, ɜ ɥɸɛɨɦ ɦɟɫɬɟ ɨɩɢɫɚɧɢɹ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ ɦɨɝɭɬ ɜɫɬɪɟɱɚɬɶɫɹ ɤɨɦɦɟɧɬɚɪɢɢ, ɡɚɤɥɸɱɟɧɧɵɟ ɜ ɮɢɝɭɪɧɵɟ ɫɤɨɛɤɢ.
4.2.3.3 ɉɪɢɦɟɪ ɨɩɢɫɚɧɢɹ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ ȼ ɷɬɨɦ ɪɚɡɞɟɥɟ ɩɪɢɜɟɞɟɧɵ ɞɜɚ ɩɪɢɦɟɪɚ ɨɩɢɫɚɧɢɹ ɨɞɧɨɝɨ ɢ ɬɨɝɨ ɠɟ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ ɞɥɹ ɦɟɬɟɨɪɨɥɨɝɢɱɟɫɤɨɣ ɡɚɞɚɱɢ. ɂɫɩɨɥɶɡɭɟɬɫɹ ɫɥɟɞɭɸɳɢɣ ɫɨɫɬɚɜ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ: ɩɟɪɜɵɣ ɷɥɟɦɟɧɬ ɜɟɤɬɨɪɚ ɜɯɨɞɧɵɯ ɞɚɧɧɵɯ (ɬɟɦɩɟɪɚɬɭɪɚ ɜɨɡɞɭɯɚ) ɨɛɪɚɛɚɬɵɜɚɟɬɫɹ ɩɪɨɫɬɟɣɲɢɦ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɨɦ (EmptyPrep); ɜɬɨɪɨɣ (ɨɛɥɚɱɧɨɫɬɶ) – ɛɢɧɚɪɧɵɦ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɨɦ (BinaryPrep); ɬɪɟɬɢɣ (ɧɚɩɪɚɜɥɟɧɢɟ ɜɟɬɪɚ) – ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɨɦ ɧɟɭɩɨɪɹɞɨɱɟɧɧɵɯ ɤɚɱɟɫɬɜɟɧɧɵɯ ɩɪɢɡɧɚɤɨɜ (UnOrdered); ɱɟɬɜɟɪɬɵɣ (ɨɫɚɞɤɢ) – ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɨɦ ɧɟɭɩɨɪɹɞɨɱɟɧɧɵɯ ɤɚɱɟɫɬɜɟɧɧɵɯ ɩɪɢɡɧɚɤɨɜ (Ordered). ȼ ɩɟɪɜɨɦ ɩɪɢɦɟɪɟ ɩɪɢɜɟɞɟɧɨ ɨɩɢɫɚɧɢɟ ɞɭɛɥɢɤɚɬɨɜ ɜɫɟɯ ɫɬɚɧɞɚɪɬɧɵɯ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɨɜ. ȼɨ ɜɬɨɪɨɦ – ɢɫɩɨɥɶɡɨɜɚɧɵ ɫɬɚɧɞɚɪɬɧɵɟ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɢ. ɉɪɢɦɟɪ 1. Preparator Meteorology Function Sigmoid( X Real ) : Real; Begin Sigmoid = X / (1 + Abs(X)) End; Prep BinaryPrep1 () {ɉɪɟɞɨɛɪɚɛɨɬɤɚ ɛɢɧɚɪɧɨɝɨ ɩɪɢɡɧɚɤɚ} Static Real MinSignals Name "ɇɢɠɧɹɹ ɝɪɚɧɢɰɚ ɢɧɬɟɪɜɚɥɚ ɩɪɢɟɦɥɟɦɵɯ ɫɢɝɧɚɥɨɜ"; Real MaxSignals Name "ȼɟɪɯɧɹɹ ɝɪɚɧɢɰɚ ɢɧɬɟɪɜɚɥɚ ɩɪɢɟɦɥɟɦɵɯ ɫɢɝɧɚɥɨɜ"; Real Unknown Name "Ɂɧɚɱɟɧɢɟ ɫɢɝɧɚɥɚ, ɟɫɥɢ ɡɧɚɱɟɧɢɟ ɜɯɨɞɧɨɝɨ ɩɪɢɡɧɚɤɚ ɧɟ ɨɩɪɟɞɟɥɟɧɨ"; Logic Type Name "Ɍɢɩ ɩɪɟɞɨɛɪɚɛɨɬɤɢ ɛɢɧɚɪɧɨɝɨ ɩɪɢɡɧɚɤɚ"; Begin If TLong(Data[1]) = UnknownLong Then Signals[1] = Unknown Else Begin
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If Type Then Begin If TLong(Data[1]) = 1 Then Signals[1] = 0 Else Begin If MaxSignals =0 Then Signals[1] = MinSignals Else Signals[1] = MaxSignals End Else Begin If TLong(Data[1]) = 1 Then Signals[1] = MinSignals Else Signals[1] = MaxSignals End End End Prep UnOrdered1 ( Num : Long ) {ɉɪɟɞɨɛɪɚɛɨɬɤɚ ɭɩɨɪɹɞɨɱɟɧɧɨɝɨ ɤɚɱɟɫɬɜɟɧɧɨɝɨ ɩɪɢɡɧɚɤɚ} Static Real MinSignals Name "ɇɢɠɧɹɹ ɝɪɚɧɢɰɚ ɢɧɬɟɪɜɚɥɚ ɩɪɢɟɦɥɟɦɵɯ ɫɢɝɧɚɥɨɜ"; Real MaxSignals Name "ȼɟɪɯɧɹɹ ɝɪɚɧɢɰɚ ɢɧɬɟɪɜɚɥɚ ɩɪɢɟɦɥɟɦɵɯ ɫɢɝɧɚɥɨɜ"; Real Unknown Name "Ɂɧɚɱɟɧɢɟ ɫɢɝɧɚɥɚ, ɟɫɥɢ ɡɧɚɱɟɧɢɟ ɜɯɨɞɧɨɝɨ ɩɪɢɡɧɚɤɚ ɧɟ ɨɩɪɟɞɟɥɟɧɨ"; Var Integer I; Begin If TLong(Data[1]) = UnknownLong Then Begin For I = 1 To Num Do Signals[I] = Unknown End Else Begin For I = 1 To Num Do Signals[I] = MinSignals Signals[TLong(Data[1])] = MaxSignals End End Prep Ordered1 ( Num : Long ) {ɉɪɟɞɨɛɪɚɛɨɬɤɚ ɭɩɨɪɹɞɨɱɟɧɧɨɝɨ ɤɚɱɟɫɬɜɟɧɧɨɝɨ ɩɪɢɡɧɚɤɚ} Static Real MinSignals Name "ɇɢɠɧɹɹ ɝɪɚɧɢɰɚ ɢɧɬɟɪɜɚɥɚ ɩɪɢɟɦɥɟɦɵɯ ɫɢɝɧɚɥɨɜ"; Real MaxSignals Name "ȼɟɪɯɧɹɹ ɝɪɚɧɢɰɚ ɢɧɬɟɪɜɚɥɚ ɩɪɢɟɦɥɟɦɵɯ ɫɢɝɧɚɥɨɜ"; Real Unknown Name "Ɂɧɚɱɟɧɢɟ ɫɢɝɧɚɥɚ, ɟɫɥɢ ɡɧɚɱɟɧɢɟ ɜɯɨɞɧɨɝɨ ɩɪɢɡɧɚɤɚ ɧɟ ɨɩɪɟɞɟɥɟɧɨ"; Var Integer I; Begin If TLong(Data[1]) = UnknownLong Then Begin For I = 1 To Num Do Signals[I] = Unknown End Else Begin For I = 1 To TLong(Data[1]) Do Signals[I] = MaxSignals For I = TLong(Data[1])+1 To Num Do Signals[I] = MinSignals End End Prep EmptyPrep1 () {ɉɪɟɞɨɛɪɚɛɨɬɱɢɤ, ɨɫɭɳɟɫɬɜɥɹɸɳɢɣ ɦɚɫɲɬɚɛɢɪɨɜɚɧɢɟ ɢ ɫɞɜɢɝ ɫɢɝɧɚɥɚ} Static Real MinSignals Name "ɇɢɠɧɹɹ ɝɪɚɧɢɰɚ ɢɧɬɟɪɜɚɥɚ ɩɪɢɟɦɥɟɦɵɯ ɫɢɝɧɚɥɨɜ"; Real MaxSignals Name "ȼɟɪɯɧɹɹ ɝɪɚɧɢɰɚ ɢɧɬɟɪɜɚɥɚ ɩɪɢɟɦɥɟɦɵɯ ɫɢɝɧɚɥɨɜ"; Real Unknown Name "Ɂɧɚɱɟɧɢɟ ɫɢɝɧɚɥɚ, ɟɫɥɢ ɡɧɚɱɟɧɢɟ ɜɯɨɞɧɨɝɨ ɩɪɢɡɧɚɤɚ ɧɟ ɨɩɪɟɞɟɥɟɧɨ"; Real MinData Name "Ɂɧɚɱɟɧɢɹ ɧɢɠɧɟɣ ɝɪɚɧɢɰɵ ɢɧɬɟɪɜɚɥɚ ɢɡɦɟɧɟɧɢɹ ɜɯɨɞɧɵɯ ɞɚɧɧɵɯ"; Real MaxData Name "Ɂɧɚɱɟɧɢɹ ɜɟɪɯɧɟɣ ɝɪɚɧɢɰɵ ɢɧɬɟɪɜɚɥɚ ɢɡɦɟɧɟɧɢɹ ɜɯɨɞɧɵɯ ɞɚɧɧɵɯ"; Begin If Data[1] = UnknownReal Then Signals[1] = Unknown Else Signals[1] = (Data[1] – MinData) * (MaxSignals – MinSignals) / (MaxData – MinData) + MinSignals End Prep ModPrep1 ( Num : Long )
CHAP4.DOC
{Ɇɨɞɭɥɹɪɧɵɣ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ}
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Static Real MinSignals Name "ɇɢɠɧɹɹ ɝɪɚɧɢɰɚ ɢɧɬɟɪɜɚɥɚ ɩɪɢɟɦɥɟɦɵɯ ɫɢɝɧɚɥɨɜ"; Real MaxSignals Name "ȼɟɪɯɧɹɹ ɝɪɚɧɢɰɚ ɢɧɬɟɪɜɚɥɚ ɩɪɢɟɦɥɟɦɵɯ ɫɢɝɧɚɥɨɜ"; Real Unknown Name "Ɂɧɚɱɟɧɢɟ ɫɢɝɧɚɥɚ, ɟɫɥɢ ɡɧɚɱɟɧɢɟ ɜɯɨɞɧɨɝɨ ɩɪɢɡɧɚɤɚ ɧɟ ɨɩɪɟɞɟɥɟɧɨ"; RealArray[Num] Y Name "Ɇɚɫɫɢɜ ɜɟɥɢɱɢɧ, ɢɫɩɨɥɶɡɭɟɦɵɯ ɞɥɹ ɩɪɟɞɨɛɪɚɛɨɬɤɢ" Var Integer I; Begin If Data[1] = UnknownReal Then Begin For I = 1 To Num Do Signals[I] = Unknown End Else Begin For I = 1 To Num Do Signals[I] = (Data[1] RMod Y[I] + Y[I]) * (MaxSignals – MinSignals) / (2 * Y[I]) + MinSignals End Prep FuncPrep1(Num : Long; F : FuncType) {Ɏɭɧɤɰɢɨɧɚɥɶɧɵɣ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ} Static Real MinSignals Name "ɇɢɠɧɹɹ ɝɪɚɧɢɰɚ ɢɧɬɟɪɜɚɥɚ ɩɪɢɟɦɥɟɦɵɯ ɫɢɝɧɚɥɨɜ"; Real MaxSignals Name "ȼɟɪɯɧɹɹ ɝɪɚɧɢɰɚ ɢɧɬɟɪɜɚɥɚ ɩɪɢɟɦɥɟɦɵɯ ɫɢɝɧɚɥɨɜ"; Real Unknown Name "Ɂɧɚɱɟɧɢɟ ɫɢɝɧɚɥɚ, ɟɫɥɢ ɡɧɚɱɟɧɢɟ ɜɯɨɞɧɨɝɨ ɩɪɢɡɧɚɤɚ ɧɟ ɨɩɪɟɞɟɥɟɧɨ"; Real MinData Name "Ɂɧɚɱɟɧɢɹ ɧɢɠɧɟɣ ɝɪɚɧɢɰɵ ɢɧɬɟɪɜɚɥɚ ɢɡɦɟɧɟɧɢɹ ɡɧɚɱɟɧɢɣ ɮɭɧɤɰɢɢ F"; Real MaxData Name "Ɂɧɚɱɟɧɢɹ ɜɟɪɯɧɟɣ ɝɪɚɧɢɰɵ ɢɧɬɟɪɜɚɥɚ ɢɡɦɟɧɟɧɢɹ ɡɧɚɱɟɧɢɣ ɮɭɧɤɰɢɢ F"; RealArray[Num] Y Name "Ɇɚɫɫɢɜ ɜɟɥɢɱɢɧ, ɢɫɩɨɥɶɡɭɟɦɵɯ ɞɥɹ ɩɪɟɞɨɛɪɚɛɨɬɤɢ" Var Integer I; Begin If Data[1] = UnknownReal Then Begin For I = 1 To Num Do Signals[I] = Unknown End Else Begin For I = 1 To Num Do Signals[1] = (F(Data[1] – Y[1] – MinData) * (MaxSignals – MinSignals) / (MaxData – MinData) + MinSignals End Prep PositPrep1( Num : Long ) {ɉɨɡɢɰɢɨɧɧɵɣ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ} Static Real MinSignals Name "ɇɢɠɧɹɹ ɝɪɚɧɢɰɚ ɢɧɬɟɪɜɚɥɚ ɩɪɢɟɦɥɟɦɵɯ ɫɢɝɧɚɥɨɜ" Real MaxSignals Name "ȼɟɪɯɧɹɹ ɝɪɚɧɢɰɚ ɢɧɬɟɪɜɚɥɚ ɩɪɢɟɦɥɟɦɵɯ ɫɢɝɧɚɥɨɜ" Real Unknown Name "Ɂɧɚɱɟɧɢɟ ɫɢɝɧɚɥɚ, ɟɫɥɢ ɡɧɚɱɟɧɢɟ ɜɯɨɞɧɨɝɨ ɩɪɢɡɧɚɤɚ ɧɟ ɨɩɪɟɞɟɥɟɧɨ" Real Y Name "Ɉɫɧɨɜɚɧɢɟ ɫɢɫɬɟɦɵ ɫɱɢɫɥɟɧɢɹ" Var Integer I; Real W, Q; Begin If Data[1] = UnknownReal Then Begin For I = 1 To Num Do Signals[I] = Unknown End Else Begin W = Data[1]; For I = 1 To Num Do Begin Q = W RMod Y; Signals[I] = Q * (MaxSignals – MinSignals) / Y + MinSignals; W = (W - Q) / Y End; End Contents Temp : EmptyPrep1, Cloud : BinaryPrep1, Wind : UnOrdered1(8), Rain : Ordered1(3);
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Temp SetParameters -1, 1, 1E-40, 273, 293; {Ⱦɥɹ ɜɫɟɯ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɨɜ ɩɪɢɟɦɥɟɦɵɟ ɡɧɚɱɟɧɢɹ ɜɯɨɞ-} Cloud SetParameters -1, 1, 0, True; {ɧɵɯ ɫɢɝɧɚɥɨɜ ɥɟɠɚɬ ɜ ɢɧɬɟɪɜɚɥɟ ɨɬ -1 ɞɨ 1. ȼ ɫɥɭɱɚɟ ɧɟɨɩɪɟɞɟ-} Wind SetParameters -1, 1, 0; {ɥɟɧɧɨɝɨ ɡɧɚɱɟɧɢɹ ɜɨ ɜɯɨɞɧɵɯ ɞɚɧɧɵɯ ɜɫɟ ɫɢɝɧɚɥɵ ɞɚɧɧɨɝɨ} Rain SetParameters -1, 1, 0 {ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ ɩɨɥɚɝɚɸɬɫɹ ɪɚɜɧɵɦɢ ɧɭɥɸ. ȼɯɨɞɧɵɟ ɞɚɧɧɵɟ} {ɩɟɪɜɨɝɨ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ ɦɟɧɹɸɬɫɹ ɨɬ 273 ɞɨ 293} Signals NumberOf(Signals,Temp) + NumberOf(Signals, Cloud) + NumberOf(Signals, Wind(8)) + NumberOf(Signals, Rain(3)) Data NumberOf(Data,Temp) + NumberOf(Data, Cloud) + NumberOf(Data, Wind(8)) + NumberOf(Data, Rain(3)) Connections Temp.Data <=> Data[1]; Cloud.Data <=> Data[2]; Wind.Data <=> Data[3]; Rain.Data <=> Data[4]; Temp.Signals <=> Signals[1]; Cloud.Signals <=> Signals[2]; Wind.Signals[1..8] <=> Signals[3..10]; Rain.Signals[1..3] <=> Signals[11..13] End Preparator
ɉɪɢɦɟɪ 2. Preparator Meteorology Contents Temp : EmptyPrep, Cloud : BinaryPrep, Wind : UnOrdered(8), Rain : Ordered(3);
Temp SetParameters -1, 1, 1E-40, 273, 293 End Preparator
4.3 ɋɬɚɧɞɚɪɬ ɜɬɨɪɨɝɨ ɭɪɨɜɧɹ ɤɨɦɩɨɧɟɧɬɚ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ Ɂɚɩɪɨɫɵ ɤ ɤɨɦɩɨɧɟɧɬɭ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ ɦɨɠɧɨ ɪɚɡɛɢɬɶ ɧɚ ɩɹɬɶ ɝɪɭɩɩ: ɉɪɟɞɨɛɪɚɛɨɬɤɚ. ɂɡɦɟɧɟɧɢɟ ɩɚɪɚɦɟɬɪɨɜ. Ɋɚɛɨɬɚ ɫɨ ɫɬɪɭɤɬɭɪɨɣ. ɂɧɢɰɢɚɰɢɹ ɪɟɞɚɤɬɨɪɚ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ. Ɉɛɪɚɛɨɬɤɚ ɨɲɢɛɨɤ. ɉɨɫɤɨɥɶɤɭ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪ ɦɨɠɟɬ ɪɚɛɨɬɚɬɶ ɨɞɧɨɜɪɟɦɟɧɧɨ ɫ ɧɟɫɤɨɥɶɤɢɦɢ ɫɟɬɹɦɢ, ɬɨ ɢ ɤɨɦɩɨɧɟɧɬ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ ɞɨɥɠɧɚ ɢɦɟɬɶ ɜɨɡɦɨɠɧɨɫɬɶ ɨɞɧɨɜɪɟɦɟɧɧɨɣ ɪɚɛɨɬɵ ɫ ɧɟɫɤɨɥɶɤɢɦɢ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚɦɢ. ɉɨɷɬɨɦɭ ɛɨɥɶɲɢɧɫɬɜɨ ɡɚɩɪɨɫɨɜ ɤ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɭ ɫɨɞɟɪɠɚɬ ɹɜɧɨɟ ɭɤɚɡɚɧɢɟ ɢɦɟɧɢ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ. ɇɢɠɟ ɩɪɢɜɟɞɟɧɨ ɨɩɢɫɚɧɢɟ ɜɫɟɯ ɡɚɩɪɨɫɨɜ ɤ ɤɨɦɩɨɧɟɧɬɭ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ. Ʉɚɠɞɵɣ ɡɚɩɪɨɫ ɹɜɥɹɟɬɫɹ ɥɨɝɢɱɟɫɤɨɣ ɮɭɧɤɰɢɟɣ, ɜɨɡɜɪɚɳɚɸɳɟɣ ɡɧɚɱɟɧɢɟ ɢɫɬɢɧɚ, ɟɫɥɢ ɡɚɩɪɨɫ ɜɵɩɨɥɧɟɧ ɭɫɩɟɲɧɨ, ɢ ɥɨɠɶ – ɩɪɢ ɨɲɢɛɨɱɧɨɦ ɡɚɜɟɪɲɟɧɢɢ ɢɫɩɨɥɧɟɧɢɹ ɡɚɩɪɨɫɚ. ȼ ɡɚɩɪɨɫɚɯ ɜɬɨɪɨɣ ɢ ɬɪɟɬɶɟɣ ɝɪɭɩɩɵ ɩɪɢ ɨɛɪɚɳɟɧɢɢ ɤ ɱɚɫɬɧɵɦ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚɦ ɢɫɩɨɥɶɡɭɟɬɫɹ ɫɥɟɞɭɸɳɢɣ ɫɢɧɬɚɤɫɢɫ: <ɉɨɥɧɨɟ ɢɦɹ ɱɚɫɬɧɨɝɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ> ::= <ɂɦɹ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ>.<ɉɫɟɜɞɨɧɢɦ ɱɚɫɬɧɨɝɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ> [[<ɇɨɦɟɪ ɷɤɡɟɦɩɥɹɪɚ>] ] 1. 2. 3. 4. 5.
Ɍɚɛɥɢɰɚ 13. Ɂɧɚɱɟɧɢɹ ɩɪɟɞɨɩɪɟɞɟɥɟɧɧɵɯ ɤɨɧɫɬɚɧɬ ɤɨɦɩɨɧɟɧɬɚ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ ɇɚɡɜɚɧɢɟȼɟɥɢɱɢ Ɂɧɚɱɟɧɢɟ ɧɚ BinaryPrep 0 ɋɬɚɧɞɚɪɬɧɵɣ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ ɛɢɧɚɪɧɵɯ ɩɪɢɡɧɚɤɨɜ UnOrdered 1 ɋɬɚɧɞɚɪɬɧɵɣ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ ɧɟɭɩɨɪɹɞɨɱɟɧɧɵɯ ɤɚɱɟɫɬɜɟɧɧɵɯ ɩɪɢɡɧɚɤɨɜ Ordered 2 ɋɬɚɧɞɚɪɬɧɵɣ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ ɭɩɨɪɹɞɨɱɟɧɧɵɯ ɤɚɱɟɫɬɜɟɧɧɵɯ ɩɪɢɡɧɚɤɨɜ. EmptyPrep 3 ɋɬɚɧɞɚɪɬɧɵɣ ɩɪɨɫɬɟɣɲɢɣ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ ModPrep 4 ɋɬɚɧɞɚɪɬɧɵɣ ɦɨɞɭɥɹɪɧɵɣ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ FuncPrep 5 ɋɬɚɧɞɚɪɬɧɵɣ ɮɭɧɤɰɢɨɧɚɥɶɧɵɣ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ PositPrep 6 ɋɬɚɧɞɚɪɬɧɵɣ ɩɨɡɢɰɢɨɧɧɵɣ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ UserType -1 ɉɪɟɞɨɛɪɚɛɨɬɱɢɤ, ɨɩɪɟɞɟɥɟɧɧɵɣ ɩɨɥɶɡɨɜɚɬɟɥɟɦ.
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ɉɪɢ ɜɵɡɨɜɟ ɪɹɞɚ ɡɚɩɪɨɫɨɜ ɢɫɩɨɥɶɡɭɸɬɫɹ ɩɪɟɞɨɩɪɟɞɟɥɟɧɧɵɟ ɤɨɧɫɬɚɧɬɵ. ɂɯ ɡɧɚɱɟɧɢɹ ɩɪɢɜɟɞɟɧɵ ɜ ɬɚɛɥ. 13.
4.3.1 Ɂɚɩɪɨɫ ɧɚ ɩɪɟɞɨɛɪɚɛɨɬɤɭ ȿɞɢɧɫɬɜɟɧɧɵɣ ɡɚɩɪɨɫ ɩɟɪɜɨɣ ɝɪɭɩɩɵ ɜɵɩɨɥɧɹɟɬ ɨɫɧɨɜɧɭɸ ɮɭɧɤɰɢɸ ɤɨɦɩɨɧɟɧɬɚ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ – ɩɪɟɞɨɛɪɚɛɚɬɵɜɚɟɬ ɜɯɨɞɧɵɟ ɞɚɧɧɵɟ, ɜɵɱɢɫɥɹɹ ɜɟɤɬɨɪ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ.
4.3.1.1 ɉɪɟɞɨɛɪɚɛɨɬɚɬɶ ɜɟɤɬɨɪ ɫɢɝɧɚɥɨɜ (Prepare) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function Prepare(CompName : PString; Data : PRealArray; Var Signals : PRealArray) : Logic; C: Logic Prepare(PString CompName, PRealArray Data; PRealArray* Signals) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɚ: CompName – ɭɤɚɡɚɬɟɥɶ ɧɚ ɫɬɪɨɤɭ ɫɢɦɜɨɥɨɜ, ɫɨɞɟɪɠɚɳɭɸ ɢɦɹ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ. Data – ɦɚɫɫɢɜ ɜɯɨɞɧɵɯ ɞɚɧɧɵɯ. Signals – ɜɵɱɢɫɥɹɟɦɵɣ ɦɚɫɫɢɜ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ. ɇɚɡɧɚɱɟɧɢɟ – ɩɪɟɞɨɛɪɚɛɚɬɵɜɚɟɬ ɦɚɫɫɢɜ ɜɯɨɞɧɵɯ ɞɚɧɧɵɯ Data, ɜɵɱɢɫɥɹɹ ɦɚɫɫɢɜ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ Signals ɢɫɩɨɥɶɡɭɹ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ, ɭɤɚɡɚɧɧɵɣ ɜ ɩɚɪɚɦɟɬɪɟ CompName. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. 1. ȿɫɥɢ Error <> 0, ɬɨ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 2. ȿɫɥɢ ɜ ɤɚɱɟɫɬɜɟ ɚɪɝɭɦɟɧɬɚ CompName ɞɚɧ ɩɭɫɬɨɣ ɭɤɚɡɚɬɟɥɶ, ɢɥɢ ɭɤɚɡɚɬɟɥɶ ɧɚ ɩɭɫɬɭɸ ɫɬɪɨɤɭ, ɬɨ ɢɫɩɨɥɧɹɸɳɢɦ ɡɚɩɪɨɫ ɨɛɴɟɤɬɨɦ ɹɜɥɹɟɬɫɹ ɬɟɤɭɳɢɣ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ – ɩɟɪɜɵɣ ɜ ɫɩɢɫɤɟ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɨɜ ɤɨɦɩɨɧɟɧɬɚ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ. 3. ȿɫɥɢ ɫɩɢɫɨɤ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɨɜ ɤɨɦɩɨɧɟɧɬɚ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ ɩɭɫɬ ɢɥɢ ɢɦɹ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ, ɩɟɪɟɞɚɧɧɨɟ ɜ ɚɪɝɭɦɟɧɬɟ CompName ɜ ɷɬɨɦ ɫɩɢɫɤɟ ɧɟ ɧɚɣɞɟɧɨ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 201 – ɧɟɜɟɪɧɨɟ ɢɦɹ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 4. ɉɪɨɢɡɜɨɞɢɬɫɹ ɩɪɟɞɨɛɪɚɛɨɬɤɚ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɨɦ, ɢɦɹ ɤɨɬɨɪɨɝɨ ɛɵɥɨ ɭɤɚɡɚɧɨ ɜ ɚɪɝɭɦɟɧɬɟ CompName. 5. ȿɫɥɢ ɜɨ ɜɪɟɦɹ ɜɵɩɨɥɧɟɧɢɹ ɡɚɩɪɨɫɚ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ, ɬɨ ɝɟɧɟɪɢɪɭɟɬɫɹ ɜɧɭɬɪɟɧɧɹɹ ɨɲɢɛɤɚ 204 ɨɲɢɛɤɚ ɩɪɟɞɨɛɪɚɛɨɬɤɢ. ɍɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ. ȼɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. ȼ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɭɫɩɟɲɧɨ ɡɚɜɟɪɲɚɟɬɫɹ.
4.3.2 Ɉɫɬɚɥɶɧɵɟ ɡɚɩɪɨɫɵ. ɇɢɠɟ ɩɪɢɜɟɞɟɧ ɫɩɢɫɨɤ ɡɚɩɪɨɫɨɜ ɤ ɤɨɦɩɨɧɟɧɬɭ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ, ɢɫɩɨɥɧɟɧɢɟ ɤɨɬɨɪɵɯ ɨɩɢɫɚɧɨ ɜ ɝɥɚɜɟ «Ɉɛɳɢɣ ɫɬɚɧɞɚɪɬ»: prSetCurrent – ɋɞɟɥɚɬɶ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ ɬɟɤɭɳɢɦ prAdd – Ⱦɨɛɚɜɥɟɧɢɟ ɧɨɜɨɝɨ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ prDelete – ɍɞɚɥɟɧɢɟ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ prWrite – Ɂɚɩɢɫɶ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ prGetStructNames – ȼɟɪɧɭɬɶ ɢɦɟɧɚ ɫɬɪɭɤɬɭɪɧɵɯ ɟɞɢɧɢɰ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ prGetType – ȼɟɪɧɭɬɶ ɬɢɩ ɫɬɪɭɤɬɭɪɧɨɣ ɟɞɢɧɢɰɵ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ prGetData – ɉɨɥɭɱɢɬɶ ɩɚɪɚɦɟɬɪɵ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ prGetName – ɉɨɥɭɱɢɬɶ ɢɦɟɧɚ ɩɚɪɚɦɟɬɪɨɜ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ prSetData – ɍɫɬɚɧɨɜɢɬɶ ɩɚɪɚɦɟɬɪɵ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚ prEdit – Ɋɟɞɚɤɬɢɪɨɜɚɬɶ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ OnError – ɍɫɬɚɧɨɜɢɬɶ ɨɛɪɚɛɨɬɱɢɤ ɨɲɢɛɨɤ GetError – Ⱦɚɬɶ ɧɨɦɟɪ ɨɲɢɛɤɢ FreeMemory – Ɉɫɜɨɛɨɞɢɬɶ ɩɚɦɹɬɶ ȼ ɡɚɩɪɨɫɟ prGetType ɜ ɩɟɪɟɦɟɧɧɨɣ TypeId ɜɨɡɜɪɚɳɚɟɬɫɹ ɡɧɚɱɟɧɢɟ ɨɞɧɨɣ ɢɡ ɩɪɟɞɨɩɪɟɞɟɥɟɧɧɵɯ ɤɨɧɫɬɚɧɬ, ɩɟɪɟɱɢɫɥɟɧɧɵɯ ɜ ɬɚɛɥ. 13.
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4.3.3 Ɉɲɢɛɤɢ ɤɨɦɩɨɧɟɧɬɚ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ ȼ ɬɚɛɥ. 14 ɩɪɢɜɟɞɟɧ ɩɨɥɧɵɣ ɫɩɢɫɨɤ ɨɲɢɛɨɤ, ɤɨɬɨɪɵɟ ɦɨɝɭɬ ɜɨɡɧɢɤɚɬɶ ɩɪɢ ɜɵɩɨɥɧɟɧɢɢ ɡɚɩɪɨɫɨɜ ɤɨɦɩɨɧɟɧɬɨɦ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ, ɢ ɞɟɣɫɬɜɢɹ ɫɬɚɧɞɚɪɬɧɨɝɨ ɨɛɪɚɛɨɬɱɢɤɚ ɨɲɢɛɨɤ. Ɍɚɛɥɢɰɚ 14. Ɉɲɢɛɤɢ ɤɨɦɩɨɧɟɧɬɚ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ ɢ ɞɟɣɫɬɜɢɹ ɫɬɚɧɞɚɪɬɧɨɝɨ ɨɛɪɚɛɨɬɱɢɤɚ ɨɲɢɛɨɤ. ʋɇɚɡɜɚɧɢɟ ɨɲɢɛɤɢɋɬɚɧɞɚɪɬɧɚɹ ɨɛɪɚɛɨɬɤɚ 201 ɇɟɜɟɪɧɨɟ ɢɦɹ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚɁɚɧɟɫɟɧɢɟ ɧɨɦɟɪɚ ɜ Error 202 Ɉɲɢɛɤɚ ɫɱɢɬɵɜɚɧɢɹ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚɁɚɧɟɫɟɧɢɟ ɧɨɦɟɪɚ ɜ Error 203 Ɉɲɢɛɤɚ ɫɨɯɪɚɧɟɧɢɹ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤɚɁɚɧɟɫɟɧɢɟ ɧɨɦɟɪɚ ɜ Error 204 Ɉɲɢɛɤɚ ɩɪɟɞɨɛɪɚɛɨɬɤɢɁɚɧɟɫɟɧɢɟ ɧɨɦɟɪɚ ɜ Error
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5. Ɉɩɢɫɚɧɢɟ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ɗɬɚ ɝɥɚɜɚ ɫɨɫɬɨɢɬ ɢɡ ɱɟɬɵɪɟɯ ɱɚɫɬɟɣ. ȼ ɩɟɪɜɨɣ ɱɚɫɬɢ ɨɩɢɫɚɧɚ ɫɢɫɬɟɦɚ ɩɨɫɬɪɨɟɧɢɹ ɫɟɬɟɣ ɢɡ ɷɥɟɦɟɧɬɨɜ. Ɉɩɢɫɚɧɵ ɩɪɹɦɨɟ ɢ ɨɛɪɚɬɧɨɟ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɟ ɫɟɬɟɣ ɢ ɫɨɫɬɚɜɥɹɸɳɢɯ ɢɯ ɷɥɟɦɟɧɬɨɜ. ȼɨ ɜɬɨɪɨɣ ɱɚɫɬɢ ɩɪɢɜɟɞɟɧɵ ɩɪɢɦɟɪɵ ɪɚɡɥɢɱɧɵɯ ɩɚɪɚɞɢɝɦ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ, ɨɩɢɫɚɧɧɵɟ ɜ ɫɨɨɬɜɟɬɫɬɜɢɢ ɫ ɩɪɟɞɥɨɠɟɧɧɨɣ ɜ ɩɟɪɜɨɣ ɱɚɫɬɢ ɝɥɚɜɵ ɦɟɬɨɞɢɤɨɣ. ȼ ɬɪɟɬɶɟɣ ɢ ɱɟɬɜɟɪɬɨɣ ɱɚɫɬɹɯ ɝɥɚɜɵ ɨɩɢɫɚɧɵ ɫɬɚɧɞɚɪɬɵ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ. Ʉɚɤ ɭɠɟ ɝɨɜɨɪɢɥɨɫɶ ɜ ɩɟɪɜɨɣ ɝɥɚɜɟ, ɧɚ ɞɚɧɧɵɣ ɦɨɦɟɧɬ ɜ ɧɟɣɪɨɫɟɬɟɜɨɦ ɫɨɨɛɳɟɫɬɜɟ ɩɪɢɧɹɬɨ ɨɩɢɫɵɜɚɬɶ ɚɪɯɢɬɟɤɬɭɪɭ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ɜ ɧɟɪɚɡɪɵɜɧɨɦ ɟɞɢɧɫɬɜɟ ɫ ɦɟɬɨɞɚɦɢ ɢɯ ɨɛɭɱɟɧɢɹ. ɗɬɚ ɫɜɹɡɶ ɧɟ ɹɜɥɹɟɬɫɹ ɟɫɬɟɫɬɜɟɧɧɨɣ. Ɍɚɤ, ɜ ɩɟɪɜɨɣ ɱɚɫɬɢ ɷɬɨɣ ɝɥɚɜɵ ɛɭɞɟɬ ɪɚɫɫɦɚɬɪɢɜɚɬɶɫɹ ɬɨɥɶɤɨ ɚɪɯɢɬɟɤɬɭɪɚ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ. ȼɨ ɜɬɨɪɨɣ ɱɚɫɬɢ ɛɭɞɟɬ ɩɪɨɞɟɦɨɧɫɬɪɢɪɨɜɚɧɚ ɧɟɡɚɜɢɫɢɦɨɫɬɶ ɪɹɞɚ ɦɟɬɨɞɨɜ ɨɛɭɱɟɧɢɹ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ɨɬ ɢɯ ɚɪɯɢɬɟɤɬɭɪɵ. Ɉɞɧɚɤɨ, ɞɥɹ ɭɞɨɛɫɬɜɚ ɱɢɬɚɬɟɥɹ, ɜɨ ɜɬɨɪɨɣ ɱɚɫɬɢ ɝɥɚɜɵ ɚɪɯɢɬɟɤɬɭɪɵ ɜɫɟɯ ɩɚɪɚɞɢɝɦ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ɛɭɞɭɬ ɨɩɢɫɚɧɵ ɜɦɟɫɬɟ ɫ ɦɟɬɨɞɚɦɢ ɨɛɭɱɟɧɢɹ. ɇɟɣɪɨɧɧɵɟ ɫɟɬɢ ɦɨɠɧɨ ɤɥɚɫɫɢɮɢɰɢɪɨɜɚɬɶ ɩɨ ɪɚɡɧɵɦ ɩɪɢɡɧɚɤɚɦ. Ⱦɥɹ ɨɩɢɫɚɧɢɹ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ɜ ɞɚɧɧɨɣ ɝɥɚɜɟ ɫɭɳɟɫɬɜɟɧɧɨɣ ɹɜɥɹɟɬɫɹ ɤɥɚɫɫɢɮɢɤɚɰɢɹ ɩɨ ɬɢɩɭ ɜɪɟɦɟɧɢ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ ɫɟɬɟɣ. ɉɨ ɷɬɨɦɭ ɩɪɢɡɧɚɤɭ ɫɟɬɢ ɦɨɠɧɨ ɪɚɡɛɢɬɶ ɧɚ ɬɪɢ ɤɥɚɫɫɚ. 1. ɋɟɬɢ ɫ ɧɟɩɪɟɪɵɜɧɵɦ ɜɪɟɦɟɧɟɦ (ɚɧɚɥɨɝɨɜɵɟ ɫɟɬɢ). 2. ɋɟɬɢ ɫ ɞɢɫɤɪɟɬɧɵɦ ɚɫɢɧɯɪɨɧɧɵɦ ɜɪɟɦɟɧɟɦ. 3. ɋɟɬɢ ɫ ɞɢɫɤɪɟɬɧɵɦ ɜɪɟɦɟɧɟɦ, ɮɭɧɤɰɢɨɧɢɪɭɸɳɢɟ ɫɢɧɯɪɨɧɧɨ. ȼ ɞɚɧɧɨɣ ɪɚɛɨɬɟ ɪɚɫɫɦɚɬɪɢɜɚɸɬɫɹ ɬɨɥɶɤɨ ɫɟɬɢ ɬɪɟɬɶɟɝɨ ɜɢɞɚ, ɬɨ ɟɫɬɶ ɫɟɬɢ, ɜ ɤɨɬɨɪɵɯ ɜɫɟ ɷɥɟɦɟɧɬɵ ɤɚɠɞɨɝɨ ɫɥɨɹ ɫɪɚɛɚɬɵɜɚɸɬ ɨɞɧɨɜɪɟɦɟɧɧɨ ɢ ɡɚɬɟɦ ɩɟɪɟɞɚɸɬ ɫɜɨɢ ɫɢɝɧɚɥɵ ɧɟɣɪɨɧɚɦ ɫɥɟɞɭɸɳɟɝɨ ɫɥɨɹ.
5.1 Ʉɨɧɫɬɪɭɢɪɨɜɚɧɢɟ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ȼɩɟɪɜɵɟ ɩɨɫɥɟɞɨɜɚɬɟɥɶɧɨɟ ɨɩɢɫɚɧɢɟ ɤɨɧɫɬɪɭɢɪɨɜɚɧɢɹ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ɢɡ ɷɥɟɦɟɧɬɨɜ ɛɵɥɨ ɩɪɟɞɥɨɠɟɧɨ ɜ ɤɧɢɝɟ Ⱥ.ɇ.Ƚɨɪɛɚɧɹ [64]. Ɉɞɧɚɤɨ ɡɚ ɩɪɨɲɟɞɲɟɟ ɜɪɟɦɹ ɩɪɟɞɥɨɠɟɧɧɵɣ Ⱥ.ɇ. Ƚɨɪɛɚɧɟɦ ɫɩɨɫɨɛ ɤɨɧɫɬɪɭɢɪɨɜɚɧɢɹ ɩɪɟɬɟɪɩɟɥ ɪɹɞ ɢɡɦɟɧɟɧɢɣ. ɉɪɢ ɨɩɢɫɚɧɢɢ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ɩɪɢɧɹɬɨ ɨɩɟɪɢɪɨɜɚɬɶ ɬɚɤɢɦɢ ɬɟɪɦɢɧɚɦɢ, ɤɚɤ ɧɟɣɪɨɧ ɢ ɫɥɨɣ. Ɉɞɧɚɤɨ, ɩɪɢ ɫɪɚɜɧɟɧɢɢ ɪɚɛɨɬ ɪɚɡɧɵɯ ɚɜɬɨɪɨɜ ɜɵɹɫɧɹɟɬɫɹ, ɱɬɨ ɟɫɥɢ ɫɥɨɟɦ ɜɫɟ ɚɜɬɨɪɵ ɧɚɡɵɜɚɸɬ ɩɪɢɛɥɢɡɢɬɟɥɶɧɨ ɨɞɢɧɚɤɨɜɵɟ ɫɬɪɭɤɬɭɪɵ, ɬɨ ɧɟɣɪɨɧɵ ɪɚɡɧɵɯ ɚɜɬɨɪɨɜ ɫɨɜɟɪɲɟɧɧɨ ɪɚɡɥɢɱɧɵ. Ɍɚɤɢɦ ɨɛɪɚɡɨɦ, ɨɩɢɫɚɧɢɟ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ, ɚ ɡɧɚɱɢɬ ɢ ɫɬɚɧɞɚɪɬɢɡɚɰɢɹ, ɧɚ ɭɪɨɜɧɟ ɧɟɣɪɨɧɨɜ ɧɟɜɨɡɦɨɠɧɚ. Ɉɞɧɚɤɨ, ɜɨɡɦɨɠɧɚ ɫɬɚɧɞɚɪɬɢɡɚɰɢɹ ɧɚ ɭɪɨɜɧɟ ɫɨɫɬɚɜɥɹɸɳɢɯ ɧɟɣɪɨɧɵ ɷɥɟɦɟɧɬɨɜ ɢ ɩɪɨɰɟɞɭɪ ɤɨɧɫɬɪɭɢɪɨɜɚɧɢɹ ɫɥɨɠɧɵɯ ɫɟɬɟɣ ɢɡ ɩɪɨɫɬɵɯ.
5.1.1 ɗɥɟɦɟɧɬɵ ɧɟɣɪɨɧɧɨɣ ɫɟɬɢ ɇɚ ɪɢɫ. 1 ɩɪɢɜɟɞɟɧɵ ɜɫɟ ɷɥɟɦɟɧɬɵ, ɧɟɨɛɯɨɞɢɦɵɟ ɞɥɹ ɩɨɫɬɪɨɟɧɢɹ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ. ȿɫɬɟɫɬɜɟɧɧɨ, ɱɬɨ ɜɨɡɦɨɠɧɨ ɪɚɫɲɢɪɟɧɢɟ ɫɩɢɫɤɚ ɧɟɥɢɧɟɣɧɵɯ ɩɪɟɨɛɪɚɡɨɜɚɬɟɥɟɣ. Ɉɞɧɚɤɨ, ɷɬɨ ɟɞɢɧɫɬɜɟɧɧɵɣ ɜɢɞ ɷɥɟɦɟɧɬɨɜ, ɤɨɬɨɪɵɣ ɦɨɠɟɬ ɞɨɩɨɥɧɹɬɶɫɹ. ȼɟɪɬɢɤɚɥɶɧɵɦɢ ɫɬɪɟɥɤɚɦɢ ɨɛɨɡɧɚɱɟɧɵ ɜɯɨɞɵ ɩɚɪɚɦɟɬɪɨɜ (ɞɥɹ ɫɢɧɚɩɫɚ – ɫɢɧɚɩɬɢɱɟɫɤɢɯ ɜɟɫɨɜ ɢɥɢ ɜɟɫɨɜ ɫɜɹɡɟɣ), ɚ ɝɨɪɢɡɨɧɬɚɥɶɧɵɦɢ – ɜɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɷɥɟɦɟɧɬɨɜ. ɋ ɬɨɱɤɢ ɡɪɟɧɢɹ ɮɭɧɤɰɢɨɧɢɪɨp s ɜɚɧɢɹ ɷɥɟɦɟɧɬɨɜ ɫɟɬɢ ɫɢɝɧɚɥɵ ɢ ɜɯɨɞɧɵɟ ɇɟɥɢɧɟɣɧɵɣ ɇɟɥɢɧɟɣɧɵɣ ɩɚɪɚɦɟɬɪɵ ɷɥɟɦɟɧɬɨɜ ɪɚɜɧɨɡɧɚɱɧɵ. Ɋɚɡɉɚɞɟ ɫɢɝɦɨɢɞɧɵɣ ɥɢɱɢɟ ɦɟɠɞɭ ɷɬɢɦɢ ɞɜɭɦɹ ɜɢɞɚɦɢ ɩɚɪɚɋɭɦɦɚɬɨɪ ɩɪɟɨɛɪɚɡɨɜɚɬɟɥɶ ɩɪɟɨɛɪɚɡɨɜɚɬɟɥɶ ɦɟɬɪɨɜ ɨɬɧɨɫɹɬɫɹ ɤ ɫɩɨɫɨɛɭ ɢɯ ɢɫɩɨɥɶɡɨ-
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ɜɚɧɢɹ ɜ ɨɛɭɱɟɧɢɢ. Ʉɪɨɦɟ ɬɨɝɨ, ɭɞɨɛɧɨ ɫɱɢɬɚɬɶ, ɱɬɨ ɩɚɪɚɦɟɬɪɵ ɤɚɠɞɨɝɨ ɷɥɟɦɟɧ* ɬɚ ɹɜɥɹɸɬɫɹ ɟɝɨ ɫɜɨɣɫɬɜɚɦɢ ɢ ɯɪɚɧɹɬɫɹ Ɍɨɱɤɚ ɜɟɬɜɥɟɧɢɹ ɍɦɧɨɠɢɬɟɥɶ ɋɢɧɚɩɫ ɩɪɢ ɧɟɦ. ɋɨɜɨɤɭɩɧɨɫɬɶ ɩɚɪɚɦɟɬɪɨɜ ɜɫɟɯ ɷɥɟɦɟɧɬɨɜ ɫɟɬɢ ɧɚɡɵɜɚɸɬ ɜɟɤɬɨɪɨɦ ɩɚɪɚɊɢɫ. 1. ɉɪɨɫɬɟɣɲɢɟ ɷɥɟɦɟɧɬɵ ɫɟɬɢ ɦɟɬɪɨɜ ɫɟɬɢ. ɋɨɜɨɤɭɩɧɨɫɬɶ ɩɚɪɚɦɟɬɪɨɜ ɜɫɟɯ ɫɢɧɚɩɫɨɜ ɧɚɡɵɜɚɸɬ ɜɟɤɬɨɪɨɦ ɨɛɭɱɚɟɦɵɯ ɩɚɪɚɦɟɬɪɨɜ ɫɟɬɢ, ɤɚɪɬɨɣ ɜɟɫɨɜ ɫɜɹɡɟɣ ɢɥɢ ɫɢɧɚɩɬɢɱɟɫɤɨɣ ɤɚɪɬɨɣ. Ɉɬɦɟɬɢɦ, ɱɬɨ ɧɟɨɛɯɨɞɢɦɨ ɪɚɡɥɢɱɚɬɶ ɜɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɷɥɟɦɟɧɬɨɜ ɢ ɜɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɫɟɬɢ. Ɉɧɢ ɫɨɜɩɚɞɚɸɬ ɬɨɥɶɤɨ ɞɥɹ ɷɥɟɦɟɧɬɨɜ ɜɯɨɞɧɨɝɨ ɫɥɨɹ ɫɟɬɢ. ɂɡ ɩɪɢɜɟɞɟɧɧɵɯ ɧɚ ɪɢɫ. 1 ɷɥɟɦɟɧɬɨɜ ɦɨɠɧɨ ɩɨɫɬɪɨɢɬɶ ɩɪɚɤɬɢɱɟɫɤɢ ɥɸɛɭɸ ɧɟɣɪɨɧɧɭɸ ɫɟɬɶ. ȼɨɨɛɳɟ ɝɨɜɨɪɹ, ɧɟɬ ɧɢɤɚɤɢɯ ɩɪɚɜɢɥ, ɨɝɪɚɧɢɱɢɜɚɸɳɢɯ ɫɜɨɛɨɞɭ ɬɜɨɪɱɟɫɬɜɚ ɤɨɧɫɬɪɭɤɬɨɪɚ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ.
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Ɉɞɧɚɤɨ, ɟɫɬɶ ɧɚɛɨɪ ɫɬɪɭɤɬɭɪɧɵɯ ɟɞɢɧɢɰ ɩɨɫɬɪɨɟɧɢɹ ɫɟɬɟɣ, ɩɨɡɜɨɥɹɸɳɢɣ ɫɬɚɧɞɚɪɬɢɡɨɜɚɬɶ ɩɪɨɰɟɫɫ ɤɨɧɫɬɪɭɢɪɨɜɚɧɢɹ. Ⱦɟɬɚɥɶɧɵɣ ɚɧɚɥɢɡ ɪɚɡɥɢɱɧɵɯ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ɩɨɡɜɨɥɢɥ ɜɵɞɟɥɢɬɶ ɫɥɟɞɭɸɳɢɟ ɫɬɪɭɤɬɭɪɧɵɟ ɟɞɢɧɢɰɵ: 1 ɷɥɟɦɟɧɬ – ɧɟɞɟɥɢɦɚɹ ɱɚɫɬɶ ɫɟɬɢ, ɞɥɹ ɤɨɬɨɪɨɣ ɨɩɪɟɞɟɥɟɧɵ ɦɟɬɨɞɵ ɩɪɹɦɨɝɨ ɢ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ; 2 ɤɚɫɤɚɞ – ɫɟɬɶ ɫɨɫɬɚɜɥɟɧɧɚɹ ɢɡ ɩɨɫɥɟɞɨɜɚɬɟɥɶɧɨ ɫɜɹɡɚɧɧɵɯ ɫɥɨɟɜ, ɤɚɫɤɚɞɨɜ, ɰɢɤɥɨɜ ɢɥɢ ɷɥɟɦɟɧɬɨɜ; 3 ɫɥɨɣ – ɫɟɬɶ ɫɨɫɬɚɜɥɟɧɧɚɹ ɢɡ ɩɚɪɚɥɥɟɥɶɧɨ ɪɚɛɨɬɚɸɳɢɯ ɫɥɨɟɜ, ɤɚɫɤɚɞɨɜ, ɰɢɤɥɨɜ ɢɥɢ ɷɥɟɦɟɧɬɨɜ; 4 ɰɢɤɥ – ɤɚɫɤɚɞ ɜɵɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɤɨɬɨɪɨɝɨ ɩɨɫɬɭɩɚɸɬ ɧɚ ɟɝɨ ɫɨɛɫɬɜɟɧɧɵɣ ɜɯɨɞ. Ɉɱɟɜɢɞɧɨ, ɱɬɨ ɧɟ ɜɫɟ ɷɥɟɦɟɧɬɵ ɹɜɥɹɸɬɫɹ ɧɟɞɟɥɢɦɵɦɢ. ȼ ɫɥɟɞɭɸɳɟɦ ɪɚɡɞɟɥɟ ɛɭɞɟɬ ɩɪɢɜɟɞɟɧ ɪɹɞ ɫɨɫɬɚɜɧɵɯ ɷɥɟɦɟɧɬɨɜ. ȼɜɟɞɟɧɢɟ 1 S(4) 2 N(4) 3 SB(2,6) 4 SN(2,4,2) ɬɪɟɯ ɬɢɩɨɜ ɫɨɫɬɚɜɧɵɯ ɫɟɬɟɣ ɫɜɹɡɚɧɨ ɫ ɞɜɭɦɹ N(4) ɩɪɢɱɢɧɚɦɢ: ɢɫɩɨɥɶɡɨS(4) ɜɚɧɢɟ ɰɢɤɥɨɜ ɩɪɢɜɨɞɢɬ ɤ ɢɡɦɟɧɟɧɢɸ ɩɪɚN(4) ɜɢɥ ɨɫɬɚɧɨɜɤɢ ɪɚɛɨɬɵ ɫɟɬɢ, ɨɩɢɫɚɧɧɵɯ ɜ 5 K(4,2,4,2) 6 NW(4,2,3,1) ɪɚɡɞ. "ɉɪɚɜɢɥɚ ɨɫɬɚɧɨɜɤɢ ɪɚɛɨɬɵ ɫɟɬɢ"; SB(4,8) SN(2,4,2) K(4,2,4,2) K(2,3,2,3) N(3) ɪɚɡɞɟɥɟɧɢɟ ɤɚɫɤɚɞɨɜ ɢ ɫɥɨɟɜ ɩɨɡɜɨɥɹɟɬ ɷɮɮɟɤɬɢɜɧɨ ɢɫɩɨɥɶɡɨɊɢɫ. 2. ɉɨɫɬɪɨɟɧɢɟ ɫɟɬɢ ɢɡ ɩɪɨɫɬɟɣɲɢɯ ɷɥɟɦɟɧɬɨɜ. 1 - ɫɥɨɣ ɫɢɧɚɩɫɨɜ S4 (4 ɜɚɬɶ ɪɟɫɭɪɫɵ ɩɚɪɚɥɨɡɧɚɱɚɟɬ ɱɢɫɥɨ ɫɢɧɚɩɫɨɜ, ɜɯɨɞɹɳɢɯ ɜ ɫɥɨɣ). 2 - ɤɚɫɤɚɞ-ɧɟɣɪɨɧ N4 (4 - ɨɡɧɚɱɚɟɬ ɥɟɥɶɧɵɯ ɗȼɆ. Ⱦɟɣɫɬɱɢɫɥɨ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ). 3 - ɫɥɨɣ ɬɨɱɟɤ ɜɟɬɜɥɟɧɢɹ SB(2,6) (ɩɟɪɜɚɹ ɰɢɮɪɚ ɜɢɬɟɥɶɧɨ, ɜɫɟ ɫɟɬɢ, ɱɢɫɥɨ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ, ɜɬɨɪɚɹ - ɜɵɯɨɞɧɵɯ). 4 - ɫɥɨɣ ɧɟɣɪɨɧɨɜ SN(2,4,2) ɜɯɨɞɹɳɢɟ ɜ ɫɨɫɬɚɜ (ɩɟɪɜɚɹ ɰɢɮɪɚ - ɱɢɫɥɨ ɧɟɣɪɨɧɨɜ, ɜɬɨɪɚɹ - ɱɢɫɥɨ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɭ ɤɚɠɞɨɝɨ ɫɥɨɹ, ɦɨɝɭɬ ɪɚɛɨɬɚɬɶ ɧɟɣɪɨɧɚ, ɬɪɟɬɶɹ - ɜɵɯɨɞɧɵɯ). 5 - ɤɚɫɤɚɞ ɬɨɱɟɤ ɜɟɬɜɥɟɧɢɹ ɢ ɧɟɣɪɨɧɨɜ K(4,2,4,2) ɧɟɡɚɜɢɫɢɦɨ ɞɪɭɝ ɨɬ (ɩɟɪɜɚɹ ɰɢɮɪɚ ɨɡɧɚɱɚɟɬ ɱɢɫɥɨ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ, ɜɬɨɪɚɹ - ɱɢɫɥɨ ɧɟɣɪɨɧɨɜ, ɞɪɭɝɚ. Ɍɟɦ ɫɚɦɵɦ ɩɪɢ ɬɪɟɬɶɹ - ɱɢɫɥɨ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɭ ɤɚɠɞɨɝɨ ɧɟɣɪɨɧɚ, ɱɟɬɜɟɪɬɚɹ - ɱɢɫɥɨ ɜɵɯɨɞɤɨɧɫɬɪɭɢɪɨɜɚɧɢɢ ɫɟɬɢ ɧɵɯ ɫɢɝɧɚɥɨɜ ɤɚɫɤɚɞɚ). 6 - ɫɟɬɶ NW(4,2,3,1) (ɩɟɪɜɚɹ ɰɢɮɪɚ - ɱɢɫɥɨ ɜɯɨɞɧɵɯ ɚɜɬɨɦɚɬɢɱɟɫɤɢ ɡɚɤɥɚɫɢɝɧɚɥɨɜ ɫɟɬɢ, ɜɬɨɪɚɹ - ɱɢɫɥɨ ɧɟɣɪɨɧɨɜ ɜɨ ɜɯɨɞɧɨɦ ɫɥɨɟ, ɬɪɟɬɶɹ - ɱɢɫɥɨ ɧɟɣɞɵɜɚɟɬɫɹ ɛɚɡɚ ɞɥɹ ɪɨɧɨɜ ɜ ɫɤɪɵɬɨɦ ɫɥɨɟ, ɱɟɬɜɟɪɬɚɹ - ɱɢɫɥɨ ɧɟɣɪɨɧɨɜ ɜ ɜɵɯɨɞɧɨɦ ɫɥɨɟ). ɢɫɩɨɥɶɡɨɜɚɧɢɹ ɩɚɪɚɥɥɟɥɶɧɵɯ ɗȼɆ. ɇɚ ɪɢɫ. 2 ɩɪɢɜɟɞɟɧ ɩɪɢɦɟɪ ɩɨɷɬɚɩɧɨɝɨ ɤɨɧɫɬɪɭɢɪɨɜɚɧɢɹ ɬɪɟɯɫɥɨɣɧɨɣ ɫɢɝɦɨɢɞɧɨɣ ɫɟɬɢ.
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5.1.2 ɋɨɫɬɚɜɧɵɟ ɷɥɟɦɟɧɬɵ ɇɚɡɜɚɧɢɟ «ɫɨɫɬɚɜɧɵɟ ɷɥɟɦɟɧɬɵ» ɩɪɨɬɢɜɨɪɟɱɢɬ ɨɩɪɟɞɟɥɟɧɢɸ ɷɥɟɦɟɧɬɨɜ. ɗɬɨ ɩɪɨɬɢɜɨɪɟɱɢɟ ɨɛɴɹɫɧɹɟɬɫɹ ɫɨɨɛɪɚɠɟɧɢɹɦɢ ɭɞɨɛɫɬɜɚ ɪɚɛɨɬɵ. ȼɜɟɞɟɧɢɟ ɫɨɫɬɚɜɧɵɯ ɷɥɟɦɟɧɬɨɜ ɩɪɟɫɥɟɞɭɟɬ ɰɟɥɶ ɭɩɪɨɳɟɧɢɹ ɤɨɧɫɬɪɭɢɪɨɜɚɧɢɹ. Ʉɚɤ ɩɪɚɜɢɥɨ, ɫɨɫɬɚɜɧɵɟ ɷɥɟɦɟɧɬɵ ɹɜɥɹɸɬɫɹ ɤɚɫɤɚɞɚɦɢ ɩɪɨɫɬɵɯ ɷɥɟɦɟɧɬɨɜ. ɏɨɪɨɲɢɦ ɩɪɢɦɟɪɨɦ ɩɨɥɟɡɧɨ-
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Ɋɢɫ. 3. ɚ)Ɏɪɚɝɦɟɧɬ ɫɟɬɢ ɫ ɨɛɵɱɧɵɦɢ ɫɭɦɦɚɬɨɪɚɦɢ ɛ) Ɍɨɬ ɠɟ ɮɪɚɝɦɟɧɬ ɫ ɤɜɚɞɪɚɬɢɱɧɵɦɢ ɫɭɦɦɚɬɨɪɚɦɢ ɢɡ ɩɪɨɫɬɵɯ ɷɥɟɦɟɧɬɨɜ . ɜ)Ɍɨɬ ɠɟ ɮɪɚɝɦɟɧɬ ɫ ɤɜɚɞɪɚɬɢɱɧɵɦɢ ɫɭɦɦɚɬɨɪɚɦɢ ɫ ɢɫɩɨɥɶɡɨɜɚɧɢɟɦ ɫɨɫɬɚɜɧɨɝɨ ɷɥɟɦɟɧɬɚ - ɤɜɚɞɪɚɬɢɱɧɨɝɨ ɫɭɦɦɚɬɨɪɚ .
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ɫɬɢ ɫɨɫɬɚɜɧɵɯ ɷɥɟɦɟɧɬɨɜ ɦɨɠɟɬ ɫɥɭɠɢɬɶ ɢɫɩɨɥɶɡɨɜɚɧɢɟ ɫɭɦɦɚɬɨɪɨɜ. ȼ ɪɹɞɟ ɪɚɛɨɬ [36, 53, 106, 126, 288] ɢɧɬɟɧɫɢɜɧɨ ɢɫɩɨɥɶɡɭɸɬɫɹ ɫɟɬɢ, ɧɟɣɪɨɧɵ ɤɨɬɨɪɵɯ ɫɨɞɟɪɠɚɬ ɧɟɥɢɧɟɣɧɵɟ ɜɯɨɞɧɵɟ ɫɭɦɦɚɬɨɪɵ. ɉɨɞ ɧɟɥɢɧɟɣɧɵɦ ɜɯɨɞɧɵɦ ɫɭɦɦɚɬɨɪɨɦ, ɱɚɳɟ ɜɫɟɝɨ ɩɨɧɢɦɚɸɬ ɤɜɚɞɪɚɬɢɱɧɵɟ ɫɭɦɦɚɬɨɪɵ – ɫɭɦɦɚɬɨɪɵ, ɜɵɱɢɫɥɹɸɳɢɟ ɜɡɜɟɲɟɧɧɭɸ ɫɭɦɦɭ ɜɫɟɯ ɩɨɩɚɪɧɵɯ ɩɪɨɢɡɜɟɞɟɧɢɣ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɧɟɣɪɨɧɚ. Ɉɬɥɢɱɢɟ ɫɟɬɟɣ ɫ ɤɜɚɞɪɚɬɢɱɧɵɦɢ ɫɭɦɦɚɬɨɪɚɦɢ ɡɚɤɥɸɱɚɟɬɫɹ ɬɨɥɶɤɨ ɜ ɢɫɩɨɥɶɡɨɜɚɧɢɢ ɷɬɢɯ ɫɭɦɦɚɬɨɪɨɜ. ɇɚ ɪɢɫ. 3ɚ ɩɪɢɜɟɞɟɧ ɮɪɚɝɦɟɧɬ ɫɟɬɢ ɫ ɥɢɧɟɣɧɵɦɢ ɫɭɦɦɚɬɨɪɚɦɢ. ɇɚ ɪɢɫ. 3ɛ – ɫɨɨɬɜɟɬɫɬɜɭɸɳɢɣ ɟɦɭ ɮɪɚɝɦɟɧɬ ɫ ɤɜɚɞɪɚɬɢɱɧɵɦɢ ɫɭɦɦɚɬɨɪɚɦɢ, ɩɨɫɬɪɨɟɧɧɵɣ ɫ ɢɫɩɨɥɶɡɨɜɚɧɢɟɦ ɷɥɟɦɟɧɬɨɜ, ɩɪɢɜɟɞɟɧɧɵɯ ɧɚ ɪɢɫ. 1. ɇɚ (ɪɢɫ. 3ɜ) – ɬɨɬ ɠɟ ɮɪɚɝɦɟɧɬ, ɩɨɫɬɪɨɟɧɧɵɣ ɫ ɢɫɩɨɥɶɡɨɜɚɧɢɟɦ ɤɜɚɞɪɚɬɢɱɧɵɯ ɫɭɦɦɚɬɨɪɨɜ. ɉɪɢ ɫɨɫɬɚɜɥɟɧɢɢ ɫɟɬɢ ɫ ɤɜɚɞɪɚɬɢɱɧɵɦɢ ɫɭɦɦɚɬɨɪɚɦɢ ɢɡ ɩɪɨɫɬɵɯ ɷɥɟɦɟɧɬɨɜ ɧɚ ɩɨɥɶɡɨɜɚɬɟɥɹ ɥɨɠɢɬɫɹ ɛɨɥɶɲɨɣ ɨɛɴɟɦ ɪɚɛɨɬ ɩɨ ɩɪɨɜɟɞɟɧɢɸ ɫɜɹɡɟɣ ɢ ɨɪɝɚɧɢɡɚɰɢɢ ɜɵɱɢɫɥɟɧɢɹ ɩɨɩɚɪɧɵɯ ɩɪɨɢɡɜɟɞɟɧɢɣ. Ʉɪɨɦɟ ɬɨɝɨ, ɪɢɫ. 3ɜ ɝɨɪɚɡɞɨ ɩɨɧɹɬɧɟɟ ɪɢɫ. 3ɛ ɢ ɫɨɞɟɪɠɢɬ ɬɭ ɠɟ ɢɧɮɨɪɦɚɰɢɸ. Ʉɪɨɦɟ ɬɨɝɨ, ɩɨɥɶɡɨɜɚɬɟɥɶ ɦɨɠɟɬ ɢɡɦɟɧɢɬɶ ɬɢɩ ɫɭɦɦɚɬɨɪɨɜ ɭɠɟ ɫɤɨɧɫɬɪɭɢɪɨɜɚɧɧɨɣ ɫɟɬɢ, ɭɤɚɡɚɜ ɡɚɦɟɧɭ ɨɞɧɨɝɨ ɬɢɩɚ ɫɭɦɦɚɬɨɪɚ ɧɚ ɞɪɭɝɨɣ. ɇɚ ɪɢɫ. 4 ɩɪɢɜɟɞɟɧɵ ɨɛɨɡɧɚɱɟɧɢɹ ɢ ɫɯɟɦɵ ɧɚɢɛɨɥɟɟ ɱɚɫɬɨ ɢɫɩɨɥɶɡɭɟɦɵɯ ɫɨɫɬɚɜɧɵɯ ɷɥɟɦɟɧɬɨɜ.
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ɇɟɨɛɯɨɞɢɦɨ ɨɬɦɟɬɢɬɶ ɟɳɟ ɨɞɧɭ ɪɚɡɧɨɜɢɞɧɨɫɬɶ ɫɭɦɦɚɬɨɪɨɜ, ɩɨɥɟɡɧɭɸ ɩɪɢ ɪɚɛɨɬɟ ɩɨ ɤɨɧɫɬɪɭɢɪɨɜɚɧɢɸ ɫɟɬɢ – ɧɟɨɞɧɨɪɨɞɧɵɟ ɫɭɦɦɚɬɨɪɵ. ɇɟɨɞɧɨɪɨɞɧɵɣ ɫɭɦɦɚɬɨɪ ɨɬɥɢɱɚɟɬɫɹ ɨɬ ɨɞɧɨɪɨɞɧɨɝɨ ɧɚɥɢɱɢɟɦ ɟɳɟ ɨɞɧɨɝɨ ɜɯɨɞɧɨɝɨ ɫɢɝɧɚɥɚ, ɪɚɜɧɨɝɨ ɟɞɢɧɢɰɟ. ɇɚ ɪɢɫ. 4ɝ ɩɪɢɜɟɞɟɧɵ ɫɯɟɦɚ ɢ ɨɛɨɡɧɚɱɟɧɢɹ ɞɥɹ ɧɟɨɞɧɨɪɨɞɧɨɝɨ ɚɞɚɩɬɢɜɧɨɝɨ ɫɭɦɦɚɬɨɪɚ. ȼ ɬɚɛɥ. 1 ɩɪɢɜɟɞɟɧɵ ɡɧɚɱɟɧɢɹ, ɜɵɱɢɫɥɹɟɦɵɟ ɨɞɧɨɪɨɞɧɵɦɢ ɢ ɫɨɨɬɜɟɬɫɬɜɭɸɳɢɦɢ ɢɦ ɧɟɨɞɧɨɪɨɞɧɵɦɢ ɫɭɦɦɚɬɨɪɚɦɢ.
5.1.3 Ɏɭɧɤɰɢɨɧɢɪɨɜɚɧɢɟ ɫɟɬɢ ɉɪɟɠɞɟ ɜɫɟɝɨ, ɧɟɨɛɯɨɞɢɦɨ ɪɚɡɞɟɥɢɬɶ ɩɪɨɰɟɫɫɵ ɨɛɭɱɟɧɢɹ ɧɟɣɪɨɧɧɨɣ ɫɟɬɢ ɢ ɢɫɩɨɥɶɡɨɜɚɧɢɹ ɨɛɭɱɟɧɧɨɣ ɫɟɬɢ. ɉɪɢ ɢɫɩɨɥɶɡɨɜɚɧɢɢ ɨɛɭɱɟɧɧɨɣ ɫɟɬɢ ɩɪɨɢɫɯɨɞɢɬ ɬɨɥɶɤɨ ɪɟɲɟɧɢɟ ɫɟɬɶɸ ɨɩɪɟɞɟɥɟɧɧɨɣ ɡɚɞɚɱɢ. ɉɪɢ ɷɬɨɦ ɫɢɧɚɩɬɢɱɟɫɤɚɹ ɤɚɪɬɚ ɫɟɬɢ ɨɫɬɚɟɬɫɹ ɧɟɢɡɦɟɧɧɨɣ. Ɋɚɛɨɬɭ ɫɟɬɢ ɩɪɢ ɪɟɲɟɧɢɢ ɡɚɞɚɱɢ ɛɭɞɟɦ ɞɚɥɟɟ ɧɚɡɵɜɚɬɶ ɩɪɹɦɵɦ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɟɦ.
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ɉɪɢ ɨɛɭɱɟɧɢɢ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ɦɟɬɨɞɨɦ ɨɛɪɚɬɧɨɝɨ ɪɚɫɩɪɨɫɬɪɚɧɟɧɢɹ ɨɲɢɛɤɢ ɧɟɣɪɨɧɧɚɹ ɫɟɬɶ (ɢ ɤɚɠɞɵɣ ɫɨɫɬɚɜɥɹɸɳɢɣ ɟɟ ɷɥɟɦɟɧɬ) ɞɨɥɠɧɚ ɭɦɟɬɶ ɜɵɩɨɥɧɹɬɶ ɨɛɪɚɬɧɨɟ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɟ. ȼɨ ɜɬɨɪɨɣ ɱɚɫɬɢ ɷɬɨɣ ɝɥɚɜɵ ɛɭɞɟɬ ɩɨɤɚɡɚɧɨ, ɱɬɨ ɨɛɪɚɬɧɨɟ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɟ ɩɨɡɜɨɥɹɟɬ ɨɛɭɱɚɬɶ ɬɚɤɠɟ ɢ ɧɟɣɪɨɫɟɬɢ, ɬɪɚɞɢɰɢɨɧɧɨ ɫɱɢɬɚɸɳɢɟɫɹ ɧɟ ɨɛɭɱɚɟɦɵɦɢ, ɚ ɮɨɪɦɢɪɭɟɦɵɦɢ (ɧɚɩɪɢɦɟɪ, ɫɟɬɢ ɏɨɩɮɢɥɞɚ). Ɉɛɪɚɬɧɵɦ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɟɦ ɧɚɡɵɜɚɟɬɫɹ ɩɪɨɰɟɫɫ ɪɚɛɨɬɵ ɫɟɬɢ, ɤɨɝɞɚ ɧɚ ɜɵɯɨɞ ɫɟɬɢ ɩɨɞɚɸɬɫɹ ɨɩɪɟɞɟɥɟɧɧɵɟ ɫɢɝɧɚɥɵ, ɤɨɬɨɪɵɟ ɞɚɥɟɟ ɪɚɫɩɪɨɫɬɪɚɧɹɸɬɫɹ ɩɨ ɬɟɦ ɠɟ ɫɜɹɡɹɦ, ɱɬɨ ɢ ɩɪɢ ɩɪɹɦɨɦ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɢ ɞɨ ɜɯɨɞɚ ɫɟɬɢ. ɉɪɢ ɩɪɨɯɨɠɞɟɧɢɢ ɫɢɝɧɚɥɨɜ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ ɱɟɪɟɡ ɷɥɟɦɟɧɬ ɫ ɨɛɭɱɚɟɦɵɦɢ ɩɚɪɚɦɟɬɪɚɦɢ ɜɵɱɢɫɥɹɸɬɫɹ ɩɨɩɪɚɜɤɢ ɤ ɩɚɪɚɦɟɬɪɚɦ ɷɬɨɝɨ ɷɥɟɦɟɧɬɚ. ȿɫɥɢ ɧɚ ɜɵɯɨɞ ɫɟɬɢ ɫ ɧɟɩɪɟɪɵɜɧɵɦɢ ɷɥɟɦɟɧɬɚɦɢ ɩɨɞɚɟɬɫɹ ɩɪɨɢɡɜɨɞɧɚɹ ɧɟɤɨɬɨɪɨɣ ɮɭɧɤɰɢɢ F ɨɬ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɫɟɬɢ, ɬɨ ɜɵɱɢɫɥɹɟɦɵɟ ɫɟɬɶɸ ɩɨɩɪɚɜɤɢ ɞɨɥɠɧɵ ɛɵɬɶ ɷɥɟɦɟɧɬɚɦɢ ɝɪɚɞɢɟɧɬɚ ɮɭɧɤɰɢɢ F ɩɨ ɨɛɭɱɚɟɦɵɦ ɩɚɪɚɦɟɬɪɚɦ ɫɟɬɢ. ɂɫɯɨɞɹ ɢɡ ɷɬɨɝɨ ɬɪɟɛɨɜɚɧɢɹ ɨɩɪɟɞɟɥɢɦ ɩɪɚɜɢɥɚ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ ɞɥɹ ɷɥɟɦɟɧɬɨɜ ɫɟɬɢ, ɩɪɢɜɟɞɟɧɧɵɯ ɧɚ ɪɢɫ. 1, ɜ ɩɪɟɞɩɨɥɨɠɟɧɢɢ, ɱɬɨ ɫɟɬɶ ɫɨɫɬɨɢɬ ɬɨɥɶɤɨ ɢɡ ɨɞɧɨɝɨ ɷɥɟɦɟɧɬɚ.
5.1.3.1 ɋɢɧɚɩɫ ɍ ɫɢɧɚɩɫɚ ɞɜɚ ɜɯɨɞɚ – ɜɯɨɞ ɫɢɝɧɚɥɚ ɢ ɜɯɨɞ ɫɢɧɚɩɬɢɱɟɫɤɨɝɨ ɜɟɫɚ (ɪɢɫ. 5ɚ). Ɉɛɨɡɧɚɱɢɦ ɜɯɨɞɧɨɣ ɫɢɝɧɚɥ ɫɢɧɚɩɫɚ ɱɟɪɟɡ x , ɚ ɫɢɧɚɩɬɢɱɟɫɤɢɣ ɜɟɫ ɱɟɪɟɡ a . Ɍɨɝɞɚ ɜɵɯɨɞɧɨɣ ɫɢɝɧɚɥ ɫɢɧɚɩɫɚ ɪɚɜɟɧ ax . ɉɪɢ
¶F ¶ (ax ) . ɇɚ ɜɯɨɞɟ ɫɢɧɚɩɫɚ ɞɨɥɠɟɧ
ɨɛɪɚɬɧɨɦ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɢ ɧɚ ɜɵɯɨɞ ɫɢɧɚɩɫɚ ɩɨɞɚɟɬɫɹ ɫɢɝɧɚɥ ɛɵɬɶ ɩɨɥɭɱɟɧ ɫɢɝɧɚɥ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ, ɪɚɜɧɵɣ
¶F ¶F ¶ (ax ) ¶F , ɚ ɧɚ ɜɯɨɞɟ = =a ¶x ¶ (ax ) ¶x ¶ (ax )
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ɫɢɧɚɩɬɢɱɟɫɤɨɝɨ ɜɟɫɚ – ɩɨɩɪɚɜɤɚ ɤ ɫɢɧɚɩɬɢɱɟɫɤɨɦɭ
¶F ¶F ¶ (ax ) ¶F ɜɟɫɭ, ɪɚɜɧɚɹ = =x (ɪɢɫ. ¶a ¶ (ax ) ¶a ¶ (ax )
¶F ¶a ¶F ¶ (ax )
Ɋɢɫ. 5. ɉɪɹɦɨɟ (ɚ) ɢ ɨɛɪɚɬɧɨɟ (ɛ) ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɟ ɫɢɧɚɩɫɚ
5ɛ).
5.1.3.2 ɍɦɧɨɠɢɬɟɥɶ ɍɦɧɨɠɢɬɟɥɶ ɢɦɟɟɬ ɞɜɚ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɚ ɢ ɧɟ ɢɦɟɟɬ ɩɚɪɚɦɟɬɪɨɜ. Ɉɛɨɡɧɚɱɢɦ ɜɯɨɞɧɵɟ ɫɢɝɧɚɥ ɫɢɧɚɩɫɚ ɱɟɪɟɡ x1, x 2 . Ɍɨɝɞɚ ɜɵɯɨɞɧɨɣ ɫɢɝɧɚɥ
a
x2
ɭɦɧɨɠɢɬɟɥɹ ɪɚɜɟɧ x1x 2 (ɪɢɫ. 6ɚ). ɉɪɢ ɨɛɪɚɬɧɨɦ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɢ ɧɚ ɜɵɯɨɞ ɭɦɧɨɠɢɬɟɥɹ ɩɨɞɚɟɬ-
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ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ,
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Ɋɢɫ. 6. ɉɪɹɦɨɟ (ɚ) ɢ ɨɛɪɚɬɧɨɟ (ɛ) ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɟ ɭɦɧɨɠɢɬɟɥɹ
ɫɹ ɫɢɝɧɚɥ ¶F ¶ x1x 2 . ɇɚ ɜɯɨɞɚɯ ɫɢɝɧɚɥɨɜ x1 ɢ
x 2 ɞɨɥɠɧɵ ɛɵɬɶ ɩɨɥɭɱɟɧɵ ɫɢɝɧɚɥɵ ɨɛɪɚɬɧɨɝɨ
ɛ
x1x 2
¶F ¶F ¶ ( x1x 2 ) ¶F = = x2 ¶x1 ¶ ( x1x 2 ) ¶x1 ¶ ( x1x 2 )
ɢ
¶F ¶F ¶ ( x1x 2 ) ¶F = = x1 , ɫɨɨɬɜɟɬɫɬɜɟɧɧɨ (ɪɢɫ. 6ɛ). ¶x 2 ¶ ( x1x 2 ) ¶x 2 ¶ ( x1x 2 ) 5.1.3.3 Ɍɨɱɤɚ ɜɟɬɜɥɟɧɢɹ
ȼ ɨɬɥɢɱɢɟ ɨɬ ɪɚɧɟɟ ɪɚɫɫɦɨɬɪɟɧɧɵɯ ɷɥɟɦɟɧɬɨɜ, ɬɨɱɤɚ ɜɟɬɜɥɟɧɢɹ ɢɦɟɟɬ ɬɨɥɶɤɨ ɨɞɢɧ ɜɯɨɞ ɢ ɧɟɫɤɨɥɶɤɨ ɜɵɯɨɞɨɜ. Ɉɛɨɡɧɚɱɢɦ ɜɯɨɞɧɨɣ ɫɢɝɧɚɥ ɱɟɪɟɡ x, ɚ ɜɵɯɨɞɧɵɟ ɱɟɪɟɡ x1 , x2 , K , xn , ɩɪɢɱɟɦ x i = x (ɪɢɫ. 7ɚ). ɉɪɢ ɨɛɪɚɬɧɨɦ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɢ ɧɚ ɜɵɯɨɞɧɵɟ ɫɜɹɡɢ ɬɨɱɤɢ ɜɟɬɜɥɟɧɢɹ ɩɨɞɚɸɬɫɹ ɫɢɝɧɚɥɵ
¶F ¶x i
(ɪɢɫ. 7ɛ). ɇɚ ɜɯɨɞɧɨɣ ɫɜɹɡɢ ɞɨɥɠɟɧ ɩɨɥɭɱɚɬɶɫɹ ɫɢɝɧɚɥ, n n ¶F ¶F ¶x i ¶F ɪɚɜɧɵɣ . Ɇɨɠɧɨ ɫɤɚɡɚɬɶ, = = ¶x i =1 ¶x i ¶x i -1 ¶x i ɱɬɨ ɬɨɱɤɚ ɜɟɬɜɥɟɧɢɹ ɩɪɢ ɨɛɪɚɬɧɨɦ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɢ
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Ɋɢɫ. 7. ɉɪɹɦɨɟ (ɚ) ɢ ɨɛɪɚɬɧɨɟ (ɛ) ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɟ ɬɨɱɤɢ ɜɟɬɜɥɟɧɢɹ
ɩɟɪɟɯɨɞɢɬ ɜ ɫɭɦɦɚɬɨɪ, ɢɥɢ, ɞɪɭɝɢɦɢ ɫɥɨɜɚɦɢ, ɫɭɦɦɚɬɨɪ ɹɜɥɹɟɬɫɹ ɞɜɨɣɫɬɜɟɧɧɵɦ ɩɨ ɨɬɧɨɲɟɧɢɸ ɤ ɬɨɱɤɟ ɜɟɬɜɥɟɧɢɹ.
5.1.3.4 ɋɭɦɦɚɬɨɪ ɋɭɦɦɚɬɨɪ ɫɱɢɬɚɟɬ ɫɭɦɦɭ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ. Ɉɛɵɱɧɵɣ ɫɭɦɦɚɬɨɪ ɧɟ ɢɦɟɟɬ ɩɚɪɚɦɟɬɪɨɜ. ɉɪɢ ɨɩɢɫɚɧɢɢ ɩɪɹɦɨɝɨ ɢ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ ɨɝɪɚɧɢɱɢɦɫɹ ɨɩɢɫɚɧɢɟɦ ɩɪɨɫɬɨɝɨ ɫɭɦɦɚɬɨɪɚ, ɩɨɫɤɨɥɶɤɭ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɟ ɚɞɚɩɬɢɜɧɨɝɨ ɢ ɤɜɚɞɪɚɬɢɱɧɨɝɨ ɫɭɦɦɚɬɨɪɚ ɦɨɠɟɬ ɛɵɬɶ ɩɨɥɭɱɟɧɨ ɤɚɤ ɩɪɹɦɨɟ ɢ ɨɛɪɚɬɧɨɟ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɟ ɫɟɬɢ ɜ ɫɨɨɬɜɟɬɫɬɜɢɢ ɫ ɢɯ ɫɯɟɦɚɦɢ, ɩɪɢɜɟɞɟɧɧɵɦɢ ɧɚ ɪɢɫ. 3ɛ ɢ 3ɜ. Ɉɛɨɡɧɚɱɢɦ n x i . ɉɪɢ ɨɛɪɚɬɜɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɫɭɦɦɚɬɨɪɚ ɱɟɪɟɡ x1 , x2 , K , xn (ɪɢɫ. 8ɚ). ȼɵɯɨɞɧɨɣ ɫɢɝɧɚɥ ɪɚɜɟɧ i =1 ɧɨɦ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɢ ɧɚ ɜɵɯɨɞɧɭɸ ɫɜɹɡɶ ɫɭɦɦɚɬɨɪɚ ɩɨɞɚɟɬɫɹ ɚ x1 ¶F ɛ n ¶F x2 n ɫɢɝɧɚɥ (ɪɢɫ. 8ɛ). ɇɚ x i n ... ¶ xi i = 1 ¶ xi xn i =1 ¶F i =1 ¶F n ɜɯɨɞɧɵɯ ɫɜɹɡɹɯ ɞɨɥɠɧɵ ɩɨɥɭɱɚɬɶn ... ¶ xi ɫɹ ɫɢɝɧɚɥɵ, ɪɚɜɧɵɟ ¶ xi i =1 n ¶F i =1 Ɋɢɫ. 8. ɉɪɹɦɨɟ (ɚ) ɢ ¶ xi n ɨɛɪɚɬɧɨɟ (ɛ) ¶F ¶F ¶F i 1 = = = ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɟ ¶ xi n n ¶x j ¶x j ɫɭɦɦɚɬɨɪɚ i =1 ¶ xi ¶ xi
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ɂɡ ɩɨɫɥɟɞɧɟɣ ɮɨɪɦɭɥɵ ɫɥɟɞɭɟɬ, ɱɬɨ ɜɫɟ ɫɢɝɧɚɥɵ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ, ɜɵɞɚɜɚɟɦɵɟ ɧɚ ɜɯɨɞɧɵɟ ɫɜɹɡɢ ɫɭɦɦɚɬɨɪɚ, ɪɚɜɧɵ. Ɍɚɤɢɦ ɨɛɪɚɡɨɦ ɫɭɦɦɚɬɨɪ ɩɪɢ ɨɛɪɚɬɧɨɦ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɢ ɩɟɪɟɯɨɞɢɬ ɜ ɬɨɱɤɭ ɜɟɬɜɥɟɧɢɹ, ɢɥɢ, ɞɪɭɝɢɦɢ ɫɥɨɜɚɦɢ, ɫɭɦɦɚɬɨɪ ɹɜɥɹɟɬɫɹ ɞɜɨɣɫɬɜɟɧɧɵɦ ɩɨ ɨɬɧɨɲɟɧɢɸ ɤ ɬɨɱɤɟ ɜɟɬɜɥɟɧɢɹ.
5.1.3.5 ɇɟɥɢɧɟɣɧɵɣ ɉɚɞɟ ɩɪɟɨɛɪɚɡɨɜɚɬɟɥɶ ɇɟɥɢɧɟɣɧɵɣ ɉɚɞɟ ɩɪɟɨɛɪɚɡɨɜɚɬɟɥɶ ɢɥɢ ɉɚɞɟ ɷɥɟɦɟɧɬ ɢɦɟɟɬ ɞɜɚ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɚ ɢ ɨɞɢɧ ɜɵɯɨɞɧɨɣ. Ɉɛɨɡɧɚɱɢɦ ɜɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɱɟɪɟɡ x1, x 2 . Ɍɨɝɞɚ ɜɵɯɨɞɧɨɣ ɫɢɝɧɚɥ ɉɚɞɟ ɷɥɟɦɟɧɬɚ ɪɚɜɟɧ x1 x 2 (ɪɢɫ. 9ɚ). ɉɪɢ ɨɛɪɚɬɧɨɦ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɢ ɧɚ ɜɵɯɨɞ ɉɚɞɟ ɷɥɟɦɟɧɬɚ ɩɨɞɚɟɬɫɹ ɫɢɝɧɚɥ ¶F ɜɯɨɞɚɯ ɫɢɝɧɚɥɨɜ x1 ɢ x 2 ɞɨɥɠɧɵ ɛɵɬɶ ɩɨɥɭɱɟɧɵ ɫɢɝɧɚɥɵ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ, ɪɚɜɧɵɟ
¶ ( x1 x 2 ) 1 ¶F ¶F ¶F = = x 2 ¶ ( x1 x 2 ) ¶x1 ¶ ( x1 x 2 ) ¶x1
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Ɋɢɫ. 9. ɉɪɹɦɨɟ (ɚ) ɢ ɨɛɪɚɬɧɨɟ (ɛ) ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɟ ɧɟɥɢɧɟɣɧɨɝɨ ɉɚɞɟ ɩɪɟɨɛɪɚɡɨɜɚɬɟɥɹ
, ɫɨɨɬɜɟɬɫɬɜɟɧɧɨ (ɪɢɫ. 9ɛ).
5.1.3.6 ɇɟɥɢɧɟɣɧɵɣ ɫɢɝɦɨɢɞɧɵɣ ɩɪɟ-
CHAP5-1.DOC
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¶F ¶ ( x1 x 2 )
1 ¶F x 2 ¶ ( x1 x 2 )
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¶ ( x1 x 2 ) ¶F ¶F x ¶F = =- 1 2 ¶ (x x ) ¶x 2 ¶ ( x1 x 2 ) ¶x 2 x2 1 2
x1 x 2
¶F x - 1 x 22 ¶ ( x1 x 2 )
¶ ( x1 x 2 ) . ɇɚ
77
ɨɛɪɚɡɨɜɚɬɟɥɶ ɇɟɥɢɧɟɣɧɵɣ ɫɢɝɦɨɢɞɧɵɣ ɩɪɟɨɛɪɚɡɨɜɚɬɟɥɶ ɢɥɢ ɫɢɝɦɨɢɞɧɵɣ ɷɥɟɦɟɧɬ ɢɦɟɟɬ ɚ ɛ ¶F ɨɞɢɧ ɜɯɨɞɧɨɣ ɫɢɝɧɚɥ ɢ ɨɞɢɧ ɩɚɪɚɦɟɬɪ. sa a ¶s a , x ɋɬɨɪɨɧɧɢɤɢ ɱɢɫɬɨɝɨ ɤɨɧɧɟɤɰɢɨɧɢɫɬɫɤɨɝɨ s s a, x ¶F x ɩɨɞɯɨɞɚ ɫɱɢɬɚɸɬ, ɱɬɨ ɨɛɭɱɚɬɶɫɹ ɜ ɯɨɞɟ s ɨɛɭɱɟɧɢɹ ɧɟɣɪɨɧɧɨɣ ɫɟɬɢ ɦɨɝɭɬ ɬɨɥɶɤɨ ¶s a , x ¶F sx ɜɟɫɚ ɫɜɹɡɟɣ. ɋ ɷɬɨɣ ɬɨɱɤɢ ɡɪɟɧɢɹ ɩɚɪɚɦɟɬɪ ¶s a , x ɫɢɝɦɨɢɞɧɨɝɨ ɷɥɟɦɟɧɬɚ ɹɜɥɹɟɬɫɹ ɧɟ ɨɛɭɱɚɟɦɵɦ ɢ, ɤɚɤ ɫɥɟɞɫɬɜɢɟ, ɞɥɹ ɧɟɝɨ ɧɟɬ ɧɟɨɛɯɨɊɢɫ. 10. ɉɪɹɦɨɟ (ɚ) ɢ ɨɛɪɚɬɧɨɟ (ɛ) ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɟ ɞɢɦɨɫɬɢ ɜɵɱɢɫɥɹɬɶ ɩɨɩɪɚɜɤɭ. Ɉɞɧɚɤɨ, ɧɟɥɢɧɟɣɧɨɝɨ ɫɢɝɦɨɢɞɧɨɝɨ ɩɪɟɨɛɪɚɡɨɜɚɬɟɥɹ ɱɚɫɬɶ ɢɫɫɥɟɞɨɜɚɬɟɥɟɣ ɩɨɥɚɝɚɟɬ, ɱɬɨ ɧɭɠɧɨ ɨɛɭɱɚɬɶ ɜɫɟ ɩɚɪɚɦɟɬɪɵ ɜɫɟɯ ɷɥɟɦɟɧɬɨɜ ɫɟɬɢ. ɂɫɯɨɞɹ ɢɡ ɷɬɨɝɨ, ɨɩɢɲɟɦ ɜɵɱɢɫɥɟɧɢɟ ɷɬɢɦ ɷɥɟɦɟɧɬɨɦ ɩɨɩɪɚɜɤɢ ɤ ɫɨɞɟɪɠɚɳɟɦɭɫɹ ɜ ɧɟɦ ɩɚɪɚɦɟɬɪɭ. Ɉɛɨɡɧɚɱɢɦ ɜɯɨɞɧɨɣ ɫɢɝɧɚɥ ɱɟɪɟɡ x , ɩɚɪɚɦɟɬɪ ɱɟɪɟɡ a , ɚ ɜɵɱɢɫɥɹɟɦɭɸ ɷɬɢɦ ɩɪɟɨɛɪɚɡɨɜɚɬɟɥɟɦ
(
ɮɭɧɤɰɢɸ ɱɟɪɟɡ ɩɨɞɚɟɬɫɹ ɫɢɝɧɚɥ ɜɚɧɢɹ, ɪɚɜɧɵɣ
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s (a, x ) (ɪɢɫ. 10ɚ). ɉɪɢ ɨɛɪɚɬɧɨɦ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɢ ɧɚ ɜɵɯɨɞ ɫɢɝɦɨɢɞɧɨɝɨ ɷɥɟɦɟɧɬɚ ¶F ¶s (a , x ) . ɇɚ ɜɯɨɞɟ ɫɢɝɧɚɥɚ ɞɨɥɠɟɧ ɛɵɬɶ ɩɨɥɭɱɟɧ ɫɢɝɧɚɥ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨ-
¶F ¶F ¶s (a , x ) ¶F = =sx , ɚ ɧɚ ɜɯɨɞɟ ɩɚɪɚɦɟɬɪɚ ɩɨɩɪɚɜɤɚ, ɪɚɜɧɚɹ ¶x ¶s (a , x ) ¶x ¶s (a , x )
¶F ¶F ¶s (a , x ) ¶F = = sa (ɪɢɫ. 10ɛ). ¶a ¶s (a , x ) ¶a ¶s (a , x )
5.1.3.7 ɉɪɨɢɡɜɨɥɶɧɵɣ ɧɟɩɪɟɪɵɜɧɵɣ ɧɟɥɢɧɟɣɧɵɣ ɩɪɟɨɛɪɚɡɨɜɚɬɟɥɶ ɉɪɨɢɡɜɨɥɶɧɵɣ ɧɟɩɪɟɪɵɜɧɵɣ ɧɟɥɢɧɟɣɧɵɣ ɩɪɟɨɛɪɚɡɨɜɚɬɟɥɶ ɢɦɟɟɬ ɧɟɫɤɨɥɶɤɨ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ, ɚ ɪɟɚɥɢɡɭɟɦɚɹ ɢɦ ɮɭɧɤɰɢɹ ɡɚɜɢɫɢɬ ɨɬ ɧɟɫɤɨɥɶɤɢɯ ɩɚɪɚɦɟɬɪɨɜ. ȼɵɯɨɞɧɨɣ ɫɢɝɧɚɥ ɬɚɤɨɝɨ ɷɥɟɦɟɧɬɚ ɜɵɱɢɫɥɹɟɬɫɹ ɤɚɤ ɧɟɤɨɬɨɪɚɹ ɮɭɧɤɰɢɹ
j (x, a ) , ɝɞɟ x – ɜɟɤɬɨɪ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ, ɚ a – ɜɟɤɬɨɪ ɩɚɪɚɦɟɬɪɨɜ. ɉɪɢ
ɨɛɪɚɬɧɨɦ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɢ ɧɚ ɜɵɯɨɞɧɭɸ ɫɜɹɡɶ ɷɥɟɦɟɧɬɚ ɩɨɞɚɟɬɫɹ ɫɢɝɧɚɥ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ, ɪɚɜɧɵɣ ¶F ¶j . ɇɚ ɜɯɨɞɵ ɫɢɝɧɚɥɨɜ ɜɵɞɚɸɬɫɹ ɫɢɝɧɚɥɵ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ, ɪɚɜɧɵɟ
¶F ¶F ¶j ¶F = = j xi , ¶x i ¶j ¶x i ¶j
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ɧɚ
ɜɯɨɞɚɯ
ɩɚɪɚɦɟɬɪɨɜ
ɜɵɱɢɫɥɹɸɬɫɹ
ɩɨɩɪɚɜɤɢ,
ɪɚɜɧɵɟ
¶F ¶F ¶F ¶j = = ja i . ¶j ¶a i ¶j ¶a i 5.1.3.8 ɉɨɪɨɝɨɜɵɣ ɩɪɟɨɛɪɚɡɨɜɚɬɟɥɶ ɉɨɪɨɝɨɜɵɣ ɩɪɟɨɛɪɚɡɨɜɚɬɟɥɶ, ɪɟɚɥɢɡɭɸɳɢɣ ɮɭɧɤɰɢɸ ɨɩɪɟɞɟɥɟɧɢɹ ɡɧɚɤɚ (ɪɢɫ. 11ɚ), ɧɟ ɹɜɥɹɟɬɫɹ ɷɥɟɦɟɧɬɨɦ ɫ ɧɟɩɪɟɪɵɜɧɨɣ ɮɭɧɤɰɢɟɣ, ɢ, ɫɥɟɞɨɜɚɬɟɥɶɧɨ, ɟɝɨ ɨɛɪɚɬɧɨɟ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɟ ɧɟ ɦɨɠɟɬ ɛɵɬɶ ɨɩɪɟɞɟɥɟɧɨ ɢɡ ɬɪɟɛɨɜɚɧɢɹ ɜɵɱɢɫɥɟɧɢɹ ɝɪɚɞɢɟɧɬɚ. Ɉɞɧɚɤɨ, ɩɪɢ ɨɛɭɱɟɧɢɢ ɫɟɬɟɣ ɫ ɩɨɪɨɝɨɜɵɦɢ ɩɪɟɨɛɪɚɡɨɜɚɬɟɥɹɦɢ ɩɨɥɟɡɧɨ ɢɦɟɬɶ ɜɨɡɦɨɠɧɨɫɬɶ ɜɵɱɢɫɥɹɬɶ ɩɨɩɪɚɜɤɢ ɤ ɩɚɪɚɦɟɬɪɚɦ. Ɍɚɤ ɤɚɤ ɞɥɹ ɩɨɪɨɝɨɜɨɝɨ ɷɥɟɦɟɧɬɚ ɧɟɥɶɡɹ ɨɩɪɟɞɟɚ ì1 x > 0 ɥɢɬɶ ɨɞɧɨɡɧɚɱɧɨɟ ɩɨɜɟɞɟɧɢɟ ɩɪɢ ɨɛɪɚɬɧɨɦ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɢ, sign x = í x ɩɪɟɞɥɚɝɚɟɬɫɹ ɞɨɨɩɪɟɞɟɥɢɬɶ ɟɝɨ, ɢɫɯɨɞɹ ɢɡ ɫɨɨɛɪɚɠɟɧɢɣ ɩɨɥɟɡî0 x £ 0 ɧɨɫɬɢ ɩɪɢ ɤɨɧɫɬɪɭɢɪɨɜɚɧɢɢ ɨɛɭɱɚɟɦɵɯ ɫɟɬɟɣ. Ɉɫɧɨɜɧɵɦ ɦɟɬɨɞɨɦ ɨɛɭɱɟɧɢɹ ɫɟɬɟɣ ɫ ɩɨɪɨɝɨɜɵɦɢ ɷɥɟɦɟɧɬɚɦɢ ɹɜɥɹɟɬɫɹ ɩɪɚɜɢɥɨ ɜ ɛ sign x ɏɟɛɛɚ (ɩɨɞɪɨɛɧɨ ɪɚɫɫɦɨɬɪɟɧɨ ɜɨ ɜɬɨɪɨɣ ɱɚɫɬɢ ɝɥɚɜɵ). Ɉɧɨ m m m ɫɨɫɬɨɢɬ ɢɡ ɞɜɭɯ ɩɪɨɰɟɞɭɪ, ɫɨɫɬɨɹɳɢɯ ɜ ɢɡɦɟɧɟɧɢɢ «ɜɟɫɨɜ ɫɜɹɡɟɣ ɦɟɠɞɭ ɨɞɧɨɜɪɟɦɟɧɧɨ ɚɤɬɢɜɧɵɦɢ ɧɟɣɪɨɧɚɦɢ». Ⱦɥɹ ɷɬɨɝɨ ɩɪɚɜɢɥɚ ɩɨɪɨɝɨɜɵɣ ɷɥɟɦɟɧɬ ɩɪɢ ɨɛɪɚɬɧɨɦ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɢ Ɋɢɫ. 11. ɉɪɹɦɨɟ (ɚ) ɢ ɨɛɪɚɬɧɨɟ (ɛ,ɜ) ɞɨɥɠɟɧ ɜɵɞɚɜɚɬɶ ɫɢɝɧɚɥ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ, ɫɨɜɩɚɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɟ ɩɨɪɨɝɨɜɨɝɨ ɞɚɸɳɢɣ ɫ ɜɵɞɚɧɧɵɦ ɢɦ ɫɢɝɧɚɥɨɦ ɩɪɹɦɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ ɷɥɟɦɟɧɬɚ. (ɪɢɫ. 11ɛ). Ɍɚɤɨɣ ɩɨɪɨɝɨɜɵɣ ɷɥɟɦɟɧɬ ɛɭɞɟɦ ɧɚɡɵɜɚɬɶ ɡɟɪɤɚɥɶɛ) “Ɂɟɪɤɚɥɶɧɵɣ” ɩɨɪɨɝɨɜɵɣ ɷɥɟɦɟɧɬ ɧɵɦ. ɉɪɢ ɨɛɭɱɟɧɢɢ ɫɟɬɟɣ ɏɨɩɮɢɥɞɚ, ɩɨɞɪɨɛɧɨ ɪɚɫɫɦɨɬɪɟɧɧɨɦ ɜ) “ɉɪɨɡɪɚɱɧɵɣ” ɩɨɪɨɝɨɜɵɣ ɷɥɟɦɟɧɬ
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ɜɨ ɜɬɨɪɨɣ ɱɚɫɬɢ ɝɥɚɜɵ, ɧɟɨɛɯɨɞɢɦɨ ɢɫɩɨɥɶɡɨɜɚɬɶ «ɩɪɨɡɪɚɱɧɵɟ» ɩɨɪɨɝɨɜɵɟ ɷɥɟɦɟɧɬɵ, ɤɨɬɨɪɵɟ ɩɪɢ ɨɛɪɚɬɧɨɦ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɢ ɩɪɨɩɭɫɤɚɸɬ ɫɢɝɧɚɥ ɛɟɡ ɢɡɦɟɧɟɧɢɹ (ɪɢɫ. 11ɜ).
5.1.4 ɉɪɚɜɢɥɚ ɨɫɬɚɧɨɜɤɢ ɪɚɛɨɬɵ ɫɟɬɢ ɉɪɢ ɢɫɩɨɥɶɡɨɜɚɧɢɢ ɫɟɬɟɣ ɩɪɹɦɨɝɨ ɪɚɫɩɪɨɫɬɪɚɧɟɧɢɹ (ɫɟɬɟɣ ɛɟɡ ɰɢɤɥɨɜ) ɜɨɩɪɨɫɚ ɨɛ ɨɫɬɚɧɨɜɤɟ ɫɟɬɢ ɧɟ ɜɨɡɧɢɤɚɟɬ. Ⱦɟɣɫɬɜɢɬɟɥɶɧɨ, ɫɢɝɧɚɥɵ ɩɨɫɬɭɩɚɸɬ ɧɚ ɷɥɟɦɟɧɬɵ ɩɟɪɜɨɝɨ (ɜɯɨɞɧɨɝɨ) ɫɥɨɹ ɢ, ɩɪɨɯɨɞɹ ɩɨ ɫɜɹɡɹɦ, ɞɨɯɨɞɹɬ ɞɨ ɷɥɟɦɟɧɬɨɜ ɩɨɫɥɟɞɧɟɝɨ ɫɥɨɹ. ɉɨɫɥɟ ɫɧɹɬɢɹ ɫɢɝɧɚɥɨɜ ɫ ɩɨɫɥɟɞɧɟɝɨ ɫɥɨɹ ɜɫɟ ɷɥɟɦɟɧɬɵ ɫɟɬɢ ɨɤɚɡɵɜɚɸɬɫɹ «ɨɛɟɫɬɨɱɟɧɧɵɦɢ», ɬɨ ɟɫɬɶ ɧɢ ɩɨ ɨɞɧɨɣ ɫɜɹɡɢ ɫɟɬɢ ɧɟ ɩɪɨɯɨɞɢɬ ɧɢ ɨɞɧɨɝɨ ɧɟɧɭɥɟɜɨɝɨ ɫɢɝɧɚɥɚ. ɋɥɨɠɧɟɟ ɨɛɫɬɨɢɬ ɞɟɥɨ ɩɪɢ ɢɫɩɨɥɶɡɨɜɚɧɢɢ ɫɟɬɟɣ ɫ ɰɢɤɥɚɦɢ. ȼ ɫɥɭɱɚɟ ɨɛɳɟɝɨ ɩɨɥɨɠɟɧɢɹ, ɩɨɫɥɟ ɩɨɞɚɱɢ ɫɢɝɧɚɥɨɜ ɧɚ ɜɯɨɞɧɵɟ ɷɥɟɦɟɧɬɵ ɫɟɬɢ ɩɨ ɫɜɹɡɹɦ ɦɟɠɞɭ ɷɥɟɦɟɧɬɚɦɢ, ɜɯɨɞɹɳɢɦɢ ɜ ɰɢɤɥ, ɧɟɧɭɥɟɜɵɟ ɫɢɝɧɚɥɵ ɛɭɞɭɬ ɰɢɪɤɭɥɢɪɨɜɚɬɶ ɫɤɨɥɶ ɭɝɨɞɧɨ ɞɨɥɝɨ. ɋɭɳɟɫɬɜɭɟɬ ɞɜɚ ɨɫɧɨɜɧɵɯ ɩɪɚɜɢɥɚ ɨɫɬɚɧɨɜɤɢ ɪɚɛɨɬɵ ɫɟɬɢ ɫ ɰɢɤɥɚɦɢ. ɉɟɪɜɨɟ ɩɪɚɜɢɥɨ ɫɨɫɬɨɢɬ ɜ ɨɫɬɚɧɨɜɤɟ ɪɚɛɨɬɵ ɫɟɬɢ ɩɨɫɥɟ ɭɤɚɡɚɧɧɨɝɨ ɱɢɫɥɚ ɫɪɚɛɚɬɵɜɚɧɢɣ ɤɚɠɞɨɝɨ ɷɥɟɦɟɧɬɚ. ɐɢɤɥɵ ɫ ɬɚɤɢɦ ɩɪɚɜɢɥɨɦ ɨɫɬɚɧɨɜɤɢ ɛɭɞɟɦ ɧɚɡɵɜɚɬɶ ɨɝɪɚɧɢɱɟɧɧɵɦɢ. ȼɬɨɪɨɟ ɩɪɚɜɢɥɨ ɨɫɬɚɧɨɜɤɢ ɪɚɛɨɬɵ ɫɟɬɢ – ɫɟɬɶ ɩɪɟɤɪɚɳɚɟɬ ɪɚɛɨɬɭ ɩɨɫɥɟ ɭɫɬɚɧɨɜɥɟɧɢɹ ɪɚɜɧɨɜɟɫɧɨɝɨ ɪɚɫɩɪɟɞɟɥɟɧɢɹ ɫɢɝɧɚɥɨɜ ɜ ɰɢɤɥɟ. Ɍɚɤɢɟ ɫɟɬɢ ɛɭɞɟɦ ɧɚɡɵɜɚɬɶ ɪɚɜɧɨɜɟɫɧɵɦɢ. ɉɪɢɦɟɪɨɦ ɪɚɜɧɨɜɟɫɧɨɣ ɫɟɬɢ ɦɨɠɟɬ ɫɥɭɠɢɬɶ ɫɟɬɶ ɏɨɩɮɢɥɞɚ (ɫɦ. ɪɚɡɞ. "ɋɟɬɢ ɏɨɩɮɢɥɞɚ").
5.1.5 Ⱥɪɯɢɬɟɤɬɭɪɵ ɫɟɬɟɣ Ʉɚɤ ɭɠɟ ɨɬɦɟɱɚɥɨɫɶ ɪɚɧɟɟ, ɩɪɢ ɤɨɧɫɬɪɭɢɪɨɜɚɧɢɢ ɫɟɬɟɣ ɢɡ a ɷɥɟɦɟɧɬɨɜ ɦɨɠɧɨ ɩɨɫɬɪɨɢɬɶ ɫɟɬɶ ɥɸɛɨɣ ɚɪɯɢɬɟɤɬɭɪɵ. Ɉɞɧɚɤɨ ɢ ɩɪɢ x1 x2 ɩɪɨɢɡɜɨɥɶɧɨɦ ɤɨɧɫɬɪɭɢɪɨɜɚɧɢɢ ɦɨɠɧɨ ɜɵɞɟɥɢɬɶ ɧɚɢɛɨɥɟɟ ɨɛɳɢɟ s ɩɪɢɡɧɚɤɢ, ɫɭɳɟɫɬɜɟɧɧɨ ɨɬɥɢɱɚɸɳɢɟ ɨɞɧɭ ɫɟɬɶ ɨɬ ɞɪɭɝɨɣ. Ɉɱɟɜɢɞɧɨ, ɱɬɨ ɡɚɦɟɧɚ ɩɪɨɫɬɨɝɨ ɫɭɦɦɚɬɨɪɚ ɧɚ ɚɞɚɩɬɢɜɧɵɣ ɢɥɢ ɞɚɠɟ ɧɚ ɤɜɚɞɪɚɬɢɱɧɵɣ ɧɟ ɩɪɢɜɟɞɭɬ ɤ ɫɭɳɟɫɬɜɟɧɧɨɦɭ ɢɡɦɟɧɟɧɢɸ ɫɬɪɭɤɬɭɪɵ ɫɟɬɢ, Ɋɢɫ. 12. Ɏɪɚɝɦɟɧɬ ɯɨɬɹ ɱɢɫɥɨ ɨɛɭɱɚɟɦɵɯ ɩɚɪɚɦɟɬɪɨɜ ɭɜɟɥɢɱɢɬɫɹ. Ɉɞɧɚɤɨ, ɜɜɟɞɟɧɢɟ ɜ ɧɟɦɨɧɨɬɨɧɧɨɣ ɫɟɬɢ ɫɟɬɶ ɰɢɤɥɚ ɫɢɥɶɧɨ ɢɡɦɟɧɹɟɬ ɤɚɤ ɫɬɪɭɤɬɭɪɭ ɫɟɬɢ, ɬɚɤ ɢ ɟɟ ɩɨɜɟɞɟɧɢɟ. Ɍɚɤɢɦ ɨɛɪɚɡɨɦ ɦɨɠɧɨ ɜɫɟ ɫɟɬɢ ɪɚɡɛɢɬɶ ɧɚ ɞɜɚ ɫɢɥɶɧɨ ɨɬɥɢɱɚɸɳɢɯɫɹ ɤɥɚɫɫɚ: ɚɰɢɤɥɢɱɟɫɤɢɟ ɫɟɬɢ ɢ ɫɟɬɢ ɫ ɰɢɤɥɚɦɢ. ɋɪɟɞɢ ȼ ɫɟɬɟɣ ɫ ɰɢɤɥɚɦɢ ɫɭɳɟɫɬɜɭɟɬ ɟɳɟ ɨɞɧɨ ɪɚɡɞɟɥɟɧɢɟ, ɫɢɥɶȼ ɧɨ ɜɥɢɹɸɳɟɟ ɧɚ ɫɩɨɫɨɛ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ ɫɟɬɢ: ɪɚɜɧɨɜɟɫɧɵɟ ɫɟɬɢ ɫ ɰɢɤɥɚɦɢ ɢ ɫɟɬɢ ɫ ɨɝɪɚɧɢɱɟɧɧɵɦɢ ɰɢɤɌ Ɍ ɥɚɦɢ. Ȼɨɥɶɲɢɧɫɬɜɨ ɢɫɩɨɥɶɡɭɟɦɵɯ ɫɟɬɟɣ ɧɟ ɩɨɡɜɨɥɹɸɬ Ɍ ɨɩɪɟɞɟɥɢɬɶ, ɤɚɤ ɩɨɜɥɢɹɟɬ ɢɡɦɟɧɟɧɢɟ ɤɚɤɨɝɨ-ɥɢɛɨ ɜɧɭɬȼ ɪɟɧɧɟɝɨ ɩɚɪɚɦɟɬɪɚ ɫɟɬɢ ɧɚ ɜɵɯɨɞɧɨɣ ɫɢɝɧɚɥ. ɇɚ ɪɢɫ. 12 ɩɪɢɜɟɞɟɧ ɩɪɢɦɟɪ ɫɟɬɢ, ɜ ɤɨɬɨɪɨɣ ɭɜɟɥɢɱɟɧɢɟ ɩɚɪɚɦɟɬɪɚ Ɋɢɫ. 13. Ɉɛɳɚɹ ɫɯɟɦɚ ɦɨɧɨɬɨɧɧɨɣ ɫɟɬɢ. a ɩɪɢɜɨɞɢɬ ɤ ɧɟɨɞɧɨɡɧɚɱɧɨɦɭ ɜɥɢɹɧɢɸ ɧɚ ɫɢɝɧɚɥ x 2 : ȼɟɪɯɧɢɣ ɪɹɞ - ɜɨɡɛ ɭɠɞɚɸɳɢɟ ɛɥɨɤɢ ɧɟɣɪɨɧɨɜ , ɧɢɠɧɢɣ ɪɹɞ - ɬɨɪɦɨɡɹɳɢɟ . Ȼɭɤɩɪɢ ɨɬɪɢɰɚɬɟɥɶɧɵɯ x1 ɩɪɨɢɡɨɣɞɟɬ ɭɦɟɧɶɲɟɧɢɟ x 2 , ɚ ɜɨɣ “Ɍ” - ɩɨɦɟɱɟɧɵ ɬɨɪɦɨɡɹɳɢɟ ɫɜɹɡɢ, ɩɪɢ ɩɨɥɨɠɢɬɟɥɶɧɵɯ x1 – ɛɭɤɜɨɣ “ȼ” - ɜɨɡɛ ɭɠɞɚɸɳɢɟ . ɭɜɟɥɢɱɟɧɢɟ. Ɍɚɤɢɦ ɨɛɪɚA A ɡɨɦ, ɜɵɯɨɞɧɨɣ ɫɢɝɧɚɥ p p ɬɚɤɨɣ ɫɟɬɢ ɧɟɦɨɧɨɬɨɧɧɨ ɡɚɜɢɫɢɬ ɨɬ ɩɚɪɚɦɟɬɪɚ a . A+ A+ Ⱦɥɹ ɩɨɥɭɱɟɧɢɹ ɦɨɧɨɬɨɧɧɨɣ ɡɚɜɢɫɢɦɨɫɬɢ ɜɵɯɨɞA A ɧɵɯ ɫɢɝɧɚɥɨɜ ɫɟɬɢ ɨɬ p p ɩɚɪɚɦɟɬɪɨɜ ɜɧɭɬɪɟɧɧɢɯ ɫɥɨɟɜ (ɬɨ ɟɫɬɶ ɜɫɟɯ ɫɥɨɟɜ A+ A+ A ɤɪɨɦɟ ɜɯɨɞɧɨɝɨ) ɧɟɨɛɯɨp ɞɢɦɨ ɢɫɩɨɥɶɡɨɜɚɬɶ ɫɩɟɰɢA+ ɚɥɶɧɭɸ ɦɨɧɨɬɨɧɧɭɸ ɚɪA A p p ɯɢɬɟɤɬɭɪɭ ɧɟɣɪɨɧɧɨɣ ɫɟɬɢ. ɉɪɢɧɰɢɩɢɚɥɶɧɚɹ A+ A+ ɫɯɟɦɚ ɫɟɬɟɣ ɦɨɧɨɬɨɧɧɨɣ ɚɪɯɢɬɟɤɬɭɪɵ ɩɪɢɜɟɞɟɧɚ ɧɚ ɪɢɫ. 13. A A
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79 Ɋɢɫ. 14. ɇɟɦɨɧɨɬɨɧɧɚɹ ɫɟɬɶ ɫ ɉɚɞɟ ɷɥɟɦɟɧɬɚɦɢ
Ɉɫɧɨɜɧɚɹ ɢɞɟɹ ɩɨɫɬɪɨɟɧɢɹ ɦɨɧɨɬɨɧɧɵɯ ɫɟɬɟɣ ɫɨɫɬɨɢɬ ɜ ɪɚɡɞɟɥɟɧɢɢ ɤɚɠɞɨɝɨ ɫɥɨɹ ɫɟɬɢ ɧɚ ɞɜɚ – ɜɨɡɛɭɠɞɚɸɳɢɣ ɢ ɬɨɪɦɨɡɹɳɢɣ. ɉɪɢ ɷɬɨɦ ɜɫɟ ɫɜɹɡɢ ɜ ɫɟɬɢ ɭɫɬɪɨɟɧɵ ɬɚɤ, ɱɬɨ ɷɥɟɦɟɧɬɵ ɜɨɡɛɭɠɞɚɸɳɟɣ ɱɚɫɬɢ ɫɥɨɹ ɜɨɡɛɭɠɞɚɸɬ ɷɥɟɦɟɧɬɵ ɜɨɡɛɭɠɞɚɸɳɟɣ ɱɚɫɬɢ ɫɥɟɞɭɸɳɟɝɨ ɫɥɨɹ ɢ ɬɨɪɦɨɡɹɬ ɬɨɪɦɨɡɹɳɢɟ ɷɥɟɦɟɧɬɵ ɫɥɟɞɭɸɳɟɝɨ ɫɥɨɹ. Ⱥɧɚɥɨɝɢɱɧɨ, ɬɨɪɦɨɡɹɳɢɟ ɷɥɟɦɟɧɬɵ ɜɨɡɛɭɠɞɚɸɬ ɬɨɪɦɨɡɹɳɢɟ ɷɥɟɦɟɧɬɵ ɢ ɬɨɪɦɨɡɹɬ ɜɨɡɛɭɠɞɚɸɳɢɟ ɷɥɟɦɟɧɬɵ ɫɥɟɞɭɸɳɟɝɨ ɫɥɨɹ. ɇɚɡɜɚɧɢɹ «ɬɨɪɦɨɡɹɳɢɣ» ɢ «ɜɨɡɛɭɠɞɚɸɳɢɣ» ɨɬɧɨɫɹɬɫɹ ɤ ɜɥɢɹɧɢɸ ɷɥɟɦɟɧɬɨɜ ɨɛɟɢɯ ɱɚɫɬɟɣ ɧɚ ɜɵɯɨɞɧɵɟ ɷɥɟɦɟɧɬɵ. Ɉɬɦɟɬɢɦ, ɱɬɨ ɞɥɹ ɫɟɬɟɣ ɫ p p ɫɢɝɦɨɢɞɧɵɦɢ ɷɥɟɦɟɧɬɚɦɢ ɬɪɟɛɨɜɚɧɢɟ ɦɨɧɨɬɨɧɧɨɫɬɢ ɨɡɧɚɱɚɟɬ, + + ɱɬɨ ɜɟɫɚ ɜɫɟɯ ɫɜɹɡɟɣ ɞɨɥɠɧɵ ȼ ɛɵɬɶ ɧɟɨɬɪɢɰɚɬɟɥɶɧɵ. Ⱦɥɹ ɫɟɬɟɣ ɫ ɉɚɞɟ ɷɥɟɦɟɧɬɚɦɢ ɬɪɟɛɨɜɚɧɢɟ ɧɟ p p ɨɬɪɢɰɚɬɟɥɶɧɨɫɬɢ ɜɟɫɨɜ ɫɜɹɡɟɣ ɹɜɥɹɟɬɫɹ ɧɟɨɛɯɨɞɢɦɵɦ ɭɫɥɨɜɢɟɦ + + ɛɟɫɫɛɨɣɧɨɣ ɪɚɛɨɬɵ. Ɍɪɟɛɨɜɚɧɢɟ p ɦɨɧɨɬɨɧɧɨɫɬɢ ɞɥɹ ɫɟɬɟɣ ɫ ɉɚɞɟ ɷɥɟɦɟɧɬɚɦɢ ɩɪɢɜɨɞɢɬ ɤ ɢɡɦɟɧɟ+ p p ɧɢɸ ɚɪɯɢɬɟɤɬɭɪɵ ɫɟɬɢ, ɧɟ ɧɚɤɥɚɞɵɜɚɹ ɧɢɤɚɤɢɯ ɧɨɜɵɯ ɨɝɪɚɧɢɱɟ+ + ɧɢɣ ɧɚ ɩɚɪɚɦɟɬɪɵ ɫɟɬɢ. ɇɚ ɪɢɫ. Ɍ 14 ɩɪɢɜɟɞɟɧɵ ɩɪɢɦɟɪ ɧɟɦɨɧɨɬɨɧɧɨɣ ɫɟɬɢ, ɚ ɧɚ ɪɢɫ. 15 ɦɨɧɨp p ɬɨɧɧɨɣ ɫɟɬɢ ɫ ɉɚɞɟ ɷɥɟɦɟɧɬɚɦɢ. Ɉɫɨɛɨ ɨɬɦɟɬɢɦ ɚɪɯɢɬɟɤ+ + ɬɭɪɭ ɟɳɟ ɨɞɧɨɝɨ ɤɥɚɫɫɚ ɫɟɬɟɣ – ɫɟɬɟɣ ɛɟɡ ɜɟɫɨɜ ɫɜɹɡɟɣ. ɗɬɢ ɫɟɬɢ, Ɋɢɫ. 15. Ɇɨɧɨɬɨɧɧɚɹ ɫɟɬɶ ɫ ɉɚɞɟ ɷɥɟɦɟɧɬɚɦɢ . ɀɢɪɧɵɦɢ ɜ ɩɪɨɬɢɜɨɜɟɫ ɤɨɧɧɟɤɰɢɨɧɢɫɬɥɢɧɢɹɦɢ ɨɛɨɡɧɚɱɟɧɵ ɜɨɡɛ ɭɠɞɚɸɳɢɟ ɫɜɹɡɢ ɢ ɷɥɟɦɟɧɬɵ ɫɤɢɦ, ɧɟ ɢɦɟɸɬ ɨɛɭɱɚɟɦɵɯ ɩɚɪɚɜɨɡɛ ɭɠɞɚɸɳɟɣ ɱɚɫɬɢ ɫɟɬɢ ɦɟɬɪɨɜ ɫɜɹɡɟɣ. Ʌɸɛɭɸ ɫɟɬɶ ɦɨɠɧɨ ɩɪɟɜɪɚɬɢɬɶ ɜ ɫɟɬɶ ɛɟɡ ɜɟɫɨɜ ɫɜɹɡɟɣ ɡɚɦɟɧɨɣ ɜɫɟɯ ɫɢɧɚɩɫɨɜ ɧɚ ɭɦɧɨɠɢɬɟɥɢ. Ʌɟɝɤɨ ɡɚɦɟɬɢɬɶ, ɱɬɨ ɩɨɥɭɱɢɬɫɹ ɬɚɤɚɹ ɠɟ ɫɟɬɶ, ɬɨɥɶɤɨ ɜɦɟɫɬɨ ɜɟɫɨɜ ɫɜɹɡɟɣ ɛɭɞɭɬ ɢɫɩɨɥɶɡɨɜɚɬɶɫɹ ɫɢɝɧɚɥɵ. Ɍɚɤɢɦ ɨɛɪɚɡɨɦ ɜ ɫɟɬɹɯ ɛɟɡ ɜɟɫɨɜ ɫɜɹɡɟɣ ɜɵɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɨɞɧɨɝɨ ɫɥɨɹ ɦɨɝɭɬ ɫɥɭɠɢɬɶ ɞɥɹ ɫɥɟɞɭɸɳɟɝɨ ɫɥɨɹ ɤɚɤ ɜɯɨɞɧɵɦɢ ɫɢɝɧɚɥɚɦɢ, ɬɚɤ ɢ ɜɟɫɚɦɢ ɫɜɹɡɟɣ. Ɂɚɦɟɬɢɦ, ɱɬɨ ɜɫɹ ɩɚɦɹɬɶ ɬɚɤɢɯ ɫɟɬɟɣ ɫɨɞɟɪɠɢɬɫɹ ɜ ɡɧɚɱɟɧɢɹɯ ɩɚɪɚɦɟɬɪɨɜ ɧɟɥɢɧɟɣɧɵɯ ɩɪɟɨɛɪɚɡɨɜɚɬɟɥɟɣ. ɂɡ ɪɚɡɞɟɥɨɜ "ɋɢɧɚɩɫ" ɢ "ɍɦɧɨɠɢɬɟɥɶ" ɫɥɟɞɭɟɬ, ɱɬɨ ɫɟɬɢ ɛɟɡ ɜɟɫɨɜ ɫɜɹɡɟɣ ɫɩɨɫɨɛɧɵ ɜɵɱɢɫɥɹɬɶ ɝɪɚɞɢɟɧɬ ɮɭɧɤɰɢɢ ɨɰɟɧɤɢ ɢ ɡɚɬɪɚɱɢɜɚɸɬ ɧɚ ɷɬɨ ɪɨɜɧɨ ɬɨɠɟ ɜɪɟɦɹ, ɱɬɨ ɢ ɚɧɚɥɨɝɢɱɧɚɹ ɫɟɬɶ ɫ ɜɟɫɚɦɢ ɫɜɹɡɟɣ.
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5.1.6 Ɇɨɞɢɮɢɤɚɰɢɹ ɫɢɧɚɩɬɢɱɟɫɤɨɣ ɤɚɪɬɵ (ɨɛɭɱɟɧɢɟ) Ʉɪɨɦɟ ɩɪɹɦɨɝɨ ɢ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ, ɜɫɟ ɷɥɟɦɟɧɬɵ ɞɨɥɠɧɵ ɭɦɟɬɶ ɜɵɩɨɥɧɹɬɶ ɟɳɟ ɨɞɧɭ ɨɩɟɪɚɰɢɸ – ɦɨɞɢɮɢɤɚɰɢɸ ɩɚɪɚɦɟɬɪɨɜ. ɉɪɨɰɟɞɭɪɚ ɦɨɞɢɮɢɤɚɰɢɢ ɩɚɪɚɦɟɬɪɨɜ ɫɨɫɬɨɢɬ ɜ ɞɨɛɚɜɥɟɧɢɢ ɤ ɫɭɳɟɫɬɜɭɸɳɢɦ ɩɚɪɚɦɟɬɪɚɦ ɜɵɱɢɫɥɟɧɧɵɯ ɩɨɩɪɚɜɨɤ (ɧɚɩɨɦɧɢɦ, ɱɬɨ ɞɥɹ ɫɟɬɟɣ ɫ ɧɟɩɪɟɪɵɜɧɨ ɞɢɮɮɟɪɟɧɰɢɪɭɟɦɵɦɢ ɷɥɟɦɟɧɬɚɦɢ ɜɟɤɬɨɪ ɩɨɩɪɚɜɨɤ ɹɜɥɹɟɬɫɹ ɝɪɚɞɢɟɧɬɨɦ ɧɟɤɨɬɨɪɨɣ ɮɭɧɤɰɢɢ ɨɬ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ). ȿɫɥɢ ɨɛɨɡɧɚɱɢɬɶ ɬɟɤɭɳɢɣ ɩɚɪɚɦɟɬɪ ɷɥɟɦɟɧɬɚ ɱɟɪɟɡ a , ɚ ɜɵɱɢɫɥɟɧɧɭɸ ɩɨɩɪɚɜɤɭ ɱɟɪɟɡ Da , ɬɨ
ɧɨɜɨɟ ɡɧɚɱɟɧɢɟ ɩɚɪɚɦɟɬɪɚ ɜɵɱɢɫɥɹɟɬɫɹ ɩɨ ɮɨɪɦɭɥɟ a ¢ = h1a + h2 Da . ɉɚɪɚɦɟɬɪɵ ɨɛɭɱɟɧɢɹ h1 ɢ h2 ɨɩɪɟɞɟɥɹɸɬɫɹ ɤɨɦɩɨɧɟɧɬɨɦ ɭɱɢɬɟɥɶ ɢ ɩɟɪɟɞɚɸɬɫɹ ɫɟɬɢ ɜɦɟɫɬɟ ɫ ɡɚɩɪɨɫɨɦ ɧɚ ɨɛɭɱɟɧɢɟ. ȼ ɧɟɤɨɬɨɪɵɯ ɫɥɭɱɚɹɯ ɛɵɜɚɟɬ ɩɨɥɟɡɧɨ ɢɫɩɨɥɶɡɨɜɚɬɶ ɛɨɥɟɟ ɫɥɨɠɧɭɸ ɩɪɨɰɟɞɭɪɭ ɦɨɞɢɮɢɤɚɰɢɢ ɤɚɪɬɵ. ȼɨ ɦɧɨɝɢɯ ɪɚɛɨɬɚɯ ɨɬɦɟɱɚɟɬɫɹ, ɱɬɨ ɩɪɢ ɨɩɢɫɚɧɧɨɣ ɜɵɲɟ ɩɪɨɰɟɞɭɪɟ ɦɨɞɢɮɢɤɚɰɢɢ ɩɚɪɚɦɟɬɪɨɜ ɩɪɨɢɫɯɨɞɢɬ ɧɟɨɝɪɚɧɢɱɟɧɧɵɣ ɪɨɫɬ ɜɟɥɢɱɢɧ ɩɚɪɚɦɟɬɪɨɜ. ɋɭɳɟɫɬɜɭɟɬ ɧɟɫɤɨɥɶɤɨ ɪɚɡɥɢɱɧɵɯ ɦɟɬɨɞɨɜ ɪɟɲɟɧɢɹ ɷɬɨɣ ɩɪɨɛɥɟɦɵ. ɇɚɢɛɨɥɟɟ ɩɪɨɫɬɵɦ ɹɜɥɹɟɬɫɹ ɠɟɫɬɤɨɟ ɨɝɪɚɧɢɱɟɧɢɟ ɜɟɥɢɱɢɧ ɩɚɪɚɦɟɬɪɨɜ ɧɟɤɨɬɨɪɵɦɢ ɦɢɧɢɦɚɥɶɧɵɦ ɢ ɦɚɤɫɢɦɚɥɶɧɵɦ ɡɧɚɱɟɧɢɹɦɢ. ɉɪɢ ɢɫɩɨɥɶɡɨɜɚɧɢɢ ɷɬɨɝɨ ɦɟɬɨɞɚ ɩɪɨɰɟɞɭɪɚ ɦɨɞɢɮɢɤɚɰɢɢ ɩɚɪɚɦɟɬɪɨɜ ɢɦɟɟɬ ɫɥɟɞɭɸɳɢɣ ɜɢɞ:
ì a min , h1a + h2 Da < a min , ï a ¢ = íh1a + h2 Da , a min £ h1a + h2 Da < a max , ï a , a max < h1a + h2 Da . max î
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5.1.7 Ʉɨɧɬɪɚɫɬɢɪɨɜɚɧɢɟ ɢ ɧɨɪɦɚɥɢɡɚɰɢɹ ɫɟɬɢ ȼ ɩɨɫɥɟɞɧɢɟ ɝɨɞɵ ɲɢɪɨɤɨɟ ɪɚɫɩɪɨɫɬɪɚɧɟɧɢɟ ɩɨɥɭɱɢɥɢ ɪɚɡɥɢɱɧɵɟ ɦɟɬɨɞɵ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɹ ɢɥɢ ɫɤɟɥɟɬɨɧɢɡɚɰɢɢ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ. ȼ ɯɨɞɟ ɩɪɨɰɟɞɭɪɵ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɹ ɞɨɫɬɢɝɚɟɬɫɹ ɜɵɫɨɤɚɹ ɫɬɟɩɟɧɶ ɪɚɡɪɟɠɟɧɧɨɫɬɢ ɫɢɧɚɩɬɢɱɟɫɤɨɣ ɤɚɪɬɵ ɧɟɣɪɨɧɧɨɣ ɫɟɬɢ, ɬɚɤ ɤɚɤ ɛɨɥɶɲɢɧɫɬɜɨ ɫɜɹɡɟɣ ɩɨɥɭɱɚɸɬ ɧɭɥɟɜɵɟ ɜɟɫɚ (ɫɦ. ɧɚɩɪɢɦɟɪ [47, 99, 302. 303]). Ɉɱɟɜɢɞɧɨ, ɱɬɨ ɩɪɢ ɬɚɤɨɣ ɫɬɟɩɟɧɢ ɪɚɡɪɟɠɟɧɧɨɫɬɢ ɧɟɧɭɥɟɜɵɯ ɩɚɪɚɦɟɬɪɨɜ ɩɪɨɜɨɞɢɬɶ ɜɵɱɢɫɥɟɧɢɹ ɬɚɤ, ɤɚɤ ɛɭɞɬɨ ɫɬɪɭɤɬɭɪɚ ɫɟɬɢ ɧɟ ɢɡɦɟɧɢɥɚɫɶ, ɧɟɷɮɮɟɤɬɢɜɧɨ. ȼɨɡɧɢɤɚɟɬ ɩɨɬɪɟɛɧɨɫɬɶ ɜ ɩɪɨɰɟɞɭɪɟ ɧɨɪɦɚɥɢɡɚɰɢɢ ɫɟɬɢ, ɬɨ ɟɫɬɶ ɮɚɤɬɢɱɟɫɤɨɝɨ ɭɞɚɥɟɧɢɹ ɧɭɥɟɜɵɯ ɫɜɹɡɟɣ ɢɡ ɫɟɬɢ, ɚ ɧɟ ɬɨɥɶɤɨ ɢɡ ɨɛɭɱɟɧɢɹ. ɉɪɨɰɟɞɭɪɚ ɧɨɪɦɚɥɢɡɚɰɢɢ ɫɨɫɬɨɢɬ ɢɡ ɞɜɭɯ ɷɬɚɩɨɜ: 1. ɂɡ ɫɟɬɢ ɭɞɚɥɹɸɬɫɹ ɜɫɟ ɫɜɹɡɢ ɢɦɟɸɳɢɟ ɧɭɥɟɜɵɟ ɜɟɫɚ ɢ ɢɫɤɥɸɱɟɧɧɵɟ ɢɡ ɨɛɭɱɟɧɢɹ. 2. ɂɡ ɫɟɬɢ ɭɞɚɥɹɸɬɫɹ ɜɫɟ ɩɨɞɫɟɬɢ, ɜɵɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɤɨɬɨɪɵɯ ɧɟ ɢɫɩɨɥɶɡɭɸɬɫɹ ɞɪɭɝɢɦɢ ɩɨɞɫɟɬɹɦɢ ɜ ɤɚɱɟɫɬɜɟ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɢ ɧɟ ɹɜɥɹɸɬɫɹ ɜɵɯɨɞɧɵɦɢ ɫɢɝɧɚɥɚɦɢ ɫɟɬɢ ɜ ɰɟɥɨɦ. ȼ ɯɨɞɟ ɧɨɪɦɚɥɢɡɚɰɢɢ ɜɨɡɧɢɤɚɟɬ ɨɞɧɚ ɬɪɭɞɧɨɫɬɶ: ɟɫɥɢ ɩɪɢ ɨɩɢɫɚɧɢɢ ɧɟɣɪɨɧɧɨɣ ɫɟɬɢ ɜɫɟ ɧɟɣɪɨɧɵ ɨɞɢɧɚɤɨɜɵ, ɢ ɦɨɠɧɨ ɨɩɢɫɚɬɶ ɧɟɣɪɨɧ ɨɞɢɧ ɪɚɡ, ɬɨ ɩɨɫɥɟ ɭɞɚɥɟɧɢɹ ɨɬɤɨɧɬɪɚɫɬɢɪɨɜɚɧɧɵɯ ɫɜɹɡɟɣ ɧɟɣɪɨɧɵ ɨɛɵɱɧɨ ɢɦɟɸɬ ɪɚɡɥɢɱɧɭɸ ɫɬɪɭɤɬɭɪɭ. Ʉɨɦɩɨɧɟɧɬ ɫɟɬɶ ɞɨɥɠɟɧ ɨɬɫɥɟɠɢɜɚɬɶ ɫɢɬɭɚɰɢɢ, ɤɨɝɞɚ ɞɜɚ ɛɥɨɤɚ ɢɫɯɨɞɧɨ ɨɞɧɨɝɨ ɢ ɬɨɝɨ ɠɟ ɬɢɩɚ ɭɠɟ ɧɟ ɦɨɝɭɬ ɛɵɬɶ ɩɪɟɞɫɬɚɜɥɟɧɵ ɜ ɜɢɞɟ ɷɬɨɝɨ ɛɥɨɤɚ ɫ ɪɚɡɥɢɱɧɵɦɢ ɩɚɪɚɦɟɬɪɚɦɢ. ȼ ɷɬɢɯ ɫɥɭɱɚɹɯ ɤɨɦɩɨɧɟɧɬ ɫɟɬɶ ɩɨɪɨɠɞɚɟɬ ɧɨɜɵɣ ɬɢɩ ɛɥɨɤɚ. ɉɪɚɜɢɥɚ ɩɨɪɨɠɞɟɧɢɹ ɢɦɟɧ ɛɥɨɤɨɜ ɩɪɢɜɟɞɟɧɵ ɜ ɨɩɢɫɚɧɢɢ ɜɵɩɨɥɧɟɧɢɹ ɡɚɩɪɨɫɚ ɧɚ ɧɨɪɦɚɥɢɡɚɰɢɸ ɫɟɬɢ.
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5.2 ɉɪɢɦɟɪɵ ɫɟɬɟɣ ɢ ɚɥɝɨɪɢɬɦɨɜ ɢɯ ɨɛɭɱɟɧɢɹ ȼ ɷɬɨɦ ɪɚɡɞɟɥɟ ɧɚɦɟɪɟɧɧɨ ɞɨɩɭɳɟɧɨ ɨɬɫɬɭɩɥɟɧɢɟ ɨɬ ɨɛɳɟɣ ɦɟɬɨɞɢɤɢ – ɧɟ ɫɦɟɲɢɜɚɬɶ ɪɚɡɧɵɟ ɤɨɦɩɨɧɟɧɬɵ. ɗɬɨ ɫɞɟɥɚɧɨ ɞɥɹ ɨɛɥɟɝɱɟɧɢɹ ɞɟɦɨɧɫɬɪɚɰɢɢ ɩɨɫɬɪɨɟɧɢɹ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ɨɛɪɚɬɧɨɝɨ ɪɚɫɩɪɨɫɬɪɚɧɟɧɢɹ, ɩɨɡɜɨɥɹɸɳɢɯ ɪɟɚɥɢɡɨɜɚɬɶ ɧɚ ɧɢɯ ɛɨɥɶɲɢɧɫɬɜɨ ɢɡɜɟɫɬɧɵɯ ɚɥɝɨɪɢɬɦɨɜ ɨɛɭɱɟɧɢɹ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ.
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5.2.1 ɋɟɬɢ ɏɨɩɮɢɥɞɚ
Ʉɥɚɫɫɢɱɟɫɤɚɹ ɫɟɬɶ ɏɨɩɮɢɥɞɚ, ɮɭɧɤɰɢɨɧɢɪɭɸɳɚɹ ɜ ɞɢɫɤɪɟɬɧɨɦ ɜɪɟɦɟɧɢ, ɫɬɪɨɢɬɫɹ ɫɥɟɞɭɸɳɢɦ i ɨɛɪɚɡɨɦ. ɉɭɫɬɶ e – ɧɚɛɨɪ ɷɬɚɥɨɧɧɵɯ ɨɛɪɚɡɨɜ i = 1,K, m . Ʉɚɠɞɵɣ ɨɛɪɚɡ, ɜɤɥɸɱɚɹ ɢ ɷɬɚɥɨɧɵ, ɢɦɟ-
(
)
ɟɬ ɜɢɞ n-ɦɟɪɧɨɝɨ ɜɟɤɬɨɪɚ ɫ ɤɨɨɪɞɢɧɚɬɚɦɢ, ɪɚɜɧɵɦɢ ɧɭɥɸ ɢɥɢ ɟɞɢɧɢɰɟ. ɉɪɢ ɩɪɟɞɴɹɜɥɟɧɢɢ ɧɚ ɜɯɨɞ ɫɟɬɢ ɨɛɪɚɡɚ x ɫɟɬɶ ɜɵɱɢɫɥɹɟɬ ɨɛɪɚɡ, ɧɚɢɛɨɥɟɟ ɩɨɯɨɠɢɣ ɧɚ x. ȼ ɤɚɱɟɫɬɜɟ ɦɟɪɵ ɛɥɢɡɨɫɬɢ ɨɛɪɚɡɨɜ ɜɵɛɟɪɟɦ ɫɤɚɥɹɪɧɨɟ ɩɪɨɢɡɜɟɞɟɧɢɟ ɫɨɨɬɜɟɬɫɬɜɭɸɳɢɯ ɜɟɤɬɨɪɨɜ. ȼɵɱɢɫɥɟɧɢɹ ɩɪɨɜɨɞɹɬɫɹ ɩɨ ɫɥɟɞɭɸɳɟɣ ɮɨɪɦɭɥɟ: æ m ö x ¢ = sign çç x, e i e i ÷÷ . ɗɬɚ ɩɪɨɰɟɞɭɪɚ ɜɵɩɨɥɧɹɟɬɫɹ ɞɨ ɬɟɯ ɩɨɪ, ɩɨɤɚ ɩɨɫɥɟ ɨɱɟɪɟɞɧɨɣ ɢɬɟɪɚɰɢɢ ɧɟ è i=1 ø ɨɤɚɠɟɬɫɹ, ɱɬɨ x = x ¢ . ȼɟɤɬɨɪ x, ɩɨɥɭɱɟɧɧɵɣ ɜ ɯɨɞɟ ɩɨɫɥɟɞɧɟɣ ɢɬɟɪɚɰɢɢ, ɫɱɢɬɚɟɬɫɹ ɨɬɜɟɬɨɦ. Ⱦɥɹ ɧɟɣɪɨɫɟɬɟɜɨɣ ɪɟɚɥɢɡɚɰɢɢ ɮɨɪɦɭɥɚ ɪɚɛɨɬɵ ɫɟɬɢ ɩɟɪɟɩɢɫɵɜɚɟɬɫɹ ɜ ɫɥɟɞɭɸɳɟɦ ɜɢɞɟ:
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m ö æ n ö æ n ö æ m n ö æ m x ¢j = signç å ( x, e i )eij ÷ = signç å å xk eik eij ÷ = signç å xk å eik eij ÷ = signç å a jk xk ÷ ø è i =1 ø è k =1 ø è k =1 i =1 ø è i =1 k =1
ɢɥɢ
a jk = a kj = å eij eki .
x ¢ = sign (Ax ),
m
ɝɞɟ
i =1
ɇɚ ɪɢɫ. 16 ɩɪɢɜɟɞɟɧɚ ɫɯɟɦɚ ɫɟɬɢ ɏɨɩɮɢɥɞɚ ɞɥɹ ɪɚɫɩɨɡɧɚɜɚɧɢɹ ɱɟɬɵɪɟɯɦɟɪɧɵɯ ɨɛɪɚɡɨɜ. Ɉɛɵɱɧɨ ɫɟɬɢ ɏɨɩɮɢɥɞɚ ɨɬɧɨɫɹɬ ɤ ɫɟɬɹɦ ɫ ɮɨɪɦɢɪɭɟɦɨɣ ɫɢɧɚɩɬɢɱɟɫɤɨɣ ɤɚɪɬɨɣ. Ɉɞɧɚɤɨ, ɢɫɩɨɥɶɡɭɹ ɪɚɡɪɚɛɨɬɚɧɧɵɣ ɜ ɩɟɪɜɨɣ ɱɚɫɬɢ ɝɥɚɜɵ ɧɚɛɨɪ ɷɥɟɦɟɧɬɨɜ, e1k a a i1e1k ɦɨɠɧɨ ɩɨɫɬɪɨɢɬɶ ɨɛɭ4 i1 ɱɚɟɦɭɸ ɫɟɬɶ. Ⱦɥɹ ɩɨk k s = a ij ekj e a e 2 a i2 i2 2 ɫɬɪɨɟɧɢɹ ɬɚɤɨɣ ɫɟɬɢ j = 1 ɢɫɩɨɥɶɡɭɟɦ «ɩɪɨɡɪɚɱe3k a a i3e3k sign s ɧɵɟ» ɩɨɪɨɝɨɜɵɟ ɷɥɟɦɟɧi3 ɬɵ. ɇɢɠɟ ɩɪɢɜɟɞɟɧ ɚɥɝɨe4k a a i4 e4k ɪɢɬɦ ɨɛɭɱɟɧɢɹ ɫɟɬɢ i4 ɏɨɩɮɢɥɞɚ. ɛ) ɉɪɹɦɨɟ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɟ i-ɝɨ ɧɟɣɪɨɧɚ 1. ɉɨɥɨɠɢɦ ɜɫɟ ɫɟɬɢ ɏɨɩɮɢɥɞɚ ɫɢɧɚɩɬɢɱɟɫɤɢɟ ɜɟɫɚ ɪɚɜɧɵɦɢ ɧɭɥɸ. k k e e 1 i 2. ɉɪɟɞɴɹɜɢɦ eik a i1 1 ɫɟɬɢ ɩɟɪɜɵɣ ɷɬɚɥɨɧ e ɢ ɩɪɨɜɟɞɟɦ ɨɞɢɧ ɬɚɤɬ e2k eik ɚ) ɋɟɬɶ ɏɨɩɮɢɥɞɚ ɞɥɹ ɢɡɨeik ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ ɜɩɟa i2 ɛɪɚɠɟɧɢɣ ɢɡ ɱɟɬɵɪɟɯ ɬɨɱɟɤ. ɪɟɞ, ɬɨ ɟɫɬɶ ɰɢɤɥ ɛɭɞɟɬ eik eik ɪɚɛɨɬɚɬɶ ɧɟ ɞɨ ɪɚɜɧɨɜɟe3k eik k ei ɫɢɹ, ɚ ɨɞɢɧ ɪɚɡ (ɫɦ. ɪɢɫ. a i3 16ɛ). 3. ɉɨɞɚɞɢɦ ɧɚ e4k eik ɜɵɯɨɞ ɤɚɠɞɨɝɨ ɧɟɣɪɨɧɚ eik a i4 ɫɨɨɬɜɟɬɫɬɜɭɸɳɭɸ ɤɨɨɪɊɢɫ. 16. ɋɟɬɶ ɏɨɩɮɢɥɞɚ. 1 ɞɢɧɚɬɭ ɜɟɤɬɨɪɚ e (ɫɦ. ɜ) Ɉɛɪɚɬɧɨɟ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɟ i-ɝɨ ɧɟɣɉɪɹɦɨɟ ɢ ɨɛɪɚɬɧɨɟ ɮɭɧɤɪɢɫ. 16ɜ). ɉɨɩɪɚɜɤɚ, ɰɢɨɧɢɪɨɜɚɧɢɟ ɧɟɣɪɨɧɨɜ ɫɟɬɢ ɪɨɧɚ ɫɟɬɢ ɏɨɩɮɢɥɞɚ. ɇɚɞ ɫɢɧɚɩɫɚɦɢ ɨɬɨɛɪɚɠɟɧɵ ɜɵɱɢɫɥɟɧɧɵɟ ɩɨɩɪɚɜɤɢ ɏɨɩɮɢɥɞɚ.
S
å
S
S
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S S
S
CHAP5-2.DOC
82
ɜɵɱɢɫɥɟɧɧɚɹ ɧɚ j-ɨɦ ɫɢɧɚɩɫɟ i-ɝɨ ɧɟɣɪɨɧɚ ɪɚɜɧɚ ɩɪɨɢɡɜɟɞɟɧɢɸ ɫɢɝɧɚɥɚ ɩɪɹɦɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ ɧɚ ɫɢɝɧɚɥ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ. ɉɨɫɤɨɥɶɤɭ ɩɪɢ ɨɛɪɚɬɧɨɦ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɢ ɩɨɪɨɝɨɜɵɣ ɷɥɟɦɟɧɬ 11 ɩɪɨɡɪɚɱɟɧ, ɚ ɫɭɦɦɚɬɨɪ ɩɟɪɟɯɨɞɢɬ ɜ ɬɨɱɤɭ ɜɟɬɜɥɟɧɢɹ, ɬɨ ɩɨɩɪɚɜɤɚ ɪɚɜɧɚ ei e j . 4. Ⱦɚɥɟɟ ɩɪɨɜɟɞɟɦ ɲɚɝ ɨɛɭɱɟɧɢɹ ɫ ɩɚɪɚɦɟɬɪɚɦɢ ɨɛɭɱɟɧɢɹ, ɪɚɜɧɵɦɢ ɟɞɢɧɢɰɟ. ȼ ɪɟɡɭɥɶɬɚɬɟ ɩɨɥɭ11 ɱɢɦ a ij = ei e j .
åe e m
ɉɨɜɬɨɪɹɹ ɷɬɨɬ ɚɥɝɨɪɢɬɦ, ɧɚɱɢɧɚɹ ɫɨ ɜɬɨɪɨɝɨ ɲɚɝɚ, ɞɥɹ ɜɫɟɯ ɷɬɚɥɨɧɨɜ ɩɨɥɭɱɢɦ aij =
k i
k j
,
i =1
ɱɬɨ ɩɨɥɧɨɫɬɶɸ ɫɨɜɩɚɞɚɟɬ ɫ ɮɨɪɦɭɥɨɣ ɮɨɪɦɢɪɨɜɚɧɢɹ ɫɢɧɚɩɬɢɱɟɫɤɨɣ ɤɚɪɬɵ ɫɟɬɢ ɏɨɩɮɢɥɞɚ, ɩɪɢɜɟɞɟɧɧɨɣ ɜ ɧɚɱɚɥɟ ɪɚɡɞɟɥɚ.
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5.2.2 ɋɟɬɶ Ʉɨɯɨɧɟɧɚ ɋɟɬɢ Ʉɨɯɨɧɟɧɚ [98, 99] (ɱɚɫɬɧɵɣ ɫɥɭɱɚɣ ɦɟɬɨɞɚ ɞɢɧɚɦɢɱɟɫɤɢɯ ɹɞɟɪ [223, 261]) ɹɜɥɹɸɬɫɹ ɬɢɩɢɱɧɵɦ ɩɪɟɞɫɬɚɜɢɬɟɥɟɦ ɫɟɬɟɣ ɪɟɲɚɸɳɢɯ ɡɚɞɚɱɭ ɤɥɚɫɫɢɮɢɤɚɰɢɢ ɛɟɡ ɭɱɢɬɟɥɹ. Ɋɚɫɫɦɨɬɪɢɦ ɩɪɨɫɬɪɚɧɫɬɜɟɧɧɵɣ ɜɚɪɢp ɚɧɬ ɫɟɬɢ Ʉɨɯɨɧɟɧɚ. Ⱦɚɧ ɧɚɛɨɪ ɢɡ m ɬɨɱɟɤ x ɜ n-
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ɦɟɪɧɨɦ ɩɪɨɫɬɪɚɧɫɬɜɟ. ɇɟɨɛɯɨɞɢɦɨ ɪɚɡɛɢɬɶ ɦɧɨɠɟɫɬɜɨ p ɧɚ k ɤɥɚɫɫɨɜ ɛɥɢɡɤɢɯ ɜ ɫɦɵɫɥɟ ɤɜɚɞɪɚɬɚ ɬɨɱɟɤ x ɟɜɤɥɢɞɨɜɚ ɪɚɫɫɬɨɹɧɢɹ. Ⱦɥɹ ɷɬɨɝɨ ɧɟɨɛɯɨɞɢɦɨ ɧɚɣɬɢ k k l ɬɨɱɟɤ a ɬɚɤɢɯ, ɱɬɨ D = a l - x , ɦɢɧɢl =1 x ÎPl
åå
ɦɚɥɶɧɨ;
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Pl = x: a l - x < a q - x , "q ¹ l .
ɋɭɳɟɫɬɜɭɟɬ ɦɧɨɠɟɫɬɜɨ ɪɚɡɥɢɱɧɵɯ ɚɥɝɨɪɢɬɦɨɜ ɪɟɲɟɧɢɹ ɷɬɨɣ ɡɚɞɚɱɢ. Ɋɚɫɫɦɨɬɪɢɦ ɧɚɢɛɨɥɟɟ ɷɮɮɟɤɬɢɜɧɵɣ ɢɡ ɧɢɯ. 1. Ɂɚɞɚɞɢɦɫɹ ɧɟɤɨɬɨɪɵɦ ɧɚɛɨɪɨɦ ɧɚɱɚɥɶɧɵɯ l ɬɨɱɟɤ a . p ɧɚ k ɤɥɚɫ2. Ɋɚɡɨɛɶɟɦ ɦɧɨɠɟɫɬɜɨ ɬɨɱɟɤ x ɫɨɜ ɩɨ ɩɪɚɜɢɥɭ Pl
{
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}
= x : a - x < a q - x , "l ¹ q . l
3. ɉɨ ɩɨɥɭɱɟɧɧɨɦɭ ɪɚɡɛɢɟɧɢɸ ɜɵɱɢɫɥɢɦ ɧɨɜɵɟ al ɢɡ ɭɫɥɨɜɢɹ ɦɢɧɢɦɚɥɶɧɨɫɬɢ l Dl = a -x . x ÎPl
ɬɨɱɤɢ
å
a1i
a1i - x1
x1
a in
a in - x n
1 åx . Pi x ÎP i
...
xn
(a
(a *
i 1 - x1
2
)
i n - xn
)
2
S
ɚ) ɇɟɣɪɨɧ ɫɟɬɢ Ʉɨɯɨɧɟɧɚ, ɜɵɱɢɫɥɹɸɳɢɣ ɤɜɚɞɪɚɬ ɟɜɤɥɢɞɨɜɚ ɪɚɫɫɬɨɹɧɢɹ
x1
-2a1i
-2a1i x1
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-2a in x n xn -2a in ai 1 i
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a i - 2 a i, x
S
a
ɛ) ɉɪɹɦɨɟ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɟ i-ɝɨ ɧɟɣɪɨɧɚ ɫɟɬɢ Ʉɨɯɨɧɟɧɚ
D i x1
-2a1i
Di
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Di x n -2a in
Ɉɛɨɡɧɚɱɢɜ ɱɟɪɟɡ Pi ɱɢɫɥɨ ɬɨɱɟɤ ɜ i-ɨɦ ɤɥɚɫɫɟ, ɪɟɲɟɧɢɟ ɡɚɞɚɱɢ, ɩɨɫɬɚɜɥɟɧɧɨɣ ɧɚ ɬɪɟɬɶɟɦ ɲɚɝɟ ɚɥɝɨ-
i ɪɢɬɦɚ, ɦɨɠɧɨ ɡɚɩɢɫɚɬɶ ɜ ɜɢɞɟ a =
*
Di ai
Di
S
Di
Di
ɜ) Ɉɛɪɚɬɧɨɟ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɟ i-ɝɨ ɧɟɣɪɨȼɬɨɪɨɣ ɢ ɬɪɟɬɢɣ ɲɚɝɢ ɚɥɝɨɪɢɬɦɚ ɛɭɞɟɦ ɩɨɜɬɨɧɚ ɫɟɬɢ Ʉɨɯɨɧɟɧɚ. ɇɚɞ ɫɢɧɚɩɫɚɦɢ ɨɬɦɟɱɟɧɵ l ɪɹɬɶ ɞɨ ɬɟɯ ɩɨɪ, ɩɨɤɚ ɧɚɛɨɪ ɬɨɱɟɤ a ɧɟ ɩɟɪɟɫɬɚɧɟɬ ɜɵɱɢɫɥɟɧɧɵɟ ɧɚ ɩɨɫɥɟɞɧɟɦ ɲɚɝɟ ɩɨɩɪɚɜɤɢ. ɢɡɦɟɧɹɬɶɫɹ. ɉɨɫɥɟ ɨɤɨɧɱɚɧɢɹ ɨɛɭɱɟɧɢɹ ɩɨɥɭɱɚɟɦ ɧɟɣɊɢɫ. 17. ɋɟɬɶ Ʉɨɯɨɧɟɧɚ. ɉɪɹɦɨɟ ɢ ɨɛɪɚɬɧɨɟ ɪɨɧɧɭɸ ɫɟɬɶ, ɫɩɨɫɨɛɧɭɸ ɞɥɹ ɩɪɨɢɡɜɨɥɶɧɨɣ ɬɨɱɤɢ x ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɟ ɧɟɣɪɨɧɨɜ ɫɟɬɢ Ʉɨɯɨɧɟɧɚ. ɜɵɱɢɫɥɢɬɶ ɤɜɚɞɪɚɬɵ ɟɜɤɥɢɞɨɜɵɯ ɪɚɫɫɬɨɹɧɢɣ ɨɬ ɷɬɨɣ l ɬɨɱɤɢ ɞɨ ɜɫɟɯ ɬɨɱɟɤ a ɢ, ɬɟɦ ɫɚɦɵɦ, ɨɬɧɟɫɬɢ ɟɟ ɤ ɨɞɧɨɦɭ ɢɡ k ɤɥɚɫɫɨɜ. Ɉɬɜɟɬɨɦ ɹɜɥɹɟɬɫɹ ɧɨɦɟɪ ɧɟɣɪɨɧɚ, ɜɵɞɚɜɲɟɝɨ ɦɢɧɢɦɚɥɶɧɵɣ ɫɢɝɧɚɥ. Ɍɟɩɟɪɶ ɪɚɫɫɦɨɬɪɢɦ ɫɟɬɟɜɭɸ ɪɟɚɥɢɡɚɰɢɸ. ȼɨ ɩɟɪɜɵɯ, ɜɵɱɢɫɥɟɧɢɟ ɤɜɚɞɪɚɬɚ ɟɜɤɥɢɞɨɜɚ ɪɚɫɫɬɨɹɧɢɹ ɞɨɫɬɚɬɨɱɧɨ ɫɥɨɠɧɨ ɪɟɚɥɢɡɨɜɚɬɶ ɜ ɜɢɞɟ ɫɟɬɢ (ɪɢɫ. 17ɚ). Ɉɞɧɚɤɨ ɡɚɦɟɬɢɦ, ɱɬɨ ɧɟɬ ɧɟɨɛɯɨɞɢɦɨɫɬɢ ɜɵɱɢɫɥɹɬɶ ɤɜɚɞɪɚɬ ɪɚɫɫɬɨɹɧɢɹ ɩɨɥɧɨɫɬɶɸ. Ⱦɟɣɫɬɜɢɬɟɥɶɧɨ, a l - x = a l - x ,a l - x = a l - 2 a l , x + x .
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Ɉɬɦɟɬɢɦ, ɱɬɨ ɜ ɩɨɫɥɟɞɧɟɣ ɮɨɪɦɭɥɟ ɩɟɪɜɨɟ ɫɥɚɝɚɟɦɨɟ ɧɟ ɡɚɜɢɫɢɬ ɨɬ ɬɨɱɤɢ x, ɜɬɨɪɨɟ ɜɵɱɢɫɥɹɟɬɫɹ ɚɞɚɩɬɢɜɧɵɦ ɫɭɦɦɚɬɨɪɨɦ, ɚ ɬɪɟɬɶɟ ɨɞɢɧɚɤɨɜɨ ɞɥɹ ɜɫɟɯ ɫɪɚɜɧɢɜɚɟɦɵɯ ɜɟɥɢɱɢɧ. Ɍɚɤɢɦ ɨɛɪɚɡɨɦ, ɥɟɝɤɨ ɩɨɥɭɱɢɬɶ ɧɟɣɪɨɧɧɭɸ ɫɟɬɶ, ɤɨɬɨɪɚɹ ɜɵɱɢɫɥɢɬ ɞɥɹ ɤɚɠɞɨɝɨ ɤɥɚɫɫɚ ɬɨɥɶɤɨ ɩɟɪɜɵɟ ɞɜɚ ɫɥɚɝɚɟɦɵɯ (ɪɢɫ. 17ɛ). ȼɬɨɪɨɟ ɫɨɨɛɪɚɠɟɧɢɟ, ɩɨɡɜɨɥɹɸɳɟɟ ɭɩɪɨɫɬɢɬɶ ɨɛɭɱɟɧɢɟ ɫɟɬɢ, ɫɨɫɬɨɢɬ ɜ ɨɬɤɚɡɟ ɨɬ ɪɚɡɞɟɥɟɧɢɹ ɜɬɨɪɨɝɨ ɢ ɬɪɟɬɶɟɝɨ ɲɚɝɨɜ ɚɥɝɨɪɢɬɦɚ.
CHAP5-2.DOC
84
Ⱥɥɝɨɪɢɬɦ ɤɥɚɫɫɢɮɢɤɚɰɢɢ. 1. ɇɚ ɜɯɨɞ ɧɟɣɪɨɧɧɨɣ ɫɟɬɢ, ɫɨɫɬɨɹɳɟɣ ɢɡ ɨɞɧɨɝɨ ɫɥɨɹ ɧɟɣɪɨɧɨɜ, ɩɪɢɜɟɞɟɧɧɵɯ ɧɚ ɪɢɫ. 17ɛ, ɩɨɞɚɟɬɫɹ ɜɟɤɬɨɪ x. 2. ɇɨɦɟɪ ɧɟɣɪɨɧɚ, ɜɵɞɚɜɲɟɝɨ ɦɢɧɢɦɚɥɶɧɵɣ ɨɬɜɟɬ, ɹɜɥɹɟɬɫɹ ɧɨɦɟɪɨɦ ɤɥɚɫɫɚ, ɤ ɤɨɬɨɪɨɦɭ ɩɪɢɧɚɞɥɟɠɢɬ ɜɟɤɬɨɪ x. Ⱥɥɝɨɪɢɬɦ ɨɛɭɱɟɧɢɹ. 1. ɉɨɥɚɝɚɟɦ ɩɨɩɪɚɜɤɢ ɜɫɟɯ ɫɢɧɚɩɫɨɜ ɪɚɜɧɵɦɢ ɧɭɥɸ. p 2. Ⱦɥɹ ɤɚɠɞɨɣ ɬɨɱɤɢ ɦɧɨɠɟɫɬɜɚ x ɜɵɩɨɥɧɹɟɦ ɫɥɟɞɭɸɳɭɸ ɩɪɨɰɟɞɭɪɭ. 2.1. 2.2.
{ }
ɉɪɟɞɴɹɜɥɹɟɦ ɬɨɱɤɭ ɫɟɬɢ ɞɥɹ ɤɥɚɫɫɢɮɢɤɚɰɢɢ. ɉɭɫɬɶ ɩɪɢ ɤɥɚɫɫɢɮɢɤɚɰɢɢ ɩɨɥɭɱɟɧ ɨɬɜɟɬ – ɤɥɚɫɫ l. Ɍɨɝɞɚ ɞɥɹ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ ɫɟɬɢ ɩɨɞɚɟɬɫɹ ɜɟɤɬɨɪ D , ɤɨɨɪɞɢɧɚɬɵ ɤɨɬɨɪɨɝɨ ɨɩɪɟɞɟɥɹɸɬɫɹ ɩɨ ɫɥɟɞɭɸɳɟɦɭ ɩɪɚɜɢɥɭ: D i
ì0, i ¹ l =í . î1, i = l
2.3. ȼɵɱɢɫɥɟɧɧɵɟ ɞɥɹ ɞɚɧɧɨɣ ɬɨɱɤɢ ɩɨɩɪɚɜɤɢ ɞɨɛɚɜɥɹɸɬɫɹ ɤ ɪɚɧɟɟ ɜɵɱɢɫɥɟɧɧɵɦ. Ⱦɥɹ ɤɚɠɞɨɝɨ ɧɟɣɪɨɧɚ ɩɪɨɢɡɜɨɞɢɦ ɫɥɟɞɭɸɳɭɸ ɩɪɨɰɟɞɭɪɭ. 3.1. ȿɫɥɢ ɩɨɩɪɚɜɤɚ, ɜɵɱɢɫɥɟɧɧɚɹ ɩɨɫɥɟɞɧɢɦ ɫɢɧɚɩɫɨɦ ɪɚɜɧɚ 0, ɬɨ ɧɟɣɪɨɧ ɭɞɚɥɹɟɬɫɹ ɢɡ ɫɟɬɢ. 3.2. ɉɨɥɚɝɚɟɦ ɩɚɪɚɦɟɬɪ ɨɛɭɱɟɧɢɹ ɪɚɜɧɵɦ ɜɟɥɢɱɢɧɟ, ɨɛɪɚɬɧɨɣ ɤ ɩɨɩɪɚɜɤɟ, ɜɵɱɢɫɥɟɧɧɨɣ ɩɨɫɥɟɞɧɢɦ ɫɢɧɚɩɫɨɦ. 3.3. ȼɵɱɢɫɥɹɟɦ ɫɭɦɦɭ ɤɜɚɞɪɚɬɨɜ ɧɚɤɨɩɥɟɧɧɵɯ ɜ ɩɟɪɜɵɯ n ɫɢɧɚɩɫɚɯ ɩɨɩɪɚɜɨɤ ɢ, ɪɚɡɞɟɥɢɜ ɧɚ -2, ɡɚɧɨɫɢɦ ɜ ɩɨɩɪɚɜɤɭ ɩɨɫɥɟɞɧɟɝɨ ɫɢɧɚɩɫɚ. 3.4. ɉɪɨɜɨɞɢɦ ɲɚɝ ɨɛɭɱɟɧɢɹ ɫ ɩɚɪɚɦɟɬɪɚɦɢ h1 = 0 , h2 = -2 . 4. ȿɫɥɢ ɜɧɨɜɶ ɜɵɱɢɫɥɟɧɧɵɟ ɫɢɧɚɩɬɢɱɟɫɤɢɟ ɜɟɫɚ ɨɬɥɢɱɚɸɬɫɹ ɨɬ ɩɨɥɭɱɟɧɧɵɯ ɧɚ ɩɪɟɞɵɞɭɳɟɦ ɲɚɝɟ, ɬɨ ɩɟɪɟɯɨɞɢɦ ɤ ɩɟɪɜɨɦɭ ɲɚɝɭ ɚɥɝɨɪɢɬɦɚ. ȼ ɩɨɹɫɧɟɧɢɢ ɧɭɠɞɚɟɬɫɹ ɬɨɥɶɤɨ ɜɬɨɪɨɣ ɢ ɬɪɟɬɢɣ ɲɚɝɢ ɚɥɝɨɪɢɬɦɚ. ɂɡ ɪɢɫ. 16ɜ ɜɢɞɧɨ, ɱɬɨ ɜɵɱɢɫɥɟɧɧɵɟ ɧɚ ɲɚɝɟ 2.2 ɚɥɝɨɪɢɬɦɚ ɩɨɩɪɚɜɤɢ ɛɭɞɭɬ ɪɚɜɧɵ ɧɭɥɸ ɞɥɹ ɜɫɟɯ ɧɟɣɪɨɧɨɜ, ɤɪɨɦɟ ɧɟɣɪɨɧɚ, ɜɵɞɚɜɲɟɝɨ ɦɢɧɢɦɚɥɶɧɵɣ ɫɢɝɧɚɥ. ɍ ɧɟɣɪɨɧɚ, ɜɵɞɚɜɲɟɝɨ ɦɢɧɢɦɚɥɶɧɵɣ ɫɢɝɧɚɥ, ɩɟɪɜɵɟ n ɩɨɩɪɚɜɨɤ ɛɭɞɭɬ ɪɚɜɧɵ ɤɨɨɪɞɢɧɚɬɚɦ ɪɚɫɩɨɡɧɚɜɚɜɲɟɣɫɹ ɬɨɱɤɢ x, ɚ ɩɨɩɪɚɜɤɚ ɩɨɫɥɟɞɧɟɝɨ ɫɢɧɚɩɫɚ ɪɚɜɧɚ ɟɞɢɧɢɰɟ. ɉɨɫɥɟ ɡɚɜɟɪɲɟɧɢɹ ɜɬɨɪɨɝɨ ɲɚɝɚ ɚɥɝɨɪɢɬɦɚ ɩɨɩɪɚɜɤɚ ɩɨɫɥɟɞɧɟɝɨ ɫɢɧɚɩɫɚ i-ɝɨ ɧɟɣɪɨɧɚ ɛɭɞɟɬ ɪɚɜɧɚ ɱɢɫɥɭ ɬɨɱɟɤ, ɨɬɧɟɫɟɧɧɵɯ ɤ i-ɦɭ ɤɥɚɫɫɭ, ɚ ɩɨɩɪɚɜɤɢ ɨɫɬɚɥɶɧɵɯ ɫɢɧɚɩɫɨɜ ɷɬɨɝɨ ɧɟɣɪɨɧɚ ɪɚɜɧɵ ɫɭɦɦɟ ɫɨɨɬɜɟɬɫɬɜɭɸɳɢɯ ɤɨɨɪɞɢɧɚɬ ɜɫɟɯ ɬɨɱɟɤ i-ɝɨ ɤɥɚɫɫɚ. Ⱦɥɹ ɩɨɥɭɱɟɧɢɹ ɩɪɚɜɢɥɶɧɵɯ ɜɟɫɨɜ ɨɫɬɚɟɬɫɹ ɬɨɥɶɤɨ ɪɚɡɞɟɥɢɬɶ ɜɫɟ ɩɨɩɪɚɜɤɢ ɩɟɪɜɵɯ n ɫɢɧɚɩɫɨɜ ɧɚ ɩɨɩɪɚɜɤɭ ɩɨɫɥɟɞɧɟɝɨ ɫɢɧɚɩɫɚ, ɩɨɥɨɠɢɬɶ ɩɨɫɥɟɞɧɢɣ ɫɢɧɚɩɫ ɪɚɜɧɵɦ ɫɭɦɦɟ ɤɜɚɞɪɚɬɨɜ ɩɨɥɭɱɟɧɧɵɯ ɜɟɥɢɱɢɧ, ɚ ɨɫɬɚɥɶɧɵɟ ɫɢɧɚɩɫɵ – ɩɨɥɭɱɟɧɧɵɦ ɞɥɹ ɧɢɯ ɩɨɩɪɚɜɤɚɦ, ɭɦɧɨɠɟɧɧɵɦ ɧɚ -2. ɂɦɟɧɧɨ ɷɬɨ ɢ ɩɪɨɢɫɯɨɞɢɬ ɩɪɢ ɜɵɩɨɥɧɟɧɢɢ ɬɪɟɬɶɟɝɨ ɲɚɝɚ ɚɥɝɨɪɢɬɦɚ.
3.
5.2.3 ɉɟɪɫɟɩɬɪɨɧ Ɋɨɡɟɧɛɥɚɬɬɚ ɉɟɪɫɟɩɬɪɨɧ Ɋɨɡɟɧɛɥɚɬɬɚ ɹɜɥɹɟɬɫɹ ɢɫɬɨɪɢɱɟɫɤɢ ɩɟɪɜɨɣ ɨɛɭɱɚɟɦɨɣ ɧɟɣɪɨɧɧɨɣ ɫɟɬɶɸ. ɋɭɳɟɫɬɜɭɟɬ ɧɟɫɤɨɥɶɤɨ ɜɟɪɫɢɣ ɩɟɪɫɟɩɬɪɨɧɚ. Ɋɚɫɫɦɨɬɪɢɦ ɤɥɚɫɫɢɱɟɫɤɢɣ ɩɟɪɫɟɩɬɪɨɧ – ɫɟɬɶ ɫ ɩɨɪɨɝɨɜɵɦɢ ɧɟɣɪɨɧɚɦɢ ɢ ɜɯɨɞɧɵɦɢ ɫɢɝɧɚɥɚɦɢ, ɪɚɜɧɵɦɢ ɧɭɥɸ ɢɥɢ ɟɞɢɧɢɰɟ. Ɉɩɢɪɚɹɫɶ ɧɚ ɪɟɡɭɥɶɬɚɬɵ, ɢɡɥɨɠɟɧɧɵɟ ɜ ɪɚɛɨɬɟ [145] ɦɨɠɧɨ ɜɜɟɫɬɢ ɫɥɟɞɭɸɳɢɟ ɨɝɪɚɧɢɱɟɧɢɹ ɧɚ ɫɬɪɭɤɬɭɪɭ ɫɟɬɢ.
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ȼɫɟ ɫɢɧɚɩɬɢɱɟɫɤɢɟ ɜɟɫɚ ɦɨɝɭɬ ɛɵɬɶ ɰɟ-1 ɥɵɦɢ ɱɢɫɥɚɦɢ. a1 2. Ɇɧɨɝɨɫɥɨɣɧɵɣ ɩɟɪɫɟɩɬɪɨɧ ɩɨ ɫɜɨɢɦ x1 a2 ɜɨɡɦɨɠɧɨɫɬɹɦ ɷɤɜɢɜɚɥɟɧɬɟɧ ɞɜɭɯɫɥɨɣɧɨɦɭ. ȼɫɟ ɧɟɣɪɨɧɵ ɢɦɟɸɬ ɫɢɧɚɩɫ, ɧɚ ɤɨa3 -1 ɬɨɪɵɣ ɩɨɞɚɟɬɫɹ ɩɨɫɬɨɹɧɧɵɣ ɟɞɢɧɢɱɧɵɣ x2 ɫɢɝɧɚɥ. ȼɟɫ ɷɬɨɝɨ ɫɢɧɚɩɫɚ ɞɚɥɟɟ ɛɭɞɟɦ a4 ɧɚɡɵɜɚɬɶ ɩɨɪɨɝɨɦ. Ʉɚɠɞɵɣ ɧɟɣɪɨɧ ɩɟɪ-1 a5 ɜɨɝɨ ɫɥɨɹ ɢɦɟɟɬ ɟɞɢɧɢɱɧɵɟ ɫɢɧɚɩɬɢɱɟx3 ɫɤɢɟ ɜɟɫɚ ɧɚ ɜɫɟɯ ɫɜɹɡɹɯ, ɜɟɞɭɳɢɯ ɨɬ a 61 ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ, ɢ ɟɝɨ ɩɨɪɨɝ ɪɚɜɟɧ ɱɢɫɥɭ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɫɭɦɦɚɬɨɪɚ, ɭɦɟɧɶa7 ɲɟɧɧɨɦɭ ɧɚ ɞɜɚ ɢ ɜɡɹɬɨɦɭ ɫɨ ɡɧɚɤɨɦ ɦɢ-2 ɧɭɫ. ɚ) ɉɨɥɧɵɣ ɩɟɪɫɟɩɪɨɧ Ɋɨɡɟɧɛɥɚɬɬɚ ɫ ɬɪɟɦɹ ɜɯɨɞɌɚɤɢɦ ɨɛɪɚɡɨɦ, ɦɨɠɧɨ ɨɝɪɚɧɢɱɢɬɶɫɹ ɪɚɫɫɦɨɬɪɟɧɢɟɦ ɬɨɥɶɤɨ ɞɜɭɯɫɥɨɣɧɵɯ ɩɟɪɫɟɩɬɪɨɧɨɜ ɫ ɧɵɦɢ ɫɢɝɧɚɥɚɦɢ. ɧɟ ɨɛɭɱɚɟɦɵɦ ɩɟɪɜɵɦ ɫɥɨɟɦ. Ɂɚɦɟɬɢɦ, ɱɬɨ ɞɥɹ ɩɨɫɬɪɨɟɧɢɹ ɩɨɥɧɨɝɨ ɩɟɪɜɨɝɨ ɫɥɨɹ ɩɪɢɲɥɨɫɶ ɛɵ a 1 x1 x1 a1 n ɢɫɩɨɥɶɡɨɜɚɬɶ 2 ɧɟɣɪɨɧɨɜ, ɝɞɟ n – ɱɢɫɥɨ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɩɟɪɫɟɩɬɪɨɧɚ. ɇɚ ɪɢɫ. 18ɚ ɩɪɢɜɟɞɟɧɚ ɫɯɟɦɚ ɩɨɥɧɨɝɨ ɩɟɪɫɟɩɬɪɨɧɚ ɞɥɹ ɬɪɟɯɦɟɪɧɨɝɨ ɜɟɤa nx n xn ɬɨɪɚ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ. ɉɨɫɤɨɥɶɤɭ ɩɨɫɬɪɨɟɧɢɟ an ɬɚɤɨɣ ɫɟɬɢ ɩɪɢ ɞɨɫɬɚɬɨɱɧɨ ɛɨɥɶɲɨɦ n ɧɟɜɨɡɦɨɠɧɨ, ɬɨ ɨɛɵɱɧɨ ɢɫɩɨɥɶɡɭɸɬ ɧɟɤɨɬɨɪɨɟ ɩɨɞɦɧɨɠɟɛ) ɉɪɹɦɨɟ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɟ ɜɬɨɪɨɝɨ ɫɥɨɹ ɩɟɪɫɬɜɨ ɧɟɣɪɨɧɨɜ ɩɟɪɜɨɝɨ ɫɥɨɹ. Ʉ ɫɨɠɚɥɟɧɢɸ, ɬɨɥɶɤɨ ɫɟɩɬɪɨɧɚ Ɋɨɡɟɧɛɥɚɬɬɚ. ɩɨɥɧɨɫɬɶɸ ɪɟɲɢɜ ɡɚɞɚɱɭ ɦɨɠɧɨ ɬɨɱɧɨ ɭɤɚɡɚɬɶ ɧɟɨɛɯɨɞɢɦɨɟ ɩɨɞɦɧɨɠɟɫɬɜɨ. Ɉɛɵɱɧɨ ɢɫɩɨɥɶɡɭɟDx1 ɦɨɟ ɩɨɞɦɧɨɠɟɫɬɜɨ ɜɵɛɢɪɚɟɬɫɹ ɢɫɫɥɟɞɨɜɚɬɟɥɟɦ ɢɡ x1 ɤɚɤɢɯ-ɬɨ ɫɨɞɟɪɠɚɬɟɥɶɧɵɯ ɫɨɨɛɪɚɠɟɧɢɣ ɢɥɢ ɫɥɭD a1 ɱɚɣɧɨ. D D Ʉɥɚɫɫɢɱɟɫɤɢɣ ɚɥɝɨɪɢɬɦ ɨɛɭɱɟɧɢɹ ɩɟɪɫɟɩɬɪɨɧɚ ɹɜɥɹɟɬɫɹ ɱɚɫɬɧɵɦ ɫɥɭɱɚɟɦ ɩɪɚɜɢɥɚ ɏɟɛɛɚ. Dx n ɉɨɫɤɨɥɶɤɭ ɜɟɫɚ ɫɜɹɡɟɣ ɩɟɪɜɨɝɨ ɫɥɨɹ ɩɟɪɫɟɩɬɪɨɧɚ xn D ɹɜɥɹɸɬɫɹ ɧɟ ɨɛɭɱɚɟɦɵɦɢ, ɜɟɫɚ ɧɟɣɪɨɧɚ ɜɬɨɪɨɝɨ an ɫɥɨɹ ɜ ɞɚɥɶɧɟɣɲɟɦ ɛɭɞɟɦ ɧɚɡɵɜɚɬɶ ɩɪɨɫɬɨ ɜɟɫɚɦɢ. Ȼɭɞɟɦ ɫɱɢɬɚɬɶ, ɱɬɨ ɩɪɢ ɩɪɟɞɴɹɜɥɟɧɢɢ ɩɪɢɦɟɜ) Ɉɛɪɚɬɧɨɟ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɟ ɜɬɨɪɨɝɨ ɪɚ ɩɟɪɜɨɝɨ ɤɥɚɫɫɚ ɩɟɪɫɟɩɬɪɨɧ ɞɨɥɠɟɧ ɜɵɞɚɬɶ ɧɚ ɫɥɨɹ ɩɟɪɫɟɩɬɪɨɧɚ Ɋɨɡɟɧɛɥɚɬɬɚ. ɜɵɯɨɞɟ ɧɭɥɟɜɨɣ ɫɢɝɧɚɥ, ɚ ɩɪɢ ɩɪɟɞɴɹɜɥɟɧɢɢ ɩɪɢɊɢɫ. 18. ɉɟɪɫɟɩɬɪɨɧ Ɋɨɡɟɧɛɥɚɬɬɚ. ɉɪɹɦɨɟ ɢ ɨɛɦɟɪɚ ɜɬɨɪɨɝɨ ɤɥɚɫɫɚ – ɟɞɢɧɢɱɧɵɣ. ɇɢɠɟ ɩɪɢɜɟɞɟɪɚɬɧɨɟ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɟ ɜɬɨɪɨɝɨ ɫɥɨɹ ɩɟɪɫɟɩɧɨ ɨɩɢɫɚɧɢɟ ɚɥɝɨɪɢɬɦɚ ɨɛɭɱɟɧɢɹ ɩɟɪɫɟɩɬɪɨɧɚ. ɬɪɨɧɚ Ɋɨɡɟɧɛɥɚɬɬɚ. 1. ɉɨɥɚɝɚɟɦ ɜɫɟ ɜɟɫɚ ɪɚɜɧɵɦɢ ɧɭɥɸ. 2. ɉɪɨɜɨɞɢɦ ɰɢɤɥ ɩɪɟɞɴɹɜɥɟɧɢɹ ɩɪɢɦɟɪɨɜ. Ⱦɥɹ ɤɚɠɞɨɝɨ ɩɪɢɦɟɪɚ ɜɵɩɨɥɧɹɟɬɫɹ ɫɥɟɞɭɸɳɚɹ ɩɪɨɰɟɞɭɪɚ. 2.1. ȿɫɥɢ ɫɟɬɶ ɜɵɞɚɥɚ ɩɪɚɜɢɥɶɧɵɣ ɨɬɜɟɬ, ɬɨ ɩɟɪɟɯɨɞɢɦ ɤ ɲɚɝɭ 2.4. 2.2. ȿɫɥɢ ɧɚ ɜɵɯɨɞɟ ɩɟɪɫɟɩɬɪɨɧɚ ɨɠɢɞɚɥɚɫɶ ɟɞɢɧɢɰɚ, ɚ ɛɵɥ ɩɨɥɭɱɟɧ ɧɨɥɶ, ɬɨ ɜɟɫɚ ɫɜɹɡɟɣ, ɩɨ ɤɨɬɨɪɵɦ ɩɪɨɲɟɥ ɟɞɢɧɢɱɧɵɣ ɫɢɝɧɚɥ, ɭɦɟɧɶɲɚɟɦ ɧɚ ɟɞɢɧɢɰɭ. 2.3. ȿɫɥɢ ɧɚ ɜɵɯɨɞɟ ɩɟɪɫɟɩɬɪɨɧɚ ɨɠɢɞɚɥɫɹ ɧɨɥɶ, ɚ ɛɵɥɚ ɩɨɥɭɱɟɧɚ ɟɞɢɧɢɰɚ, ɬɨ ɜɟɫɚ ɫɜɹɡɟɣ, ɩɨ ɤɨɬɨɪɵɦ ɩɪɨɲɟɥ ɟɞɢɧɢɱɧɵɣ ɫɢɝɧɚɥ, ɭɜɟɥɢɱɢɜɚɟɦ ɧɚ ɟɞɢɧɢɰɭ. 2.4. ɉɟɪɟɯɨɞɢɦ ɤ ɫɥɟɞɭɸɳɟɦɭ ɩɪɢɦɟɪɭ. ȿɫɥɢ ɞɨɫɬɢɝɧɭɬ ɤɨɧɟɰ ɨɛɭɱɚɸɳɟɝɨ ɦɧɨɠɟɫɬɜɚ, ɬɨ ɩɟɪɟɯɨɞɢɦ ɤ ɲɚɝɭ 3, ɢɧɚɱɟ ɜɨɡɜɪɚɳɚɟɦɫɹ ɧɚ ɲɚɝ 2.1. 3. ȿɫɥɢ ɜ ɯɨɞɟ ɜɵɩɨɥɧɟɧɢɹ ɜɬɨɪɨɝɨ ɲɚɝɚ ɚɥɝɨɪɢɬɦɚ ɯɨɬɶ ɨɞɢɧ ɪɚɡ ɜɵɩɨɥɧɹɥɫɹ ɲɚɝ 2.2 ɢɥɢ 2.3 ɢ ɧɟ ɩɪɨɢɡɨɲɥɨ ɡɚɰɢɤɥɢɜɚɧɢɹ, ɬɨ ɩɟɪɟɯɨɞɢɦ ɤ ɲɚɝɭ 2. ȼ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ ɨɛɭɱɟɧɢɟ ɡɚɜɟɪɲɟɧɨ. ȼ ɷɬɨɦ ɚɥɝɨɪɢɬɦɟ ɧɟ ɩɪɟɞɭɫɦɨɬɪɟɧ ɦɟɯɚɧɢɡɦ ɨɬɫɥɟɠɢɜɚɧɢɹ ɡɚɰɢɤɥɢɜɚɧɢɹ ɨɛɭɱɟɧɢɹ. ɗɬɨɬ ɦɟɯɚɧɢɡɦ ɦɨɠɧɨ ɪɟɚɥɢɡɨɜɵɜɚɬɶ ɩɨ ɪɚɡɧɨɦɭ. ɇɚɢɛɨɥɟɟ ɷɤɨɧɨɦɧɵɣ ɜ ɫɦɵɫɥɟ ɢɫɩɨɥɶɡɨɜɚɧɢɹ ɞɨɩɨɥɧɢɬɟɥɶɧɨɣ ɩɚɦɹɬɢ ɢɦɟɟɬ ɫɥɟɞɭɸɳɢɣ ɜɢɞ. 1. k=1; m=0. Ɂɚɩɨɦɢɧɚɟɦ ɜɟɫɚ ɫɜɹɡɟɣ. 1.
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ɉɨɫɥɟ ɰɢɤɥɚ ɩɪɟɞɴɹɜɥɟɧɢɣ ɨɛɪɚɡɨɜ ɫɪɚɜɧɢɜɚɟɦ ɜɟɫɚ ɫɜɹɡɟɣ ɫ ɡɚɩɨɦɧɟɧɧɵɦɢ. ȿɫɥɢ ɬɟɤɭɳɢɟ ɜɟɫɚ ɫɨɜɩɚɥɢ ɫ ɡɚɩɨɦɧɟɧɧɵɦɢ, ɬɨ ɩɪɨɢɡɨɲɥɨ ɡɚɰɢɤɥɢɜɚɧɢɟ. ȼ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ ɩɟɪɟɯɨɞɢɦ ɤ ɲɚɝɭ 3. 3. m=m+1. ȿɫɥɢ m
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5.3 ɋɬɚɧɞɚɪɬ ɩɟɪɜɨɝɨ ɭɪɨɜɧɹ ɤɨɦɩɨɧɟɧɬɚ ɫɟɬɶ Ⱦɚɧɧɵɣ ɪɚɡɞɟɥ ɩɨɫɜɹɳɟɧ ɨɩɢɫɚɧɢɸ ɫɬɚɧɞɚɪɬɚ ɯɪɚɧɟɧɢɹ ɤɨɦɩɨɧɟɧɬɚ ɫɟɬɶ ɧɚ ɜɧɟɲɧɢɯ ɧɨɫɢɬɟɥɹɯ.
5.3.1 ɋɬɪɭɤɬɭɪɚ ɤɨɦɩɨɧɟɧɬɚ Ɋɚɫɫɦɨɬɪɢɦ ɛɨɥɟɟ ɩɨɞɪɨɛɧɨ ɫɬɪɭɤɬɭɪɵ ɞɚɧɧɵɯ ɫɟɬɢ. Ʉɚɤ ɭɠɟ ɛɵɥɨ ɨɩɢɫɚɧɨ ɜ ɩɟɪɜɨɣ ɱɚɫɬɢ ɝɥɚɜɵ, ɫɟɬɶ ɫɬɪɨɢɬɫɹ ɢɟɪɚɪɯɢɱɟɫɤɢ ɨɬ ɩɪɨɫɬɵɯ ɩɨɞɫɟɬɟɣ ɤ ɫɥɨɠɧɵɦ. ɉɪɨɫɬɟɣɲɢɦɢ ɩɨɞɫɟɬɹɦɢ ɹɜɥɹɸɬɫɹ ɷɥɟɦɟɧɬɵ. ɉɨɞɫɟɬɶ ɤɚɠɞɨɝɨ ɭɪɨɜɧɹ ɢɦɟɟɬ ɫɜɨɟ ɢɦɹ ɢ ɬɢɩ. ɋɭɳɟɫɬɜɭɸɬ ɫɥɟɞɭɸɳɢɟ ɬɢɩɵ ɩɨɞɫɟɬɟɣ: ɷɥɟɦɟɧɬ, ɤɚɫɤɚɞ, ɫɥɨɣ, ɰɢɤɥ ɫ ɮɢɤɫɢɪɨɜɚɧɧɵɦ ɱɢɫɥɨɦ ɬɚɤɬɨɜ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ ɢ ɰɢɤɥ, ɮɭɧɤɰɢɨɧɢɪɭɸɳɢɣ ɞɨ ɬɟɯ ɩɨɪ, ɩɨɤɚ ɧɟ ɜɵɩɨɥɧɢɬɫɹ ɧɟɤɨɬɨɪɨɟ ɭɫɥɨɜɢɟ. ɉɨɫɥɟɞɧɢɟ ɱɟɬɵɪɟ ɬɢɩɚ ɩɨɞɫɟɬɟɣ ɛɭɞɟɦ ɧɚɡɵɜɚɬɶ ɛɥɨɤɚɦɢ. ɂɦɟɧɚ ɩɨɞɫɟɬɟɣ ɨɩɪɟɞɟɥɹɸɬɫɹ ɩɪɢ ɤɨɧɫɬɪɭɢɪɨɜɚɧɢɢ. ȼ ɪɚɡɞɟɥɟ «ɂɦɟɧɚ ɫɬɪɭɤɬɭɪɧɵɯ ɟɞɢɧɢɰ ɤɨɦɩɨɧɟɧɬɨɜ» ɝɥɚɜɵ «Ɉɛɳɢɣ ɫɬɚɧɞɚɪɬ» ɩɪɢɜɟɞɟɧɵ ɩɪɚɜɢɥɚ ɩɨɫɬɪɨɟɧɢɹ ɩɨɥɧɨɝɨ ɢ ɨɞɧɨɡɧɚɱɧɨɝɨ ɢɦɟɧ ɩɨɞɫɟɬɢ. ȼ ɤɚɱɟɫɬɜɟ ɩɪɢɦɟɪɚ ɪɚɫɫɦɨɬɪɢɦ ɫɟɬɶ, ɤɨɧɫɬɪɭɢɪɨɜɚɧɢɟ ɤɨɬɨɪɨɣ ɩɪɨɢɥɥɸɫɬɪɢɪɨɜɚɧɨ ɜ ɩɟɪɜɨɣ ɱɚɫɬɢ ɝɥɚɜɵɧɚ ɪɢɫ. 2. ȼ ɨɩɢɫɚɧɢɢ ɫɟɬɢ NW ɨɞɧɨɡɧɚɱɧɨɟ ɢɦɹ ɩɟɪɜɨɝɨ ɧɟɣɪɨɧɚ ɜɬɨɪɨɝɨ ɫɥɨɹ ɢɦɟɟɬ ɜɢɞ K[2].SN.N[1]. ɉɪɢ ɨɩɢɫɚɧɢɢ ɫɥɨɹ ɨɞɧɨɡɧɚɱɧɨɟ ɢɦɹ ɩɟɪɜɨɝɨ ɧɟɣɪɨɧɚ ɡɚɩɢɫɵɜɚɟɬɫɹ ɤɚɤ N[1]. ȼ ɤɜɚɞɪɚɬɧɵɯ ɫɤɨɛɤɚɯ ɭɤɚɡɵɜɚɸɬɫɹ ɧɨɦɟɪ ɷɤɡɟɦɩɥɹɪɚ ɩɨɞɫɟɬɢ, ɜɯɨɞɹɳɟɣ ɜ ɧɟɩɨɫɪɟɞɫɬɜɟɧɧɨ ɫɨɞɟɪɠɚɳɭɸ ɟɟ ɫɬɪɭɤɬɭɪɭ ɜ ɧɟɫɤɨɥɶɤɢɯ ɷɤɡɟɦɩɥɹɪɚɯ.
5.3.2 ɋɢɝɧɚɥɵ ɢ ɩɚɪɚɦɟɬɪɵ ɉɪɢ ɢɫɩɨɥɶɡɨɜɚɧɢɢ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɹ ɞɥɹ ɢɡɦɟɧɟɧɢɹ ɫɬɪɭɤɬɭɪɵ ɫɟɬɢ ɢ ɡɧɚɱɟɧɢɣ ɨɛɭɱɚɟɦɵɯ ɩɚɪɚɦɟɬɪɨɜ ɞɪɭɝɢɦ ɤɨɦɩɨɧɟɧɬɚɦ ɛɵɜɚɟɬ ɧɟɨɛɯɨɞɢɦ ɩɪɹɦɨɣ ɞɨɫɬɭɩ ɤ ɫɢɝɧɚɥɚɦ ɢ ɩɚɪɚɦɟɬɪɚɦ ɫɟɬɢ ɜ ɰɟɥɨɦ ɢɥɢ ɨɬɞɟɥɶɧɵɯ ɟɟ ɩɨɞɫɟɬɟɣ. Ⱦɥɹ ɚɞɪɟɫɚɰɢɢ ɜɯɨɞɧɵɯ ɢ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɢɫɩɨɥɶɡɭɸɬɫɹ ɢɦɟɧɚ InSignals ɢ OutSignals, ɫɨɨɬɜɟɬɫɬɜɟɧɧɨ. Ɍɚɤɢɦ ɨɛɪɚɡɨɦ, ɞɥɹ ɩɨɥɭɱɟɧɢɹ ɦɚɫɫɢɜɚ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɜɬɨɪɨɝɨ ɫɥɨɹ ɫɟɬɢ, ɩɪɢɜɟɞɟɧɧɨɣ ɧɚ ɪɢɫ. 2, ɧɟɨɛɯɨɞɢɦɨ ɡɚɩɪɨɫɢɬɶ ɦɚɫɫɢɜ NW.K[2].InSignals, ɚ ɞɥɹ ɩɨɥɭɱɟɧɢɹ ɜɵɯɨɞɧɨɝɨ ɫɢɝɧɚɥɚ ɜɫɟɣ ɫɟɬɢ ɦɨɠɧɨ ɜɨɫɩɨɥɶɡɨɜɚɬɶɫɹ ɥɸɛɵɦ ɢɡ ɫɥɟɞɭɸɳɟɝɨ ɫɩɢɫɤɚ ɢɦɟɧ: · NW.OutSignals; · NW.N.OutSignals. Ⱦɥɹ ɩɨɥɭɱɟɧɢɹ ɤɨɧɤɪɟɬɧɨɝɨ ɫɢɝɧɚɥɚ ɢɡ ɦɚɫɫɢɜɚ ɫɢɝɧɚɥɨɜ ɧɟɨɛɯɨɞɢɦɨ ɜ ɤɨɧɰɟ ɜ ɤɜɚɞɪɚɬɧɵɯ ɫɤɨɛɤɚɯ ɭɤɚɡɚɬɶ ɧɨɦɟɪ ɫɢɝɧɚɥɚ. ɇɚɩɪɢɦɟɪ, ɞɥɹ ɩɨɥɭɱɟɧɢɹ ɬɪɟɬɶɟɝɨ ɜɯɨɞɧɨɝɨ ɫɢɝɧɚɥɚ ɜɬɨɪɨɝɨ ɫɥɨɹ ɫɟɬɢ ɧɭɠɧɨ ɭɤɚɡɚɬɶ ɫɥɟɞɭɸɳɟɟ ɢɦɹ – NW.K[2].InSignals[3]. Ⱦɥɹ ɩɨɥɭɱɟɧɢɹ ɞɨɫɬɭɩɚ ɤ ɩɚɪɚɦɟɬɪɚɦ ɧɭɠɧɨ ɭɤɚɡɚɬɶ ɢɦɹ ɩɨɞɫɟɬɢ, ɤ ɱɶɢɦ ɩɚɪɚɦɟɬɪɚɦ ɧɭɠɟɧ ɞɨɫɬɭɩ ɢ ɱɟɪɟɡ ɬɨɱɤɭ ɤɥɸɱɟɜɨɟ ɫɥɨɜɨ Parameters. ɉɪɢ ɧɟɨɛɯɨɞɢɦɨɫɬɢ ɩɨɥɭɱɢɬɶ ɤɨɧɤɪɟɬɧɵɣ ɩɚɪɚɦɟɬɪ, ɟɝɨ ɧɨɦɟɪ ɜ ɤɜɚɞɪɚɬɧɵɯ ɫɤɨɛɤɚɯ ɡɚɩɢɫɵɜɚɟɬɫɹ ɩɨɫɥɟ ɤɥɸɱɟɜɨɝɨ ɫɥɨɜɚ Parameters.
5.3.3 Ɉɛɭɱɚɟɦɵɟ ɢ ɧɟ ɨɛɭɱɚɟɦɵɟ ɩɚɪɚɦɟɬɪɵ ɢ ɫɢɝɧɚɥɵ ɉɪɢ ɨɛɭɱɟɧɢɢ ɩɚɪɚɦɟɬɪɨɜ ɢ ɫɢɝɧɚɥɨɜ (ɢɫɩɨɥɶɡɨɜɚɧɢɟ ɨɛɭɱɟɧɢɹ ɫɢɝɧɚɥɨɜ ɨɩɢɫɚɧɨ ɜɨ ɜɜɟɞɟɧɢɢ) ɜɨɡɧɢɤɚɟɬ ɧɟɨɛɯɨɞɢɦɨɫɬɶ ɨɛɭɱɚɬɶ ɬɨɥɶɤɨ ɱɚɫɬɶ ɢɡ ɧɢɯ. Ɍɚɤ, ɧɚɩɪɢɦɟɪ, ɩɪɢ ɨɩɢɫɚɧɢɢ ɨɛɭɱɟɧɢɹ ɩɟɪɫɟɩɬɪɨɧɚ ɜɨ ɜɬɨɪɨɣ ɱɚɫɬɢ ɷɬɨɣ ɝɥɚɜɵ ɛɵɥɨ ɨɬɦɟɱɟɧɨ, ɱɬɨ ɨɛɭɱɚɬɶ ɧɟɨɛɯɨɞɢɦɨ ɬɨɥɶɤɨ ɜɟɫɚ ɫɜɹɡɟɣ ɜɬɨɪɨɝɨ ɫɥɨɹ. Ⱦɥɹ ɪɟɚɥɢɡɚɰɢɢ ɷɬɨɣ ɜɨɡɦɨɠɧɨɫɬɢ ɢɫɩɨɥɶɡɭɸɬɫɹ ɞɜɚ ɦɚɫɫɢɜɚ ɥɨɝɢɱɟɫɤɢɯ ɩɟɪɟɦɟɧɧɵɯ – ɦɚɫɤɚ ɨɛɭɱɚɟɦɵɯ ɩɚɪɚɦɟɬɪɨɜ ɢ ɦɚɫɤɚ ɨɛɭɱɚɟɦɵɯ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ.
5.3.4 Ⱦɨɩɨɥɧɢɬɟɥɶɧɵɟ ɩɟɪɟɦɟɧɧɵɟ ɉɪɢ ɨɩɢɫɚɧɢɢ ɫɬɪɭɤɬɭɪɵ ɫɟɬɟɣ ɧɟɨɛɯɨɞɢɦɨ ɭɱɢɬɵɜɚɬɶ ɫɥɟɞɭɸɳɭɸ ɞɨɩɨɥɧɢɬɟɥɶɧɵɟ ɩɟɪɟɦɟɧɧɵɟ, ɞɨɫɬɭɩɧɵɟ ɜ ɦɟɬɨɞɚɯ Forw ɢ Back. Ⱦɥɹ ɤɚɠɞɨɣ ɫɟɬɢ ɩɪɢ ɩɪɹɦɨɦ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɢ ɨɩɪɟɞɟɥɟɧ ɫɥɟɞɭɸɳɢɣ ɧɚɛɨɪ ɩɟɪɟɦɟɧɧɵɯ: · InSignals[K] – ɦɚɫɫɢɜ ɢɡ K ɞɟɣɫɬɜɢɬɟɥɶɧɵɯ ɱɢɫɟɥ, ɫɨɞɟɪɠɚɳɢɯ ɜɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɩɪɹɦɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ. · OutSignals[N] – ɦɚɫɫɢɜ ɢɡ N ɞɟɣɫɬɜɢɬɟɥɶɧɵɯ ɱɢɫɟɥ, ɜ ɤɨɬɨɪɵɟ ɡɚɧɨɫɹɬɫɹ ɜɵɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɩɪɹɦɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ. · Parameters[M] – ɦɚɫɫɢɜ ɢɡ M ɞɟɣɫɬɜɢɬɟɥɶɧɵɯ ɱɢɫɟɥ, ɫɨɞɟɪɠɚɳɢɯ ɩɚɪɚɦɟɬɪɵ ɫɟɬɢ. ɉɪɢ ɜɵɩɨɥɧɟɧɢɢ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ ɫɟɬɢ ɞɨɫɬɭɩɧɵ ɟɳɟ ɬɪɢ ɦɚɫɫɢɜɚ: · Back.InSignals[K] – ɦɚɫɫɢɜ ɢɡ K ɞɟɣɫɬɜɢɬɟɥɶɧɵɯ ɱɢɫɟɥ, ɩɚɪɚɥɥɟɥɶɧɵɣ ɦɚɫɫɢɜɭ InSignals, ɜ ɤɨɬɨɪɵɣ ɡɚɧɨɫɹɬɫɹ ɜɵɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ. · Back.OutSignals[N] – ɦɚɫɫɢɜ ɢɡ N ɞɟɣɫɬɜɢɬɟɥɶɧɵɯ ɱɢɫɟɥ, ɩɚɪɚɥɥɟɥɶɧɵɣ ɦɚɫɫɢɜɭ OutSignals, ɫɨɞɟɪɠɚɳɢɣ ɜɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ. · Back.Parameters[M] – ɦɚɫɫɢɜ ɢɡ M ɞɟɣɫɬɜɢɬɟɥɶɧɵɯ ɱɢɫɟɥ, ɩɚɪɚɥɥɟɥɶɧɵɣ ɦɚɫɫɢɜɭ Parameters, ɜ ɤɨɬɨɪɵɣ ɡɚɧɨɫɹɬɫɹ ɜɵɱɢɫɥɟɧɧɵɟ ɩɪɢ ɨɛɪɚɬɧɨɦ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɢ ɩɨɩɪɚɜɤɢ ɤ ɩɚɪɚɦɟɬɪɚɦ ɫɟɬɢ.
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ɉɪɢ ɨɛɭɱɟɧɢɢ (ɦɨɞɢɮɢɤɚɰɢɢ ɩɚɪɚɦɟɬɪɨɜ ɢɥɢ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ) ɞɨɫɬɭɩɧɵ ɜɫɟ ɩɟɪɟɦɟɧɧɵɟ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ ɢ ɟɳɟ ɞɜɚ ɦɚɫɫɢɜɚ: · InSignalMask[K] – ɦɚɫɫɢɜ ɢɡ K ɥɨɝɢɱɟɫɤɢɯ ɩɟɪɟɦɟɧɧɵɯ, ɩɚɪɚɥɥɟɥɶɧɵɣ ɦɚɫɫɢɜɭ InSignals, ɫɨɞɟɪɠɚɳɢɣ ɦɚɫɤɭ ɨɛɭɱɚɟɦɨɫɬɢ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ. · ParamMask[M] – ɦɚɫɫɢɜ ɢɡ M ɥɨɝɢɱɟɫɤɢɯ ɩɟɪɟɦɟɧɧɵɯ, ɩɚɪɚɥɥɟɥɶɧɵɣ ɦɚɫɫɢɜɭ Parameters, ɫɨɞɟɪɠɚɳɢɣ ɦɚɫɤɭ ɨɛɭɱɚɟɦɨɫɬɢ ɩɚɪɚɦɟɬɪɨɜ.
5.3.5 ɋɬɚɧɞɚɪɬ ɹɡɵɤɚ ɨɩɢɫɚɧɢɹ ɫɟɬɟɣ əɡɵɤ ɨɩɢɫɚɧɢɹ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ɩɪɟɞɧɚɡɧɚɱɟɧ ɞɥɹ ɯɪɚɧɟɧɢɹ ɫɟɬɟɣ ɧɚ ɞɢɫɤɟ. ɋɥɟɞɭɟɬ ɨɬɦɟɬɢɬɶ, ɱɬɨ ɜ ɨɬɥɢɱɢɢ ɨɬ ɬɚɤɢɯ ɤɨɦɩɨɧɟɧɬɨɜ, ɤɚɤ ɩɪɟɞɨɛɪɚɛɨɬɱɢɤ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ, ɨɰɟɧɤɚ ɢɥɢ ɡɚɞɚɱɧɢɤ ɨɩɢɫɚɧɢɟ ɞɚɠɟ ɩɪɨɫɬɨɣ ɫɟɬɢ ɢɦɟɟɬ ɛɨɥɶɲɨɣ ɪɚɡɦɟɪ. ɋ ɞɪɭɝɨɣ ɫɬɨɪɨɧɵ, ɦɧɨɝɢɟ ɩɨɞɫɟɬɢ ɹɜɥɹɸɬɫɹ ɫɬɚɧɞɚɪɬɧɵɦɢ ɞɥɹ ɛɨɥɶɲɢɧɫɬɜɚ ɫɟɬɟɣ. Ⱦɥɹ ɤɨɦɩɨɧɟɧɬɚ ɫɟɬɶ ɧɟɬ ɫɦɵɫɥɚ ɜɜɨɞɢɬɶ ɧɟɛɨɥɶɲɨɣ ɧɚɛɨɪ ɫɬɚɧɞɚɪɬɧɵɯ ɷɥɟɦɟɧɬɨɜ ɢ ɩɨɞɫɟɬɟɣ, ɩɨɫɤɨɥɶɤɭ ɷɬɨɬ ɧɚɛɨɪ ɦɨɠɟɬ ɥɟɝɤɨ ɪɚɫɲɢɪɹɬɶɫɹ. Ȼɨɥɟɟ ɷɮɮɟɤɬɢɜɧɵɦ ɹɜɥɹɟɬɫɹ ɜɵɞɟɥɟɧɢɟ ɱɚɫɬɨ ɭɩɨɬɪɟɛɥɹɟɦɵɯ ɩɨɞɫɟɬɟɣ ɜ ɨɬɞɟɥɶɧɵɟ ɛɢɛɥɢɨɬɟɤɢ, ɩɨɞɤɥɸɱɚɟɦɵɟ ɤ ɨɩɢɫɚɧɢɹɦ ɤɨɧɤɪɟɬɧɵɯ ɫɟɬɟɣ. ȼ ɩɪɢɜɟɞɟɧɧɵɯ ɜ ɷɬɨɣ ɝɥɚɜɟ ɩɪɢɦɟɪɚɯ ɨɩɢɫɚɧɢɹ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ɜɵɞɟɥɟɧ ɪɹɞ ɛɢɛɥɢɨɬɟɤ.
5.3.5.1 Ʉɥɸɱɟɜɵɟ ɫɥɨɜɚ ɹɡɵɤɚ ȼ ɬɚɛɥ. 2 ɩɪɢɜɟɞɟɧ ɫɩɢɫɨɤ ɤɥɸɱɟɜɵɯ ɫɥɨɜ ɫɩɟɰɢɮɢɱɟɫɤɢɯ ɞɥɹ ɹɡɵɤɚ ɨɩɢɫɚɧɢɹ ɫɟɬɟɣ. Ɍɚɛɥɢɰɚ 2. Ʉɥɸɱɟɜɵɟ ɫɥɨɜɚ ɹɡɵɤɚ ɨɩɢɫɚɧɢɹ ɫɟɬɟɣ. Ʉɥɸɱɟɜɨɟ ɫɥɨɜɨɄɪɚɬɤɨɟ ɨɩɢɫɚɧɢɟ 1. Back Ɇɟɬɨɞ, ɨɫɭɳɟɫɬɜɥɹɸɳɢɣ ɨɛɪɚɬɧɨɟ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɟ ɩɨɞɫɟɬɢ. ɉɪɟɮɢɤɫ ɫɢɝɧɚɥɨɜ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ. 2. Block Ɍɢɩ ɚɪɝɭɦɟɧɬɚ ɩɨɞɫɟɬɢ. Ɉɡɧɚɱɚɟɬ, ɱɬɨ ɚɪɝɭɦɟɧɬ ɹɜɥɹɟɬɫɹ ɩɨɞɫɟɬɶɸ. 3. Cascad Ɍɢɩ ɩɨɞɫɟɬɢ – ɤɚɫɤɚɞ. 4. Connections ɇɚɱɚɥɨ ɛɥɨɤɚ ɨɩɢɫɚɧɢɹ ɫɜɹɡɟɣ ɩɨɞɫɟɬɢ. 5. Contents ɇɚɱɚɥɨ ɛɥɨɤɚ ɨɩɢɫɚɧɢɹ ɫɨɫɬɚɜɚ ɩɨɞɫɟɬɢ. 6. DefaultType Ɍɢɩ ɩɚɪɚɦɟɬɪɨɜ ɩɨ ɭɦɨɥɱɚɧɢɸ. 7. Element Ɍɢɩ ɩɨɞɫɟɬɢ – ɷɥɟɦɟɧɬ. 8. Forw Ɇɟɬɨɞ, ɨɫɭɳɟɫɬɜɥɹɸɳɢɣ ɩɪɹɦɨɟ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ ɩɨɞɫɟɬɢ. 9. InSignalMask ɂɦɹ, ɩɨ ɤɨɬɨɪɨɦɭ ɚɞɪɟɫɭɸɬɫɹ ɦɚɫɤɢ ɨɛɭɱɚɟɦɨɫɬɢ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɩɨɞɫɟɬɢ. 10. InSignals ɂɦɹ, ɩɨ ɤɨɬɨɪɨɦɭ ɚɞɪɟɫɭɸɬɫɹ ɜɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɩɨɞɫɟɬɢ; ɧɚɱɚɥɨ ɛɥɨɤɚ ɨɩɢɫɚɧɢɹ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ. 11. Layer Ɍɢɩ ɩɨɞɫɟɬɢ – ɫɥɨɣ. 12. Loop Ɍɢɩ ɩɨɞɫɟɬɢ – ɰɢɤɥ, ɜɵɩɨɥɧɹɟɦɵɣ ɭɤɚɡɚɧɧɨɟ ɱɢɫɥɨ ɪɚɡ. 13. MainNet ɇɚɱɚɥɨ ɨɩɢɫɚɧɢɹ ɝɥɚɜɧɨɣ ɫɟɬɢ 14. NetLib ɇɚɱɚɥɨ ɨɩɢɫɚɧɢɹ ɛɢɛɥɢɨɬɟɤɢ ɩɨɞɫɟɬɟɣ. 15. NetWork ɇɚɱɚɥɨ ɨɩɢɫɚɧɢɹ ɫɟɬɢ. 16. NumberOf Ɏɭɧɤɰɢɹ (ɡɚɩɪɨɫ). ȼɨɡɜɪɚɳɚɟɬ ɱɢɫɥɨ ɩɚɪɚɦɟɬɪɨɜ ɢɥɢ ɫɢɝɧɚɥɨɜ ɜ ɩɨɞɫɟɬɢ. 17. OutSignals ɂɦɹ, ɩɨ ɤɨɬɨɪɨɦɭ ɚɞɪɟɫɭɸɬɫɹ ɜɵɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɩɨɞɫɟɬɢ; ɧɚɱɚɥɨ ɛɥɨɤɚ ɨɩɢɫɚɧɢɹ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ. 18. ParamDef Ɂɚɝɨɥɨɜɨɤ ɨɩɪɟɞɟɥɟɧɢɹ ɬɢɩɚ ɩɚɪɚɦɟɬɪɨɜ. 19. Ɋarameters ɂɦɹ, ɩɨ ɤɨɬɨɪɨɦɭ ɚɞɪɟɫɭɸɬɫɹ ɩɚɪɚɦɟɬɪɵ ɩɨɞɫɟɬɢ; ɧɚɱɚɥɨ ɛɥɨɤɚ ɨɩɢɫɚɧɢɹ ɩɚɪɚɦɟɬɪɨɜ. 20. ParamMask ɂɦɹ, ɩɨ ɤɨɬɨɪɨɦɭ ɚɞɪɟɫɭɸɬɫɹ ɦɚɫɤɢ ɨɛɭɱɚɟɦɨɫɬɢ ɩɚɪɚɦɟɬɪɨɜ ɩɨɞɫɟɬɢ. 21. ParamType Ɂɚɝɨɥɨɜɨɤ ɨɩɢɫɚɧɢɹ ɬɢɩɚ ɩɚɪɚɦɟɬɪɨɜ. 22. Until Ɍɢɩ ɩɨɞɫɟɬɢ – ɰɢɤɥ, ɜɵɩɨɥɧɹɟɦɵɣ ɞɨ ɬɟɯ ɩɨɪ ɩɨɤɚ ɧɟ ɜɵɩɨɥɧɢɬɫɹ ɭɫɥɨɜɢɟ. 23. Used ɇɚɱɚɥɨ ɫɩɢɫɤɚ ɩɨɞɤɥɸɱɚɟɦɵɯ ɛɢɛɥɢɨɬɟɤ ɩɨɞɫɟɬɟɣ
5.3.5.2 ȻɇɎ ɹɡɵɤɚ ɨɩɢɫɚɧɢɹ ɫɟɬɟɣ Ɉɛɨɡɧɚɱɟɧɢɹ, ɩɪɢɧɹɬɵɟ ɜ ɞɚɧɧɨɦ ɪɚɫɲɢɪɟɧɢɢ ȻɇɎ ɢ ɨɩɢɫɚɧɢɟ ɪɹɞɚ ɤɨɧɫɬɪɭɤɰɢɣ ɩɪɢɜɟɞɟɧɵ ɜ ɝɥɚɜɟ “Ɉɛɳɢɣ ɫɬɚɧɞɚɪɬ” ɜ ɪɚɡɞɟɥɟ “Ɉɩɢɫɚɧɢɟ ɹɡɵɤɚ ɨɩɢɫɚɧɢɹ ɤɨɦɩɨɧɟɧɬɨɜ”. <Ɉɩɢɫɚɧɢɟ ɛɢɛɥɢɨɬɟɤɢ ɩɨɞɫɟɬɟɣ> ::= <Ɂɚɝɨɥɨɜɨɤ ɛɢɛɥɢɨɬɟɤɢ> <Ɉɩɢɫɚɧɢɟ ɩɨɞɫɟɬɟɣ> <Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɛɢɛɥɢɨɬɟɤɢ> <Ɂɚɝɨɥɨɜɨɤ ɛɢɛɥɢɨɬɟɤɢ> ::= NetLib <ɂɦɹ ɛɢɛɥɢɨɬɟɤɢ> [Used <ɋɩɢɫɨɤ ɢɦɟɧ ɛɢɛɥɢɨɬɟɤ>]
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<ɂɦɹ ɛɢɛɥɢɨɬɟɤɢ> ::= <ɂɞɟɧɬɢɮɢɤɚɬɨɪ> <ɋɩɢɫɨɤ ɢɦɟɧ ɛɢɛɥɢɨɬɟɤ> ::= <ɂɦɹ ɢɫɩɨɥɶɡɭɟɦɨɣ ɛɢɛɥɢɨɬɟɤɢ> [,<ɋɩɢɫɨɤ ɢɦɟɧ ɛɢɛɥɢɨɬɟɤ>] <ɂɦɹ ɢɫɩɨɥɶɡɭɟɦɨɣ ɛɢɛɥɢɨɬɟɤɢ> ::= <ɂɞɟɧɬɢɮɢɤɚɬɨɪ> <Ɉɩɢɫɚɧɢɟ ɩɨɞɫɟɬɟɣ> ::= <Ɉɩɢɫɚɧɢɟ ɩɨɞɫɟɬɢ> [<Ɉɩɢɫɚɧɢɟ ɩɨɞɫɟɬɟɣ>] <Ɉɩɢɫɚɧɢɟ ɩɨɞɫɟɬɢ> ::= {<Ɉɩɢɫɚɧɢɟ ɷɥɟɦɟɧɬɚ> ½ <Ɉɩɢɫɚɧɢɟ ɛɥɨɤɚ> ½ <Ɉɩɢɫɚɧɢɟ ɮɭɧɤɰɢɣ>} <Ɉɩɢɫɚɧɢɟ ɷɥɟɦɟɧɬɚ> ::= <Ɂɚɝɨɥɨɜɨɤ ɨɩɢɫɚɧɢɹ ɷɥɟɦɟɧɬɚ> <Ɉɩɢɫɚɧɢɟ ɫɢɝɧɚɥɨɜ ɢ ɩɚɪɚɦɟɬɪɨɜ> [<Ɉɩɢɫɚɧɢɟ ɬɢɩɨɜ ɩɚɪɚɦɟɬɪɨɜ>] [<Ɉɩɪɟɞɟɥɟɧɢɟ ɬɢɩɨɜ ɩɚɪɚɦɟɬɪɨɜ>] [<Ɉɩɢɫɚɧɢɟ ɫɬɚɬɢɱɟɫɤɢɯ ɩɟɪɟɦɟɧɧɵɯ>] [<ɍɫɬɚɧɨɜɥɟɧɢɟ ɡɧɚɱɟɧɢɣ ɫɬɚɬɢɱɟɫɤɢɯ ɩɟɪɟɦɟɧɧɵɯ>] <Ɉɩɢɫɚɧɢɟ ɦɟɬɨɞɨɜ> <Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɷɥɟɦɟɧɬɚ> <Ɂɚɝɨɥɨɜɨɤ ɨɩɢɫɚɧɢɹ ɷɥɟɦɟɧɬɚ> ::= Element <ɂɦɹ ɷɥɟɦɟɧɬɚ> [(<ɋɩɢɫɨɤ ɮɨɪɦɚɥɶɧɵɯ ɚɪɝɭɦɟɧɬɨɜ>)] <ɂɦɹ ɷɥɟɦɟɧɬɚ> ::= <ɂɞɟɧɬɢɮɢɤɚɬɨɪ> <Ɉɩɢɫɚɧɢɟ ɫɢɝɧɚɥɨɜ ɢ ɩɚɪɚɦɟɬɪɨɜ> ::= <Ɉɩɢɫɚɧɢɟ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ> <Ɉɩɢɫɚɧɢɟ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ> [<Ɉɩɢɫɚɧɢɟ ɩɚɪɚɦɟɬɪɨɜ>] <Ɉɩɢɫɚɧɢɟ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ> ::= InSignals <Ʉɨɧɫɬɚɧɬɧɨɟ ɜɵɪɚɠɟɧɢɟ ɬɢɩɚ Long> <Ɉɩɢɫɚɧɢɟ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ> ::= OutSignals <Ʉɨɧɫɬɚɧɬɧɨɟ ɜɵɪɚɠɟɧɢɟ ɬɢɩɚ Long> <Ɉɩɢɫɚɧɢɟ ɩɚɪɚɦɟɬɪɨɜ> ::= Parameters <Ʉɨɧɫɬɚɧɬɧɨɟ ɜɵɪɚɠɟɧɢɟ ɬɢɩɚ Long> <Ɉɩɢɫɚɧɢɟ ɬɢɩɨɜ ɩɚɪɚɦɟɬɪɨɜ> ::= <Ɉɩɢɫɚɧɢɟ ɬɢɩɚ ɩɚɪɚɦɟɬɪɨɜ> [<Ɉɩɢɫɚɧɢɟ ɬɢɩɨɜ ɩɚɪɚɦɟɬɪɨɜ>] <Ɉɩɢɫɚɧɢɟ ɬɢɩɚ ɩɚɪɚɦɟɬɪɨɜ> ::= ParamType <ɂɦɹ ɬɢɩɚ ɩɚɪɚɦɟɬɪɚ> <ɋɩɢɫɨɤ> <ɂɦɹ ɬɢɩɚ ɩɚɪɚɦɟɬɪɚ> ::= <ɂɞɟɧɬɢɮɢɤɚɬɨɪ> <ɋɩɢɫɨɤ> ::= {Parameters[<ɇɚɱɚɥɶɧɵɣ ɧɨɦɟɪ> [..<Ʉɨɧɟɱɧɵɣ ɧɨɦɟɪ> [<ɒɚɝ>]]] ½ InSignals[<ɇɚɱɚɥɶɧɵɣ ɧɨɦɟɪ> [..<Ʉɨɧɟɱɧɵɣ ɧɨɦɟɪ> [<ɒɚɝ>]]]} [;<ɋɩɢɫɨɤ>] <Ɉɩɪɟɞɟɥɟɧɢɟ ɬɢɩɨɜ ɩɚɪɚɦɟɬɪɨɜ> ::= <Ɉɩɪɟɞɟɥɟɧɢɟ ɬɢɩɚ ɩɚɪɚɦɟɬɪɚ> [<Ɉɩɪɟɞɟɥɟɧɢɟ ɬɢɩɨɜ ɩɚɪɚɦɟɬɪɨɜ>] <Ɉɩɪɟɞɟɥɟɧɢɟ ɬɢɩɚ ɩɚɪɚɦɟɬɪɚ> ::= ParamDef <ɂɦɹ ɬɢɩɚ ɩɚɪɚɦɟɬɪɚ> <Ɇɢɧɢɦɚɥɶɧɨɟ ɡɧɚɱɟɧɢɟ> <Ɇɚɤɫɢɦɚɥɶɧɨɟ ɡɧɚɱɟɧɢɟ> <Ɇɢɧɢɦɚɥɶɧɨɟ ɡɧɚɱɟɧɢɟ> ::= <Ʉɨɧɫɬɚɧɬɧɨɟ ɜɵɪɚɠɟɧɢɟ ɬɢɩɚ Real> <Ɇɚɤɫɢɦɚɥɶɧɨɟ ɡɧɚɱɟɧɢɟ> ::= <Ʉɨɧɫɬɚɧɬɧɨɟ ɜɵɪɚɠɟɧɢɟ ɬɢɩɚ Real> <ɍɫɬɚɧɨɜɥɟɧɢɟ ɡɧɚɱɟɧɢɣ ɫɬɚɬɢɱɟɫɤɢɯ ɩɟɪɟɦɟɧɧɵɯ> ::= <ɍɫɬɚɧɨɜɥɟɧɢɟ ɩɚɪɚɦɟɬɪɨɜ ɉɨɞɫɟɬɢ> [;<ɍɫɬɚɧɨɜɥɟɧɢɟ ɡɧɚɱɟɧɢɣ ɫɬɚɬɢɱɟɫɤɢɯ ɩɟɪɟɦɟɧɧɵɯ>] <Ɉɩɢɫɚɧɢɟ ɦɟɬɨɞɨɜ> ::= <Ɉɩɢɫɚɧɢɟ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ ɜɩɟɪɟɞ> <Ɉɩɢɫɚɧɢɟ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ ɧɚɡɚɞ> <Ɉɩɢɫɚɧɢɟ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ ɜɩɟɪɟɞ> ::= Forw [<Ɉɩɢɫɚɧɢɟ ɩɟɪɟɦɟɧɧɵɯ>] <Ɍɟɥɨ ɦɟɬɨɞɚ> <Ɍɟɥɨ ɦɟɬɨɞɚ> ::= Begin <ɋɨɫɬɚɜɧɨɣ ɨɩɟɪɚɬɨɪ> End <Ɉɩɢɫɚɧɢɟ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ ɧɚɡɚɞ> ::= Back [<Ɉɩɢɫɚɧɢɟ ɩɟɪɟɦɟɧɧɵɯ>] <Ɍɟɥɨ ɦɟɬɨɞɚ> <Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɷɥɟɦɟɧɬɚ> ::= End <ɂɦɹ ɷɥɟɦɟɧɬɚ> <Ɉɩɢɫɚɧɢɟ ɛɥɨɤɚ> ::= <Ɂɚɝɨɥɨɜɨɤ ɨɩɢɫɚɧɢɹ ɛɥɨɤɚ> <Ɉɩɢɫɚɧɢɟ ɫɨɫɬɚɜɚ> <Ɉɩɢɫɚɧɢɟ ɫɢɝɧɚɥɨɜ ɢ ɩɚɪɚɦɟɬɪɨɜ> [<Ɉɩɢɫɚɧɢɟ ɫɬɚɬɢɱɟɫɤɢɯ ɩɟɪɟɦɟɧɧɵɯ>] [<ɍɫɬɚɧɨɜɥɟɧɢɟ ɡɧɚɱɟɧɢɣ ɫɬɚɬɢɱɟɫɤɢɯ ɩɟɪɟɦɟɧɧɵɯ>] <Ɉɩɢɫɚɧɢɟ ɫɜɹɡɟɣ> [<Ɉɩɪɟɞɟɥɟɧɢɟ ɬɢɩɨɜ ɩɚɪɚɦɟɬɪɨɜ>] <Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɛɥɨɤɚ> <Ɂɚɝɨɥɨɜɨɤ ɨɩɢɫɚɧɢɹ ɛɥɨɤɚ> ::= {<Ɉɩɢɫɚɧɢɟ ɤɚɫɤɚɞɚ> ½ <Ɉɩɢɫɚɧɢɟ ɫɥɨɹ> ½ <Ɉɩɢɫɚɧɢɟ ɰɢɤɥɚ ɫ ɮɢɤɫɢɪɨɜɚɧɧɵɦ ɱɢɫɥɨɦ ɲɚɝɨɜ> ½ <Ɉɩɢɫɚɧɢɟ ɰɢɤɥɚ ɩɨ ɭɫɥɨɜɢɸ>} <Ɉɩɢɫɚɧɢɟ ɤɚɫɤɚɞɚ> ::= Cascad <ɂɦɹ ɛɥɨɤɚ> [(<ɋɩɢɫɨɤ ɮɨɪɦɚɥɶɧɵɯ ɚɪɝɭɦɟɧɬɨɜ ɛɥɨɤɚ>)] <ɂɦɹ ɛɥɨɤɚ> ::= <ɂɞɟɧɬɢɮɢɤɚɬɨɪ> <ɋɩɢɫɨɤ ɮɨɪɦɚɥɶɧɵɯ ɚɪɝɭɦɟɧɬɨɜ ɛɥɨɤɚ> ::= {<ɋɩɢɫɨɤ ɮɨɪɦɚɥɶɧɵɯ ɚɪɝɭɦɟɧɬɨɜ> ½ <Ⱥɪɝɭɦɟɧɬ – ɩɨɞɫɟɬɶ>} [;<ɋɩɢɫɨɤ ɮɨɪɦɚɥɶɧɵɯ ɚɪɝɭɦɟɧɬɨɜ ɛɥɨɤɚ>] <Ⱥɪɝɭɦɟɧɬ – ɩɨɞɫɟɬɶ>::= <ɋɩɢɫɨɤ ɢɦɟɧ ɚɪɝɭɦɟɧɬɨɜ – ɩɨɞɫɟɬɟɣ> : Block <ɋɩɢɫɨɤ ɢɦɟɧ ɚɪɝɭɦɟɧɬɨɜ – ɩɨɞɫɟɬɟɣ> ::= <ɂɦɹ ɚɪɝɭɦɟɧɬɚ – ɩɨɞɫɟɬɢ> [,<ɋɩɢɫɨɤ ɢɦɟɧ ɚɪɝɭɦɟɧɬɨɜ – ɩɨɞɫɟɬɟɣ>] <ɂɦɹ ɚɪɝɭɦɟɧɬɚ – ɩɨɞɫɟɬɢ> ::= <ɂɞɟɧɬɢɮɢɤɚɬɨɪ> <Ɉɩɢɫɚɧɢɟ ɫɥɨɹ> ::= Layer <ɂɦɹ ɛɥɨɤɚ> [(<ɋɩɢɫɨɤ ɮɨɪɦɚɥɶɧɵɯ ɚɪɝɭɦɟɧɬɨɜ ɛɥɨɤɚ>)] <Ɉɩɢɫɚɧɢɟ ɰɢɤɥɚ ɫ ɮɢɤɫɢɪɨɜɚɧɧɵɦ ɱɢɫɥɨɦ ɲɚɝɨɜ> ::= Loop <ɂɦɹ ɛɥɨɤɚ> [(<ɋɩɢɫɨɤ ɮɨɪɦɚɥɶɧɵɯ ɚɪɝɭɦɟɧɬɨɜ ɛɥɨɤɚ>)] <ɑɢɫɥɨ ɩɨɜɬɨɪɨɜ ɰɢɤɥɚ> <ɑɢɫɥɨ ɩɨɜɬɨɪɨɜ ɰɢɤɥɚ> ::= <Ʉɨɧɫɬɚɧɬɧɨɟ ɜɵɪɚɠɟɧɢɟ ɬɢɩɚ Long> <Ɉɩɢɫɚɧɢɟ ɰɢɤɥɚ ɩɨ ɭɫɥɨɜɢɸ> ::= Until <ɂɦɹ ɛɥɨɤɚ> [(<ɋɩɢɫɨɤ ɮɨɪɦɚɥɶɧɵɯ ɚɪɝɭɦɟɧɬɨɜ ɛɥɨɤɚ>)] : <ȼɵɪɚɠɟɧɢɟ ɬɢɩɚ Logic> <Ɉɩɢɫɚɧɢɟ ɫɨɫɬɚɜɚ> ::= Contents <ɋɩɢɫɨɤ ɢɦɟɧ ɩɨɞɫɟɬɟɣ> <ɋɩɢɫɨɤ ɢɦɟɧ ɩɨɞɫɟɬɟɣ> ::= <ɂɦɹ ɩɨɞɫɟɬɢ> [,<ɋɩɢɫɨɤ ɢɦɟɧ ɩɨɞɫɟɬɟɣ>] <ɂɦɹ ɩɨɞɫɟɬɢ> ::= <ɉɫɟɜɞɨɧɢɦ>: {<ɂɦɹ ɪɚɧɟɟ ɨɩɢɫɚɧɧɨɣ ɩɨɞɫɟɬɢ> [(<ɋɩɢɫɨɤ ɮɚɤɬɢɱɟɫɤɢɯ ɚɪɝɭɦɟɧɬɨɜ ɛɥɨɤɚ>)] [[<ɑɢɫɥɨ ɷɤɡɟɦɩɥɹɪɨɜ>]] ½ <ɂɦɹ ɚɪɝɭɦɟɧɬɚ – ɩɨɞɫɟɬɢ> [[<ɑɢɫɥɨ ɷɤɡɟɦɩɥɹɪɨɜ >]]} <ɉɫɟɜɞɨɧɢɦ> ::= <ɂɞɟɧɬɢɮɢɤɚɬɨɪ>
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<ɑɢɫɥɨ ɷɤɡɟɦɩɥɹɪɨɜ > ::= <Ʉɨɧɫɬɚɧɬɧɨɟ ɜɵɪɚɠɟɧɢɟ ɬɢɩɚ Long> <ɂɦɹ ɪɚɧɟɟ ɨɩɢɫɚɧɧɨɣ ɩɨɞɫɟɬɢ> ::= <ɂɞɟɧɬɢɮɢɤɚɬɨɪ> <ɋɩɢɫɨɤ ɮɚɤɬɢɱɟɫɤɢɯ ɚɪɝɭɦɟɧɬɨɜ ɛɥɨɤɚ> ::= <Ɏɚɤɬɢɱɟɫɤɢɣ ɚɪɝɭɦɟɧɬ ɛɥɨɤɚ> [,<ɋɩɢɫɨɤ ɮɚɤɬɢɱɟɫɤɢɯ ɚɪɝɭɦɟɧɬɨɜ ɛɥɨɤɚ>] <Ɏɚɤɬɢɱɟɫɤɢɣ ɚɪɝɭɦɟɧɬ ɛɥɨɤɚ> ::= {<Ɏɚɤɬɢɱɟɫɤɢɣ ɚɪɝɭɦɟɧɬ> ½ <ɂɦɹ ɚɪɝɭɦɟɧɬɚ – ɩɨɞɫɟɬɢ>} <Ɉɩɢɫɚɧɢɟ ɫɜɹɡɟɣ> ::= {<Ɉɩɢɫɚɧɢɟ ɪɚɫɩɪɟɞɟɥɟɧɢɹ ȼɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ, Ȼɥɨɤɚ, ɉɨɞɫɟɬɢ, InSignals > ½ <Ɉɩɢɫɚɧɢɟ ɪɚɫɩɪɟɞɟɥɟɧɢɹ ȼɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ, Ȼɥɨɤɚ, ɉɨɞɫɟɬɢ, OutSignals > ½ <Ɉɩɢɫɚɧɢɟ ɪɚɫɩɪɟɞɟɥɟɧɢɹ ɉɚɪɚɦɟɬɪɨɜ, Ȼɥɨɤɚ, ɉɨɞɫɟɬɢ, Parameters >} <Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɛɥɨɤɚ>::= End <ɂɦɹ ɛɥɨɤɚ> <Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɛɢɛɥɢɨɬɟɤɢ> ::= End NetLib <Ɉɩɢɫɚɧɢɟ ɫɟɬɢ> ::= <Ɂɚɝɨɥɨɜɨɤ ɨɩɢɫɚɧɢɹ ɫɟɬɢ> <Ɉɩɢɫɚɧɢɟ ɩɨɞɫɟɬɟɣ> <Ɉɩɢɫɚɧɢɟ ɝɥɚɜɧɨɣ ɫɟɬɢ> <Ɇɚɫɫɢɜɵ ɩɚɪɚɦɟɬɪɨɜ ɢ ɦɚɫɨɤ ɫɟɬɢ> <Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɫɟɬɢ> <Ɂɚɝɨɥɨɜɨɤ ɨɩɢɫɚɧɢɹ ɫɟɬɢ> ::= NetWork <ɂɦɹ ɫɟɬɢ> [Used <ɋɩɢɫɨɤ ɢɦɟɧ ɛɢɛɥɢɨɬɟɤ>] <ɂɦɹ ɫɟɬɢ> ::= <ɂɞɟɧɬɢɮɢɤɚɬɨɪ> <Ɉɩɢɫɚɧɢɟ ɝɥɚɜɧɨɣ ɫɟɬɢ> ::= MainNet <ɂɦɹ ɪɚɧɟɟ ɨɩɢɫɚɧɧɨɣ ɩɨɞɫɟɬɢ> [(<ɋɩɢɫɨɤ ɮɚɤɬɢɱɟɫɤɢɯ ɚɪɝɭɦɟɧɬɨɜ ɛɥɨɤɚ>)] <Ɇɚɫɫɢɜɵ ɩɚɪɚɦɟɬɪɨɜ ɢ ɦɚɫɨɤ ɫɟɬɢ> ::= <Ɇɚɫɫɢɜ ɩɚɪɚɦɟɬɪɨɜ> <Ɇɚɫɫɢɜ ɦɚɫɤɢ ɨɛɭɱɚɟɦɨɫɬɢ ɩɚɪɚɦɟɬɪɨɜ> <Ɇɚɫɫɢɜ ɩɚɪɚɦɟɬɪɨɜ> ::= Parameters <Ɂɧɚɱɟɧɢɹ ɩɚɪɚɦɟɬɪɨɜ>; <Ɂɧɚɱɟɧɢɹ ɩɚɪɚɦɟɬɪɨɜ> ::= <Ⱦɟɣɫɬɜɢɬɟɥɶɧɨɟ ɱɢɫɥɨ> [, <Ɂɧɚɱɟɧɢɹ ɩɚɪɚɦɟɬɪɨɜ>] <Ɇɚɫɫɢɜ ɦɚɫɤɢ ɨɛɭɱɚɟɦɨɫɬɢ ɩɚɪɚɦɟɬɪɨɜ> ::= ParamMask <Ɂɧɚɱɟɧɢɹ ɦɚɫɤɢ>; <Ɂɧɚɱɟɧɢɹ ɦɚɫɤɢ> ::= <Ʉɨɧɫɬɚɧɬɚ ɬɢɩɚ Logic> [,<Ɂɧɚɱɟɧɢɹ ɦɚɫɤɢ>] <Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɫɟɬɢ> ::= End NetWork
5.3.5.3 Ɉɩɢɫɚɧɢɟ ɹɡɵɤɚ ɨɩɢɫɚɧɢɹ ɫɟɬɟɣ ȼ ɷɬɨɦ ɪɚɡɞɟɥɟ ɩɪɢɜɨɞɢɬɫɹ ɞɟɬɚɥɶɧɨɟ ɨɩɢɫɚɧɢɟ ɹɡɵɤɚ ɨɩɢɫɚɧɢɹ ɫɟɬɟɣ, ɞɨɩɨɥɧɹɸɳɟɟ ȻɇɎ, ɩɪɢɜɟɞɟɧɧɭɸ ɜ ɩɪɟɞɵɞɭɳɟɦ ɪɚɡɞɟɥɟ ɢ ɨɩɢɫɚɧɢɟ ɨɛɳɢɯ ɤɨɧɫɬɪɭɤɰɢɣ, ɩɪɢɜɟɞɟɧɧɨɟ ɜ ɝɥɚɜɟ «Ɉɛɳɢɣ ɫɬɚɧɞɚɪɬ».
5.3.5.3.1 Ɉɩɢɫɚɧɢɟ ɢ ɨɛɥɚɫɬɶ ɞɟɣɫɬɜɢɹ ɩɟɪɟɦɟɧɧɵɯ ȼɫɩɨɦɨɝɚɬɟɥɶɧɵɟ ɩɟɪɟɦɟɧɧɵɟ ɦɨɝɭɬ ɩɨɬɪɟɛɨɜɚɬɶɫɹ ɩɪɢ ɨɩɢɫɚɧɢɢ ɩɪɹɦɨɝɨ ɢ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ ɷɥɟɦɟɧɬɨɜ. ɉɟɪɟɦɟɧɧɚɹ ɞɟɣɫɬɜɭɟɬ ɬɨɥɶɤɨ ɜ ɩɪɟɞɟɥɚɯ ɬɨɣ ɩɪɨɰɟɞɭɪɵ, ɜ ɤɨɬɨɪɨɣ ɨɧɚ ɨɩɢɫɚɧɚ. Ʉɪɨɦɟ ɹɜɧɨ ɨɩɢɫɚɧɧɵɯ ɩɟɪɟɦɟɧɧɵɯ, ɜ ɦɟɬɨɞɟ Forw ɞɨɫɬɭɩɧɵ ɬɚɤɠɟ ɫɢɝɧɚɥɵ ɩɪɹɦɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ ɢ ɩɚɪɚɦɟɬɪɵ ɷɥɟɦɟɧɬɚ, ɚ ɜ ɦɟɬɨɞɟ Back – ɜɯɨɞɧɵɟ ɢ ɜɵɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɩɪɹɦɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ, ɜɵɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ, ɩɚɪɚɦɟɬɪɵ ɷɥɟɦɟɧɬɚ ɢ ɝɪɚɞɢɟɧɬ ɩɨ ɩɚɪɚɦɟɬɪɚɦ ɷɥɟɦɟɧɬɚ. ȼɨ ɜɫɟɯ ɦɟɬɨɞɚɯ ɞɨɫɬɭɩɧɵ ɚɪɝɭɦɟɧɬɵ ɷɥɟɦɟɧɬɚ. ɋɬɚɬɢɱɟɫɤɢɟ ɩɟɪɟɦɟɧɧɵɟ, ɨɩɢɫɵɜɚɟɦɵɟ ɩɨɫɥɟ ɤɥɸɱɟɜɨɝɨ ɫɥɨɜɚ Static, ɭɧɢɤɚɥɶɧɵ ɞɥɹ ɤɚɠɞɨɝɨ ɷɤɡɟɦɩɥɹɪɚ ɷɥɟɦɟɧɬɚ ɢɥɢ ɛɥɨɤɚ, ɢ ɞɨɫɬɭɩɧɵ ɬɨɥɶɤɨ ɜ ɩɪɟɞɟɥɚɯ ɛɥɨɤɚ. ɗɬɢ ɩɟɪɟɦɟɧɧɵɟ ɦɨɝɭɬ ɩɨɬɪɟɛɨɜɚɬɶɫɹ ɞɥɹ ɜɵɱɢɫɥɟɧɢɹ ɭɫɥɨɜɢɣ ɜ ɰɢɤɥɟ ɬɢɩɚ Until. ȼɨɡɦɨɠɧɨ ɢɫɩɨɥɶɡɨɜɚɧɢɟ ɬɚɤɢɯ ɩɟɪɟɦɟɧɧɵɯ ɜ ɷɥɟɦɟɧɬɚɯ, ɧɚɩɪɢɦɟɪ, ɞɥɹ ɯɪɚɧɟɧɢɹ ɩɪɟɞɵɞɭɳɟɝɨ ɫɨɫɬɨɹɧɢɹ ɷɥɟɦɟɧɬɚ. Ʉɪɨɦɟ ɬɨɝɨ, ɜ ɫɬɚɬɢɱɟɫɤɨɣ ɩɟɪɟɦɟɧɧɨɣ ɦɨɠɧɨ ɯɪɚɧɢɬɶ ɡɧɚɱɟɧɢɹ ɧɟ ɨɛɭɱɚɟɦɵɯ ɩɚɪɚɦɟɬɪɨɜ.
5.3.5.3.2 Ɇɟɬɨɞɵ Forw ɢ Back ɞɥɹ ɛɥɨɤɨɜ Ɇɟɬɨɞɵ Forw ɢ Back ɞɥɹ ɛɥɨɤɨɜ ɧɟ ɨɩɢɫɵɜɚɸɬɫɹ ɜ ɹɡɵɤɟ ɨɩɢɫɚɧɢɹ ɫɟɬɟɣ. ɗɬɨ ɫɜɹɡɚɧɨ ɫ ɬɟɦ, ɱɬɨ ɩɪɢ ɜɵɩɨɥɧɟɧɢɢ ɦɟɬɨɞɚ Forw ɛɥɨɤɨɦ ɩɪɨɢɫɯɨɞɢɬ ɜɵɡɨɜ ɦɟɬɨɞɚ Forw ɫɨɫɬɚɜɥɹɸɳɢɯ ɛɥɨɤ ɩɨɞɫɟɬɟɣ (ɞɥɹ ɷɥɟɦɟɧɬɨɜ – ɦɟɬɨɞɚ Forw) ɜ ɩɨɪɹɞɤɟ ɢɯ ɨɩɢɫɚɧɢɹ ɜ ɪɚɡɞɟɥɟ ɨɩɢɫɚɧɢɹ ɫɨɫɬɚɜɚ ɛɥɨɤɚ. ɉɪɢ ɜɵɩɨɥɧɟɧɢɢ ɦɟɬɨɞɚ Back ɩɪɨɢɫɯɨɞɢɬ ɜɵɡɨɜ ɦɟɬɨɞɨɜ Back ɫɨɫɬɚɜɥɹɸɳɢɯ ɛɥɨɤ ɩɨɞɫɟɬɟɣ ɜ ɩɨɪɹɞɤɟ ɨɛɪɚɬɧɨɦ ɩɨɪɹɞɤɭ ɢɯ ɨɩɢɫɚɧɢɹ ɜ ɪɚɡɞɟɥɟ ɨɩɢɫɚɧɢɹ ɫɨɫɬɚɜɚ ɛɥɨɤɚ.
5.3.5.3.3 Ɉɩɢɫɚɧɢɟ ɷɥɟɦɟɧɬɨɜ Ɉɩɢɫɚɧɢɟ ɷɥɟɦɟɧɬɚ ɫɨɫɬɨɢɬ ɢɡ ɫɥɟɞɭɸɳɢɯ ɨɫɧɨɜɧɵɯ ɪɚɡɞɟɥɨɜ: ɡɚɝɨɥɨɜɤɚ ɷɥɟɦɟɧɬɚ, ɨɩɢɫɚɧɢɹ ɫɢɝɧɚɥɨɜ ɢ ɩɚɪɚɦɟɬɪɨɜ, ɨɩɢɫɚɧɢɹ ɫɬɚɬɢɱɟɫɤɢɯ ɩɟɪɟɦɟɧɧɵɯ ɢ ɨɩɢɫɚɧɢɹ ɦɟɬɨɞɨɜ. Ɂɚɝɨɥɨɜɨɤ ɷɥɟɦɟɧɬɚ ɢɦɟɟɬ ɫɥɟɞɭɸɳɢɣ ɫɢɧɬɚɤɫɢɫ: Element ɂɦɹ_ɗɥɟɦɟɧɬɚ (Ⱥɪɝɭɦɟɧɬɵ ɷɥɟɦɟɧɬɚ) Ⱥɪɝɭɦɟɧɬɵ ɷɥɟɦɟɧɬɚ ɹɜɥɹɸɬɫɹ ɧɟɨɛɹɡɚɬɟɥɶɧɨɣ ɱɚɫɬɶɸ ɡɚɝɨɥɨɜɤɚ. ȼ ɫɥɟɞɭɸɳɟɦ ɪɚɡɞɟɥɟ ɩɪɢɜɟɞɟɧɵ ɨɩɢɫɚɧɢɹ ɧɟɫɤɨɥɶɤɢɯ ɷɥɟɦɟɧɬɨɜ. Ɉɬɦɟɬɢɦ, ɱɬɨ ɫɢɝɦɨɢɞɧɵɣ ɷɥɟɦɟɧɬ ɨɩɢɫɚɧ ɞɜɭɦɹ ɫɩɨɫɨɛɚɦɢ: ɫ ɩɪɢɧɰɢɩɢɚɥɶɧɨ ɧɟ ɨɛɭɱɚɟɦɨɣ (S_NotTrain) ɢ ɫ ɨɛɭɱɚɟɦɨɣ (S_Train) ɯɚɪɚɤɬɟɪɢɫɬɢɤɨɣ.
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Ɋɚɡɞɟɥ ɨɩɢɫɚɧɢɹ ɫɢɝɧɚɥɨɜ ɢ ɩɚɪɚɦɟɬɪɨɜ ɫɥɟɞɭɟɬ ɫɪɚɡɭ ɩɨɫɥɟ ɡɚɝɨɥɨɜɤɚ ɷɥɟɦɟɧɬɚ ɢ ɫɨɫɬɨɢɬ ɢɡ ɭɤɚɡɚɧɢɹ ɱɢɫɥɚ ɜɯɨɞɧɵɯ ɢ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɢ ɱɢɫɥɚ ɩɚɪɚɦɟɬɪɨɜ ɷɥɟɦɟɧɬɚ. ȿɫɥɢ ɭ ɷɥɟɦɟɧɬɚ ɨɬɫɭɬɫɬɜɭɸɬ ɩɚɪɚɦɟɬɪɵ, ɬɨ ɭɤɚɡɚɧɢɟ ɱɢɫɥɚ ɩɚɪɚɦɟɬɪɨɜ ɦɨɠɧɨ ɨɩɭɫɬɢɬɶ. ȼ ɫɥɟɞɭɸɳɟɦ ɪɚɡɞɟɥɟ ɩɪɢɜɟɞɟɧɵ ɷɥɟɦɟɧɬɵ ɤɚɤ ɢɦɟɸɳɢɟ ɩɚɪɚɦɟɬɪɵ (S_Train, Adaptiv_Sum, Square_Sum), ɬɚɤ ɢ ɷɥɟɦɟɧɬɵ ɛɟɡ ɩɚɪɚɦɟɬɪɨɜ (Sum, S_NotTrain, Branch). Ʉɨɧɰɨɦ ɪɚɡɞɟɥɚ ɨɩɢɫɚɧɢɹ ɫɢɝɧɚɥɨɜ ɢ ɩɚɪɚɦɟɬɪɨɜ ɫɥɭɠɢɬ ɨɞɧɨ ɢɡ ɤɥɸɱɟɜɵɯ ɫɥɨɜ ParamType, ParamDef, Forw ɢɥɢ Back. Ɉɩɢɫɚɧɢɟ ɬɢɩɨɜ ɩɚɪɚɦɟɬɪɨɜ ɹɜɥɹɟɬɫɹ ɧɟɨɛɹɡɚɬɟɥɶɧɨɣ ɱɚɫɬɶɸ ɨɩɢɫɚɧɢɹ ɷɥɟɦɟɧɬɚ ɢ ɧɚɱɢɧɚɟɬɫɹ ɫ ɤɥɸɱɟɜɨɝɨ ɫɥɨɜɚ ParamType. ȿɫɥɢ ɪɚɡɞɟɥ ɨɩɢɫɚɧɢɹ ɬɢɩɨɜ ɩɚɪɚɦɟɬɪɨɜ ɨɬɫɭɬɫɬɜɭɟɬ, ɬɨ ɜɫɟ ɩɚɪɚɦɟɬɪɵ ɷɬɨɝɨ ɷɥɟɦɟɧɬɚ ɫɱɢɬɚɸɬɫɹ ɩɚɪɚɦɟɬɪɚɦɢ ɬɢɩɚ DefaultType. ȿɫɥɢ ɜ ɫɟɬɢ ɞɨɥɠɧɵ ɩɪɢɫɭɬɫɬɜɨɜɚɬɶ ɩɚɪɚɦɟɬɪɵ ɪɚɡɧɵɯ ɬɢɩɨɜ (ɧɚɩɪɢɦɟɪ ɫ ɪɚɡɧɵɦɢ ɨɝɪɚɧɢɱɟɧɢɹɦɢ ɧɚ ɦɢɧɢɦɚɥɶɧɨɟ ɢ ɦɚɤɫɢɦɚɥɶɧɨɟ ɡɧɚɱɟɧɢɟ) ɧɟɨɛɯɨɞɢɦɨ ɨɩɢɫɚɬɶ ɬɢɩɵ ɩɚɪɚɦɟɬɪɨɜ. Ʉɨɧɰɨɦ ɷɬɨɝɨ ɪɚɡɞɟɥɚ ɫɥɭɠɢɬ ɨɞɧɨ ɢɡ ɤɥɸɱɟɜɵɯ ɫɥɨɜ ParamDef, Forw ɢɥɢ Back. Ɋɚɡɞɟɥ ɨɩɪɟɞɟɥɟɧɢɹ ɬɢɩɨɜ ɩɚɪɚɦɟɬɪɨɜ ɹɜɥɹɟɬɫɹ ɧɟɨɛɹɡɚɬɟɥɶɧɵɦ ɪɚɡɞɟɥɨɦ ɜ ɨɩɢɫɚɧɢɢ ɷɥɟɦɟɧɬɚ ɢ ɧɚɱɢɧɚɟɬɫɹ ɫ ɤɥɸɱɟɜɨɝɨ ɫɥɨɜɚ ParamDef. ȼ ɤɚɠɞɨɣ ɫɬɪɨɤɟ ɷɬɨɝɨ ɪɚɡɞɟɥɚ ɦɨɠɧɨ ɡɚɞɚɬɶ ɦɢɧɢɦɚɥɶɧɭɸ ɢ ɦɚɫɢɦɚɥɶɧɭɸ ɝɪɚɧɢɰɵ ɢɡɦɟɧɟɧɢɹ ɨɞɧɨɝɨ ɬɢɩɚ ɩɚɪɚɦɟɬɪɨɜ. ȿɫɥɢ ɜ ɨɩɢɫɚɧɢɢ ɫɟɬɢ ɜɫɬɪɟɱɚɸɬɫɹ ɩɚɪɚɦɟɬɪɵ ɧɟɨɩɪɟɞɟɥɟɧɧɨɝɨ ɬɢɩɚ ɬɨ ɷɬɨɬ ɬɢɩ ɫɱɢɬɚɟɬɫɹ ɫɨɜɩɚɞɚɸɳɢɦ ɫ ɬɢɩɨɦ DefaultType. Ɉɩɢɫɚɧɢɟ ɬɢɩɚ ɧɟ ɨɛɹɡɚɧɨ ɩɪɟɞɲɟɫɬɜɨɜɚɬɶ ɨɩɢɫɚɧɢɸ ɩɚɪɚɦɟɬɪɨɜ ɷɬɨɝɨ ɬɢɩɚ. Ɍɚɤ ɧɚɩɪɢɦɟɪ, ɨɩɪɟɞɟɥɟɧɢɟ ɬɢɩɚ ɩɚɪɚɦɟɬɪɨɜ ɦɨɠɟɬ ɧɚɯɨɞɢɬɶɫɹ ɜ ɨɩɢɫɚɧɢɢ ɝɥɚɜɧɨɣ ɫɟɬɢ. Ʉɨɧɰɨɦ ɷɬɨɝɨ ɪɚɡɞɟɥɚ ɫɥɭɠɢɬ ɨɞɧɨ ɢɡ ɤɥɸɱɟɜɵɯ ɫɥɨɜ Forw ɢɥɢ Back. Ɋɚɡɞɟɥ ɨɩɢɫɚɧɢɹ ɦɟɬɨɞɨɜ ɫɨɫɬɨɢɬ ɢɡ ɨɩɢɫɚɧɢɹ ɞɜɭɯ ɦɟɬɨɞɨɜ: Forw ɢ Back. Ɉɩɢɫɚɧɢɟ ɦɟɬɨɞɚ ɫɨɫɬɨɢɬ ɢɡ ɡɚɝɨɥɨɜɤɚ, ɪɚɡɞɟɥɚ ɨɩɢɫɚɧɢɹ ɩɟɪɟɦɟɧɧɵɯ ɢ ɬɟɥɚ ɦɟɬɨɞɚ. Ɂɚɝɨɥɨɜɨɤ ɢɦɟɟɬ ɜɢɞ ɤɥɸɱɟɜɨɝɨ ɫɥɨɜɚ Forw ɢɥɢ Back ɞɥɹ ɫɨɨɬɜɟɬɫɬɜɭɸɳɟɝɨ ɦɟɬɨɞɚ. Ɋɚɡɞɟɥ ɨɩɢɫɚɧɢɹ ɩɟɪɟɦɟɧɧɵɯ ɫɨɫɬɨɢɬ ɢɡ ɤɥɸɱɟɜɨɝɨ ɫɥɨɜɚ Var, ɡɚ ɤɨɬɨɪɵɦ ɫɥɟɞɭɸɬ ɨɩɢɫɚɧɢɹ ɨɞɧɨɬɢɩɧɵɯ ɩɟɪɟɦɟɧɧɵɯ, ɤɚɠɞɨɟ ɢɡ ɤɨɬɨɪɵɯ ɡɚɤɚɧɱɢɜɚɟɬɫɹ ɫɢɦɜɨɥɨɦ «;». ɇɟɨɛɯɨɞɢɦɨ ɩɨɧɢɦɚɬɶ, ɱɬɨ ɨɩɢɫɚɧɢɟ ɡɚɝɨɥɨɜɤɨɜ ɦɟɬɨɞɨɜ ɷɬɨ ɧɟ ɨɩɢɫɚɧɢɟ ɡɚɝɨɥɨɜɤɚ (ɩɪɨɬɨɬɢɩɚ) ɮɭɧɤɰɢɢ, ɜɵɩɨɥɧɹɸɳɟɣ ɬɟɥɨ ɦɟɬɨɞɚ. ɇɢɠɟ ɩɪɢɜɟɞɟɧ ɫɢɧɬɚɤɫɢɫ ɡɚɝɨɥɨɜɤɨɜ ɦɟɬɨɞɨɜ Forw ɢ Back ɧɚ ɦɨɦɟɧɬ ɜɵɡɨɜɚ: Pascal: Procedure Forw( InSignals, OutSignals, Parameters : PRealArray); Procedure Back(InSignals, OutSignals, Parameters, Back.InSignals, Back.OutSignals, Back.Parameters : PRealArray); C void Forw(PRealArray InSignals, PRealArray OutSignals, PRealArray Parameters) void Back(PRealArray InSignals, PRealArray OutSignals, PRealArray Parameters, PRealArray Back.InSignals, PRealArray Back.OutSignals, PRealArray Back.Parameters) ȼ ɦɟɬɨɞɟ Forw ɜ ɥɟɜɨɣ ɱɚɫɬɢ ɨɩɟɪɚɬɨɪɚ ɩɪɢɫɜɚɢɜɚɧɢɹ ɦɨɝɭɬ ɮɢɝɭɪɢɪɨɜɚɬɶ ɢɦɟɧɚ ɥɸɛɵɯ ɩɟɪɟɦɟɧɧɵɯ ɢ ɷɥɟɦɟɧɬɨɜ ɩɪɟɞɨɩɪɟɞɟɥɟɧɧɨɝɨ ɦɚɫɫɢɜɚ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ (OutSignals). ȼ ɜɵɪɚɠɟɧɢɢ, ɫɬɨɹɳɟɦ ɜ ɩɪɚɜɨɣ ɱɚɫɬɢ ɨɩɟɪɚɬɨɪɚ ɩɪɢɫɜɚɢɜɚɧɢɹ ɦɨɝɭɬ ɭɱɚɫɬɜɨɜɚɬɶ ɥɸɛɵɟ ɩɟɪɟɦɟɧɧɵɟ, ɚɪɝɭɦɟɧɬɵ ɷɥɟɦɟɧɬɚ ɢ ɷɥɟɦɟɧɬɵ ɩɪɟɞɨɩɪɟɞɟɥɟɧɧɵɯ ɦɚɫɫɢɜɨɜ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ (InSignals) ɢ ɩɚɪɚɦɟɬɪɨɜ (Parameters). ȼ ɦɟɬɨɞɟ Back ɜ ɥɟɜɨɣ ɱɚɫɬɢ ɨɩɟɪɚɬɨɪɚ ɩɪɢɫɜɚɢɜɚɧɢɹ ɦɨɝɭɬ ɮɢɝɭɪɢɪɨɜɚɬɶ ɢɦɟɧɚ ɥɸɛɵɯ ɩɟɪɟɦɟɧɧɵɯ, ɷɥɟɦɟɧɬɨɜ ɩɪɟɞɨɩɪɟɞɟɥɟɧɧɵɯ ɦɚɫɫɢɜɨɜ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ (Back.InSignals) ɢ ɩɚɪɚɦɟɬɪɨɜ (Back.Parameters). ȼ ɜɵɪɚɠɟɧɢɢ, ɫɬɨɹɳɟɦ ɜ ɩɪɚɜɨɣ ɱɚɫɬɢ ɨɩɟɪɚɬɨɪɚ ɩɪɢɫɜɚɢɜɚɧɢɹ, ɦɨɝɭɬ ɭɱɚɫɬɜɨɜɚɬɶ ɥɸɛɵɟ ɩɟɪɟɦɟɧɧɵɟ, ɚɪɝɭɦɟɧɬɵ ɷɥɟɦɟɧɬɚ ɢ ɷɥɟɦɟɧɬɵ ɩɪɟɞɨɩɪɟɞɟɥɟɧɧɵɯ ɦɚɫɫɢɜɨɜ ɜɯɨɞɧɵɯ (InSignals) ɢ ɜɵɯɨɞɧɵɯ (OutSignals) ɫɢɝɧɚɥɨɜ ɢ ɩɚɪɚɦɟɬɪɨɜ (Parameters).Ɉɬɦɟɬɢɦ ɜɚɠɧɭɸ ɨɫɨɛɟɧɧɨɫɬɶ ɜɵɱɢɫɥɟɧɢɹ ɩɨɩɪɚɜɨɤ ɤ ɩɚɪɚɦɟɬɪɚɦ. ɉɨɫɤɨɥɶɤɭ ɨɞɢɧ ɢ ɬɨɬ ɠɟ ɩɚɪɚɦɟɬɪ ɦɨɠɟɬ ɢɫɩɨɥɶɡɨɜɚɬɶɫɹ ɧɟɫɤɨɥɶɤɢɦɢ ɷɥɟɦɟɧɬɚɦɢ, ɩɪɢ ɜɵɱɢɫɥɟɧɢɢ ɩɨɩɪɚɜɤɢ ɤ ɩɚɪɚɦɟɬɪɭ ɜɵɱɢɫɥɟɧɧɨɟ ɡɧɚɱɟɧɢɟ ɧɭɠɧɨ ɧɟ ɩɪɢɫɜɚɢɜɚɬɶ ɫɨɨɬɜɟɬɫɬɜɭɸɳɟɦɭ ɷɥɟɦɟɧɬɭ ɦɚɫɫɢɜɚ Back.Parameters, ɚ ɞɨɛɚɜɥɹɬɶ. ɉɪɢ ɷɬɨɦ ɜ ɬɟɥɟ ɦɟɬɨɞɚ ɷɥɟɦɟɧɬɵ ɦɚɫɫɢɜɚ Back.Parameters ɧɟ ɦɨɝɭɬ ɮɢɝɭɪɢɪɨɜɚɬɶ ɜ ɩɪɚɜɨɣ ɱɚɫɬɢ ɨɩɟɪɚɬɨɪɚ ɩɪɢɫɜɚɢɜɚɧɢɹ. ɗɬɚ ɨɫɨɛɟɧɧɨɫɬɶ ɜɵɱɢɫɥɟɧɢɹ ɩɨɩɪɚɜɨɤ ɤ ɩɚɪɚɦɟɬɪɚɦ ɨɛɪɚɛɚɬɵɜɚɟɬɫɹ ɤɨɦɩɨɧɟɧɬɨɦ ɫɟɬɶ. Ɉɩɢɫɚɧɢɟ ɷɥɟɦɟɧɬɚ ɡɚɜɟɪɲɚɟɬɫɹ ɤɥɸɱɟɜɵɦ ɫɥɨɜɨɦ End ɡɚ ɤɨɬɨɪɵɦ ɫɥɟɞɭɟɬ ɢɦɹ ɷɥɟɦɟɧɬɚ.
5.3.5.3.4 ɉɪɢɦɟɪ ɨɩɢɫɚɧɢɹ ɷɥɟɦɟɧɬɨɜ NetBibl Elements;
{Ȼɢɛɥɢɨɬɟɤɚ ɷɥɟɦɟɧɬɨɜ}
Element Synaps InSignals 1 OutSignals 1 Parameters 1
{Ɉɛɵɱɧɵɣ ɫɢɧɚɩɫ} {Ɉɞɢɧ ɜɯɨɞɧɨɣ ɫɢɝɧɚɥ} {Ɉɞɢɧ ɜɵɯɨɞɧɨɣ ɫɢɝɧɚɥ} {Ɉɞɢɧ ɩɚɪɚɦɟɬɪ – ɜɟɫ ɫɜɹɡɢ}
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Forw Begin
{ɇɚɱɚɥɨ ɨɩɢɫɚɧɢɹ ɩɪɹɦɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ}
{ȼɵɱɢɫɥɟɧɢɟ ɜɵɯɨɞɧɨɝɨ ɫɢɝɧɚɥɚ ɤɚɤ ɩɪɨɢɡɜɟɞɟɧɢɹ ɜɯɨɞɧɨɝɨ ɫɢɝɧɚɥɚ ɧɚ ɩɚɪɚɦɟɬɪ} OutSignals[1] = InSignals[1] * Parameters[1] End {Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɩɪɹɦɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ} Back Begin
{ɇɚɱɚɥɨ ɨɩɢɫɚɧɢɹ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ }
{ȼɵɱɢɫɥɟɧɢɟ ɩɨɩɪɚɜɤɢ ɤ ɜɯɨɞɧɨɦɭ ɫɢɝɧɚɥɭ ɤɚɤ ɩɪɨɢɡɜɟɞɟɧɢɹ ɩɨɩɪɚɜɤɢ ɤ ɜɵɯɨɞɧɨɦɭ ɫɢɝɧɚɥɭ ɧɚ ɩɚɪɚɦɟɬɪ} Back.InSignals[1] = Back.OutSignals[1] * Parameters[1]; {ȼɵɱɢɫɥɟɧɢɟ ɩɨɩɪɚɜɤɢ ɤ ɩɚɪɚɦɟɬɪɭ ɤɚɤ ɫɭɦɦɵ ɪɚɧɟɟ ɜɵɱɢɫɥɟɧɧɨɣ ɩɨɩɪɚɜɤɢ ɤ ɩɚɪɚɦɟɬɪɭ ɧɚ ɩɪɨɢɡɜɟɞɟɧɢɟ ɩɨɩɪɚɜɤɢ ɤ ɨɛɪɚɬɧɨɦɭ ɫɢɝɧɚɥɭ ɧɚ ɜɯɨɞɧɨɣ ɫɢɝɧɚɥ} Back.Parameters[1] = Back.Parameters[1] + Back.OutSignals[1] * InSignals[1] End {Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ} End Synaps {Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɫɢɧɚɩɫɚ} Element Branch(N : Long) InSignals 1 OutSignals N
{Ɍɨɱɤɚ ɜɟɬɜɥɟɧɢɹ ɧɚ N ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ} {Ɉɞɢɧ ɜɯɨɞɧɨɣ ɫɢɝɧɚɥ} {N ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ}
Forw Var Long I; Begin For I=1 To N Do OutSignals[I] = InSignals[1] End
{ɇɚɱɚɥɨ ɨɩɢɫɚɧɢɹ ɩɪɹɦɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ} {I – ɥɨɤɚɥɶɧɚɹ ɩɟɪɟɦɟɧɧɚɹ ɬɢɩɚ ɞɥɢɧɧɨɟ ɰɟɥɨɟ – ɢɧɞɟɤɫ}
Back Var Long I; Real R; Begin R = 0; For I=1 To N Do R = R + Back.OutSignals[I]; Back. InSignals[1] = R End End Branch
{ɇɚɱɚɥɨ ɨɩɢɫɚɧɢɹ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ } {Ɉɩɢɫɚɧɢɟ ɥɨɤɚɥɶɧɵɯ ɩɟɪɟɦɟɧɧɵɯ} {I – ɞɥɢɧɧɨɟ ɰɟɥɨɟ – ɢɧɞɟɤɫ} {R – ɞɟɣɫɬɜɢɬɟɥɶɧɨɟ – ɞɥɹ ɧɚɤɨɩɥɟɧɢɹ ɫɭɦɦɵ}
{Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ} {Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɬɨɱɤɢ ɜɟɬɜɥɟɧɢɹ}
Element Sum(N Long) InSignals N OutSignals 1
{ɉɪɨɫɬɨɣ ɫɭɦɦɚɬɨɪ ɧɚ N ɜɯɨɞɨɜ} {N ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ} {Ɉɞɢɧ ɜɵɯɨɞɧɨɣ ɫɢɝɧɚɥ}
Forw Var Long I; Real R; Begin R = 0; For I=1 To N Do R = R + InSignals[I]; OutSignals[1] = R End
{ɇɚ ɤɚɠɞɵɣ ɢɡ N ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ} {ɩɟɪɟɞɚɟɦ ɜɯɨɞɧɨɣ ɫɢɝɧɚɥ} {Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɩɪɹɦɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ}
{ɉɨɩɪɚɜɤɚ ɤɨ ɜɯɨɞɧɨɦɭ ɫɢɝɧɚɥɭ ɪɚɜɧɚ} {ɫɭɦɦɟ ɩɨɩɪɚɜɨɤ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ}
{ɇɚɱɚɥɨ ɨɩɢɫɚɧɢɹ ɩɪɹɦɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ} {Ɉɩɢɫɚɧɢɟ ɥɨɤɚɥɶɧɵɯ ɩɟɪɟɦɟɧɧɵɯ} {I – ɞɥɢɧɧɨɟ ɰɟɥɨɟ – ɢɧɞɟɤɫ} {R – ɞɟɣɫɬɜɢɬɟɥɶɧɨɟ – ɞɥɹ ɧɚɤɨɩɥɟɧɢɹ ɫɭɦɦɵ} {ȼɵɯɨɞɧɨɣ ɫɢɝɧɚɥ ɪɚɜɟɧ ɫɭɦɦɟ ɜɯɨɞɧɵɯ} {Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɩɪɹɦɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ}
Back {ɇɚɱɚɥɨ ɨɩɢɫɚɧɢɹ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ} Var Long I; {I – ɥɨɤɚɥɶɧɚɹ ɩɟɪɟɦɟɧɧɚɹ ɬɢɩɚ ɞɥɢɧɧɨɟ ɰɟɥɨɟ – ɢɧɞɟɤɫ} Begin For I=1 To N Do {ɉɨɩɪɚɜɤɚ ɤ ɤɚɠɞɨɦɭ ɜɯɨɞɧɨɦɭ ɫɢɝɧɚɥɭ} Back.InSignals[I] = Back.OutSignals[1] {ɪɚɜɧɚ ɩɨɩɪɚɜɤɟ ɜɵɯɨɞɧɨɝɨ ɫɢɝɧɚɥɚ} End {Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ} End Sum {Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɩɪɨɫɬɨɝɨ ɫɭɦɦɚɬɨɪɚ}
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Element Mul InSignals 2 OutSignals 1
{ɍɦɧɨɠɢɬɟɥɶ} {Ⱦɜɚ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɚ} {Ɉɞɢɧ ɜɵɯɨɞɧɨɣ ɫɢɝɧɚɥ} {ɇɚɱɚɥɨ ɨɩɢɫɚɧɢɹ ɩɪɹɦɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ }
Forw Begin
{ȼɵɯɨɞɧɨɣ ɫɢɝɧɚɥ ɪɚɜɟɧ ɩɪɨɢɡɜɟɞɟɧɢɸ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ} OutSignals[1] = InSignals[1] * InSignals[2] End {Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɩɪɹɦɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ} {ɇɚɱɚɥɨ ɨɩɢɫɚɧɢɹ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ }
Back Begin
{ɉɨɩɪɚɜɤɚ ɤ ɤɚɠɞɨɦɭ ɜɯɨɞɧɨɦɭ ɫɢɝɧɚɥɭ ɪɚɜɧɚ ɩɪɨɢɡɜɟɞɟɧɢɸ ɩɨɩɪɚɜɤɢ ɜɵɯɨɞɧɨɝɨ ɫɢɝɧɚɥɚ ɧɚ ɞɪɭɝɨɣ ɜɯɨɞɧɨɣ ɫɢɝɧɚɥ} Back.InSignals[1] = Back.OutSignals[1] * InSignals[2]; Back.InSignals[2] = Back.OutSignals[1] * InSignals[1] End {Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ} End Mul {Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɭɦɧɨɠɢɬɟɥɹ} Element S_Train InSignals 1 OutSignals 1 Parameters 1
{Ɉɛɭɱɚɟɦɵɣ ɝɢɩɟɪɛɨɥɢɱɟɫɤɢɣ ɫɢɝɦɨɢɞɧɵɣ ɷɥɟɦɟɧɬ} {Ɉɞɢɧ ɜɯɨɞɧɨɣ ɫɢɝɧɚɥ} {Ɉɞɢɧ ɜɵɯɨɞɧɨɣ ɫɢɝɧɚɥ} {Ɉɞɢɧ ɩɚɪɚɦɟɬɪ – ɯɚɪɚɤɬɟɪɢɫɬɢɤɚ} {ɇɚɱɚɥɨ ɨɩɢɫɚɧɢɹ ɩɪɹɦɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ}
Forw Begin
{ȼɵɯɨɞɧɨɣ ɫɢɝɧɚɥ ɪɚɜɟɧ ɨɬɧɨɲɟɧɢɸ ɜɯɨɞɧɨɝɨ ɫɢɝɧɚɥɚ ɤ ɫɭɦɦɟ ɩɚɪɚɦɟɬɪɚ ɢ ɚɛɫɨɥɸɬɧɨɣ ɜɟɥɢɱɢɧɵ ɜɯɨɞɧɨɝɨ ɫɢɝɧɚɥɚ} OutSignals[1] = InSignals[1] / (Parameters[1] + Abs(InSignals[1]) End {Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɩɪɹɦɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ} Back {ɇɚɱɚɥɨ ɨɩɢɫɚɧɢɹ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ} Var Real R; {R – ɞɟɣɫɬɜɢɬɟɥɶɧɨɟ} Begin {R – ɜɫɩɨɦɨɝɚɬɟɥɶɧɚɹ ɜɟɥɢɱɢɧɚ ɞɥɹ ɜɵɱɢɫɥɟɧɢɹ ɩɨɩɪɚɜɨɤ, ɪɚɜɧɚɹ ɨɬɧɨɲɟɧɢɸ ɩɨɩɪɚɜɤɢ ɜɵɯɨɞɧɨɝɨ ɫɢɝɧɚɥɚ ɤ ɤɜɚɞɪɚɬɭ ɫɭɦɦɵ ɩɚɪɚɦɟɬɪɚ ɢ ɚɛɫɨɥɸɬɧɨɣ ɜɟɥɢɱɢɧɵ ɜɯɨɞɧɨɝɨ ɫɢɝɧɚɥɚ} R = Back.OutSignals[1] / Sqr(Parameters[1] + Abs(InSignals[1]); {ɉɨɩɪɚɜɤɚ ɤ ɜɯɨɞɧɨɦɭ ɫɢɝɧɚɥɭ ɪɚɜɧɚ ɩɪɨɢɡɜɟɞɟɧɢɸ ɜɫɩɨɦɨɝɚɬɟɥɶɧɨɣ ɜɟɥɢɱɢɧɵ ɧɚ ɩɚɪɚɦɟɬɪ} Back.InSignals[1] = R * Parameters[1]; {ɉɨɩɪɚɜɤɚ ɤ ɩɚɪɚɦɟɬɪɭ ɪɚɜɧɚ ɫɭɦɦɟ ɪɚɧɟɟ ɜɵɱɢɫɥɟɧɧɨɣ ɜɟɥɢɱɢɧɵ ɩɨɩɪɚɜɤɢ ɢ ɩɪɨɢɡɜɟɞɟɧɢɹ ɜɫɩɨɦɨɝɚɬɟɥɶɧɨɣ ɜɟɥɢɱɢɧɵ ɧɚ ɜɯɨɞɧɨɣ ɫɢɝɧɚɥ} Back.Parameters[1] = Back.Parameters[1] + R * InSignals[1] End {Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ} End S_Train {Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɨɛɭɱɚɟɦɨɝɨ ɝɢɩɟɪɛɨɥɢɱɟɫɤɨɝɨ ɫɢɝɦɨɢɞɧɨɝɨ ɷɥɟɦɟɧɬɚ} Element S_NotTrain( Char : Real) InSignals 1 OutSignals 1
{ɇɟ ɨɛɭɱɚɟɦɵɣ ɝɢɩɟɪɛɨɥɢɱɟɫɤɢɣ ɫɢɝɦɨɢɞɧɵɣ ɷɥɟɦɟɧɬ} {Char – ɯɚɪɚɤɬɟɪɢɫɬɢɤɚ} {Ɉɞɢɧ ɜɯɨɞɧɨɣ ɫɢɝɧɚɥ} {Ɉɞɢɧ ɜɵɯɨɞɧɨɣ ɫɢɝɧɚɥ} {ɇɚɱɚɥɨ ɨɩɢɫɚɧɢɹ ɩɪɹɦɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ}
Forw Begin
{ȼɵɯɨɞɧɨɣ ɫɢɝɧɚɥ ɪɚɜɟɧ ɨɬɧɨɲɟɧɢɸ ɜɯɨɞɧɨɝɨ ɫɢɝɧɚɥɚ ɤ ɫɭɦɦɟ ɯɚɪɚɤɬɟɪɢɫɬɢɤɢ ɢ ɚɛɫɨɥɸɬɧɨɣ ɜɟɥɢɱɢɧɵ ɜɯɨɞɧɨɝɨ ɫɢɝɧɚɥɚ} OutSignals[1] = InSignals[1] / (Char + Abs(InSignals[1]) End {Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɩɪɹɦɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ} {ɇɚɱɚɥɨ ɨɩɢɫɚɧɢɹ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ}
Back Begin
{ɉɨɩɪɚɜɤɚ ɤ ɜɯɨɞɧɨɦɭ ɫɢɝɧɚɥɭ ɪɚɜɧɚ ɨɬɧɨɲɟɧɢɸ ɩɪɨɢɡɜɟɞɟɧɢɹ ɩɨɩɪɚɜɤɢ ɜɵɯɨɞɧɨɝɨ ɫɢɝɧɚɥɚ ɧɚ ɯɚɪɚɤɬɟɪɢɫɬɢɤɭ ɤ ɤɜɚɞɪɚɬɭ ɫɭɦɦɵ ɯɚɪɚɤɬɟɪɢɫɬɢɤɢ ɢ ɚɛɫɨɥɸɬɧɨɣ
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ɜɟɥɢɱɢɧɵ ɜɯɨɞɧɨɝɨ ɫɢɝɧɚɥɚ} Back.InSignals[1] = Back.OutSignals[1] * Char / Sqr(Char + Abs(InSignals[1]); End {Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ} End S_NotTrain {Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɧɟ ɨɛɭɱɚɟɦɨɝɨ ɝɢɩɟɪɛɨɥɢɱɟɫɤɨɝɨ ɫɢɝɦɨɢɞɧɨɝɨ ɷɥɟɦɟɧɬɚ} Element Pade(Char : Real) InSignals 2 OutSignals 1
{ɉɚɞɟ ɩɪɟɨɛɪɚɡɨɜɚɬɟɥɶ Char – ɯɚɪɚɤɬɟɪɢɫɬɢɤɚ} {Ⱦɜɚ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɚ} {Ɉɞɢɧ ɜɵɯɨɞɧɨɣ ɫɢɝɧɚɥ}
Forw Begin
{ɇɚɱɚɥɨ ɨɩɢɫɚɧɢɹ ɩɪɹɦɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ}
{ȼɵɯɨɞɧɨɣ ɫɢɝɧɚɥ ɪɚɜɟɧ ɨɬɧɨɲɟɧɢɸ ɩɟɪɜɨɝɨ ɜɯɨɞɧɨɝɨ ɫɢɝɧɚɥɚ ɤ ɫɭɦɦɟ ɯɚɪɚɤɬɟɪɢɫɬɢɤɢ ɢ ɜɬɨɪɨɝɨ ɜɯɨɞɧɨɝɨ ɫɢɝɧɚɥɚ} OutSignals[1] = InSignals[1] / (Char+ InSignals[2]) End {Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɩɪɹɦɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ} Back {ɇɚɱɚɥɨ ɨɩɢɫɚɧɢɹ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ} Var Real R; {R – ɞɟɣɫɬɜɢɬɟɥɶɧɨɟ} Begin {ȼɫɩɨɦɨɝɚɬɟɥɶɧɚɹ ɜɟɥɢɱɢɧɚ ɪɚɜɧɚ ɩɨɩɪɚɜɤɟ ɤ ɩɟɪɜɨɦɭ ɜɯɨɞɧɨɦɭ ɫɢɝɧɚɥɭ – ɨɬɧɨɲɟɧɢɸ ɩɨɩɪɚɜɤɢ ɜɵɯɨɞɧɨɝɨ ɫɢɝɧɚɥɚ ɤ ɫɭɦɦɟ ɯɚɪɚɤɬɟɪɢɫɬɢɤɢ ɢ ɜɬɨɪɨɝɨ ɜɯɨɞɧɨɝɨ ɫɢɝɧɚɥɚ} R = Back.OutSignals[1] / (Char + InSignals[2]); Back.InSignals[1] = R; {ɉɨɩɪɚɜɤɚ ɤɨ ɜɬɨɪɨɦɭ ɜɯɨɞɧɨɦɭ ɫɢɝɧɚɥɭ ɪɚɜɧɚ ɦɢɧɭɫ ɨɬɧɨɲɟɧɢɸ ɩɪɨɢɡɜɟɞɟɧɢɹ ɩɟɪɜɨɝɨ ɜɯɨɞɧɨɝɨ ɫɢɝɧɚɥɚ ɧɚ ɩɨɩɪɚɜɤɭ ɜɵɯɨɞɧɨɝɨ ɫɢɝɧɚɥɚ ɤ ɤɜɚɞɪɚɬɭ ɫɭɦɦɵ ɯɚɪɚɤɬɟɪɢɫɬɢɤɢ ɢ ɜɬɨɪɨɝɨ ɜɯɨɞɧɨɝɨ ɫɢɝɧɚɥɚ} Back.InSignals[2] = -R * OutSignals[1]; End {Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ} End Pade {Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɉɚɞɟ ɩɪɟɨɛɪɚɡɨɜɚɬɟɥɹ} Element Sign_Mirror InSignals 1 OutSignals 1 Forw Begin If InSignals[1] > 0 Then OutSignals[1] = 1 Else OutSignals[1] = 0 End
{Ɂɟɪɤɚɥɶɧɵɣ ɩɨɪɨɝɨɜɵɣ ɷɥɟɦɟɧɬ} {Ɉɞɢɧ ɜɯɨɞɧɨɣ ɫɢɝɧɚɥ} {Ɉɞɢɧ ɜɵɯɨɞɧɨɣ ɫɢɝɧɚɥ} {ɇɚɱɚɥɨ ɨɩɢɫɚɧɢɹ ɩɪɹɦɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ } {ȼɵɯɨɞɧɨɣ ɫɢɝɧɚɥ ɪɚɜɟɧ 1, ɟɫɥɢ ɜɯɨɞɧɨɣ ɫɢɝɧɚɥ} {ɛɨɥɶɲɟ ɧɭɥɹ, ɢ ɧɭɥɸ ɜ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ} {Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɩɪɹɦɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ}
Back {ɇɚɱɚɥɨ ɨɩɢɫɚɧɢɹ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ} Begin Back.InSignals[1] = OutSignals[1]; {ɉɨɩɪɚɜɤɚ ɤ ɜɯɨɞɧɨɦɭ ɫɢɝɧɚɥɭ ɪɚɜɧɚ ɜɵɯɨɞɧɨɦɭ ɫɢɝɧɚɥɭ} End {Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ} End Sign_Mirror {Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɡɟɪɤɚɥɶɧɨɝɨ ɩɨɪɨɝɨɜɨɝɨ ɷɥɟɦɟɧɬɚ} Element Sign_ Easy InSignals 1 OutSignals 1 Forw Begin If InSignals[1] > 0 Then OutSignals[1] = 1 Else OutSignals[1] = 0 End Back Begin
{ɉɪɨɡɪɚɱɧɵɣ ɩɨɪɨɝɨɜɵɣ ɷɥɟɦɟɧɬ} {Ɉɞɢɧ ɜɯɨɞɧɨɣ ɫɢɝɧɚɥ} {Ɉɞɢɧ ɜɵɯɨɞɧɨɣ ɫɢɝɧɚɥ} {ɇɚɱɚɥɨ ɨɩɢɫɚɧɢɹ ɩɪɹɦɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ } {ȼɵɯɨɞɧɨɣ ɫɢɝɧɚɥ ɪɚɜɟɧ 1, ɟɫɥɢ ɜɯɨɞɧɨɣ ɫɢɝɧɚɥ} {ɛɨɥɶɲɟ ɧɭɥɹ, ɢ ɧɭɥɸ ɜ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ} {Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɩɪɹɦɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ} {ɇɚɱɚɥɨ ɨɩɢɫɚɧɢɹ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ}
{ɉɨɩɪɚɜɤɚ ɤ ɜɯɨɞɧɨɦɭ ɫɢɝɧɚɥɭ ɪɚɜɧɚ ɩɨɩɪɚɜɤɟ ɤ ɜɵɯɨɞɧɨɦɭ ɫɢɝɧɚɥɭ} Back.InSignals[1] = Back.OutSignals[1]; End {Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ} End Sign_Easy {Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɩɪɨɡɪɚɱɧɨɝɨ ɩɨɪɨɝɨɜɨɝɨ ɷɥɟɦɟɧɬɚ}
CHAP5-3.DOC
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Element Adaptiv_Sum( N : Long) InSignals N OutSignals 1 Parameters N
{Ⱥɞɚɩɬɢɜɧɵɣ ɫɭɦɦɚɬɨɪ ɧɚ N ɜɯɨɞɨɜ} {N ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ} {Ɉɞɢɧ ɜɵɯɨɞɧɨɣ ɫɢɝɧɚɥ} {N ɩɚɪɚɦɟɬɪɨɜ – ɜɟɫɨɜ ɫɜɹɡɟɣ}
Forw Var Long I; Real R; Begin R = 0; For I=1 To N Do R = R + InSignals[I] * Parameters[I]; OutSignals[1] = R End
{ɇɚɱɚɥɨ ɨɩɢɫɚɧɢɹ ɩɪɹɦɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ} {Ɉɩɢɫɚɧɢɟ ɥɨɤɚɥɶɧɵɯ ɩɟɪɟɦɟɧɧɵɯ} {I – ɞɥɢɧɧɨɟ ɰɟɥɨɟ – ɢɧɞɟɤɫ} {R – ɞɟɣɫɬɜɢɬɟɥɶɧɨɟ – ɞɥɹ ɧɚɤɨɩɥɟɧɢɹ ɫɭɦɦɵ} {ȼɵɯɨɞɧɨɣ ɫɢɝɧɚɥ ɪɚɜɟɧ ɫɤɚɥɹɪɧɨɦɭ } {ɩɪɨɢɡɜɟɞɟɧɢɸ ɦɚɫɫɢɜɚ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ} {ɧɚ ɦɚɫɫɢɜ ɩɚɪɚɦɟɬɪɨɜ} {Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ}
Back {ɇɚɱɚɥɨ ɨɩɢɫɚɧɢɹ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ} Var Long I; {I – ɥɨɤɚɥɶɧɚɹ ɩɟɪɟɦɟɧɧɚɹ ɬɢɩɚ} Begin {ɞɥɢɧɧɨɟ ɰɟɥɨɟ – ɢɧɞɟɤɫ} For I=1 To N Do Begin {ɉɨɩɪɚɜɤɚ ɤ I-ɭ ɜɯɨɞɧɨɦɭ ɫɢɝɧɚɥɭ ɪɚɜɧɚ ɫɭɦɦɟ ɪɚɧɟɟ ɜɵɱɢɫɥɟɧɧɨɣ ɩɨɩɪɚɜɤɢ ɢ ɩɪɨɢɡɜɟɞɟɧɢɹ ɩɨɩɪɚɜɤɢ ɜɵɯɨɞɧɨɝɨ ɫɢɝɧɚɥɚ ɧɚ I-ɣ ɩɚɪɚɦɟɬɪ} Back.InSignals[I] = Back.OutSignals[1] * Parameters[I]; {ɉɨɩɪɚɜɤɚ ɤ I-ɭ ɩɚɪɚɦɟɬɪɭ ɪɚɜɧɚ ɩɪɨɢɡɜɟɞɟɧɢɸ ɩɨɩɪɚɜɤɢ ɜɵɯɨɞɧɨɝɨ ɫɢɝɧɚɥɚ ɧɚ I-ɣ ɜɯɨɞɧɨɣ ɫɢɝɧɚɥ} Back. Parameters[I] = Back. Parameters[I] + Back.OutSignals[1] * InSignals[I] End End {Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ} End Adaptiv_Sum {Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɚɞɚɩɬɢɜɧɨɝɨ ɫɭɦɦɚɬɨɪɚ} Element Adaptiv_Sum_Plus ( N : Long) InSignals N OutSignals 1 Parameters N+1
{Ⱥɞɚɩɬɢɜɧɵɣ ɧɟɨɞɧɨɪɨɞɧɵɣ ɫɭɦɦɚɬɨɪ ɧɚ N ɜɯɨɞɨɜ} {N ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ} {Ɉɞɢɧ ɜɵɯɨɞɧɨɣ ɫɢɝɧɚɥ} {N+1 ɩɚɪɚɦɟɬɪ – ɜɟɫɚ ɫɜɹɡɟɣ}
Forw Var Long I; Real R; Begin R = Parameters[N+1]; For I=1 To N Do R = R + InSignals[I] * Parameters[I]; OutSignals[1] = R End
{ɇɚɱɚɥɨ ɨɩɢɫɚɧɢɹ ɩɪɹɦɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ } {Ɉɩɢɫɚɧɢɟ ɥɨɤɚɥɶɧɵɯ ɩɟɪɟɦɟɧɧɵɯ} {I – ɞɥɢɧɧɨɟ ɰɟɥɨɟ – ɢɧɞɟɤɫ} {R – ɞɟɣɫɬɜɢɬɟɥɶɧɨɟ – ɞɥɹ ɧɚɤɨɩɥɟɧɢɹ ɫɭɦɦɵ} {ȼɵɯɨɞɧɨɣ ɫɢɝɧɚɥ ɪɚɜɟɧ ɫɭɦɦɟ N+1 ɩɚɪɚɦɟɬɪɚ} {ɢ ɫɤɚɥɹɪɧɨɝɨ ɩɪɨɢɡɜɟɞɟɧɢɹ ɦɚɫɫɢɜɚ ɜɯɨɞɧɵɯ} {ɫɢɝɧɚɥɨɜ ɧɚ ɦɚɫɫɢɜ ɩɚɪɚɦɟɬɪɨɜ} {Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɩɪɹɦɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ}
Back {ɇɚɱɚɥɨ ɨɩɢɫɚɧɢɹ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ } Var Long I; {I – ɥɨɤɚɥɶɧɚɹ ɩɟɪɟɦɟɧɧɚɹ ɬɢɩɚ} Begin {ɞɥɢɧɧɨɟ ɰɟɥɨɟ – ɢɧɞɟɤɫ} For I=1 To N Do Begin {ɉɨɩɪɚɜɤɚ ɤ I-ɭ ɜɯɨɞɧɨɦɭ ɫɢɝɧɚɥɭ ɪɚɜɧɚ ɩɪɨɢɡɜɟɞɟɧɢɸ ɩɨɩɪɚɜɤɢ ɜɵɯɨɞɧɨɝɨ ɫɢɝɧɚɥɚ ɧɚ I-ɣ ɩɚɪɚɦɟɬɪ} Back.InSignals[I] = Back.OutSignals[1] * Parameters[I]; {ɉɨɩɪɚɜɤɚ ɤ I-ɭ ɩɚɪɚɦɟɬɪɭ ɪɚɜɧɚ ɫɭɦɦɟ ɪɚɧɟɟ ɜɵɱɢɫɥɟɧɧɨɣ ɩɨɩɪɚɜɤɢ ɢ ɩɪɨɢɡɜɟɞɟɧɢɹ ɩɨɩɪɚɜɤɢ ɜɵɯɨɞɧɨɝɨ ɫɢɝɧɚɥɚ ɧɚ I-ɣ ɜɯɨɞɧɨɣ ɫɢɝɧɚɥ} Back. Parameters[I] = Back. Parameters[I] + Back.OutSignals[1] * InSignals[I] End; {ɉɨɩɪɚɜɤɚ ɤ (N+1)-ɭ ɩɚɪɚɦɟɬɪɭ ɪɚɜɧɚ ɫɭɦɦɟ ɪɚɧɟɟ ɜɵɱɢɫɥɟɧɧɨɣ ɩɨɩɪɚɜɤɢ ɢ ɩɨɩɪɚɤɢ ɤ ɜɵɯɨɞɧɨɦɭ ɫɢɝɧɚɥɭ} Back.Parameters[N+1] = Back.Parameters[N+1] + Back.OutSignals[1]
CHAP5-3.DOC
95
End End Adaptiv_Sum_Plus Element Square_Sum( N : Long) InSignals N OutSignals 1 Parameters (Sqr(N) + N) Div 2
{Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ} {Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɧɟɨɞɧɨɪɨɞɧɨɝɨ ɚɞɚɩɬɢɜɧɨɝɨ ɫɭɦɦɚɬɨɪɚ} {Ʉɜɚɞɪɚɬɢɱɧɵɣ ɫɭɦɦɚɬɨɪ ɧɚ N ɜɯɨɞɨɜ} {N ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ} {Ɉɞɢɧ ɜɵɯɨɞɧɨɣ ɫɢɝɧɚɥ} {N(N+1)/2 ɩɚɪɚɦɟɬɪɨɜ – ɜɟɫɨɜ ɫɜɹɡɟɣ}
Forw {ɇɚɱɚɥɨ ɨɩɢɫɚɧɢɹ ɩɪɹɦɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ} Var {Ɉɩɢɫɚɧɢɟ ɥɨɤɚɥɶɧɵɯ ɩɟɪɟɦɟɧɧɵɯ} Long I,J,K; {I,J,K – ɩɟɪɟɦɟɧɧɵɟ ɬɢɩɚ ɞɥɢɧɧɨɟ ɰɟɥɨɟ } Real R; {R – ɞɟɣɫɬɜɢɬɟɥɶɧɨɟ – ɞɥɹ ɧɚɤɨɩɥɟɧɢɹ ɫɭɦɦɵ} Begin K = 1; {K – ɧɨɦɟɪ ɨɛɪɚɛɚɬɵɜɚɟɦɨɝɨ ɩɚɪɚɦɟɬɪɚ} R = 0; For I = 1 To N Do {I,J – ɧɨɦɟɪɚ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ} For J = I To N Do Begin R = R + InSignals[I] * InSignals[J] * Parameters[K]; K=K+1 End; {ȼɵɯɨɞɧɨɣ ɫɢɝɧɚɥ ɪɚɜɟɧ ɫɭɦɦɟ ɜɫɟɯ ɩɨɩɚɪɧɵɯ ɩɪɨɢɡɜɟɞɟɧɢɣ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ, ɭɦɧɨɠɟɧɧɵɯ ɧɚ ɫɨɨɬɜɟɬɫɬɜɭɸɳɢɟ ɩɚɪɚɦɟɬɪɵ} OutSignals[1] = R End {Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɩɪɹɦɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ} Back {ɇɚɱɚɥɨ ɨɩɢɫɚɧɢɹ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ } Var {Ɉɩɢɫɚɧɢɟ ɥɨɤɚɥɶɧɵɯ ɩɟɪɟɦɟɧɧɵɯ} Long I, J, K; {I,J,K – ɩɟɪɟɦɟɧɧɵɟ ɬɢɩɚ ɞɥɢɧɧɨɟ ɰɟɥɨɟ } Real R; {R – ɞɟɣɫɬɜɢɬɟɥɶɧɨɟ} Vector W; {Ɇɚɫɫɢɜ ɞɥɹ ɧɚɤɨɩɥɟɧɢɹ ɩɪɨɦɟɠɭɬɨɱɧɵɯ ɜɟɥɢɱɢɧ} Begin For I = 1 To N Do W[I] = 0; K = 1; {K – ɧɨɦɟɪ ɨɛɪɚɛɚɬɵɜɚɟɦɨɝɨ ɩɚɪɚɦɟɬɪɚ} For I = 1 To N Do For J = I To N Do Begin {ɉɨɩɪɚɜɤɚ ɤ ɩɚɪɚɦɟɬɪɭ ɪɚɜɧɚ ɫɭɦɦɟ ɪɚɧɟɟ ɜɵɱɢɫɥɟɧɧɨɣ ɩɨɩɪɚɜɤɢ ɢ ɩɪɨɢɡɜɟɞɟɧɢɹ ɩɨɩɪɚɜɤɢ ɤ ɜɯɨɞɧɨɦɭ ɫɢɝɧɚɥɭ ɧɚ ɩɪɨɢɡɜɟɞɟɧɢɟ ɫɢɝɧɚɥɨɜ, ɩɪɨɲɟɞɲɢɯ ɱɟɪɟɡ ɷɬɨɬ ɩɚɪɚɦɟɬɪ ɩɪɢ ɩɪɹɦɨɦ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɢ} Back.Parameters[K] = Back.Parameters[K] + Back.OutSignals[1] * InSignals[I] * InSignals[J]; R = Back.OutSignals[1] * Parameters[K]; W[I] = W[I] + R * InSignals[J]; W[J] = W[J] + R * InSignals[I]; K=K+1 End; For I = 1 To N Do {ɉɨɩɪɚɜɤɚ ɤ ɜɯɨɞɧɨɦɭ ɫɢɝɧɚɥɭ ɪɚɜɧɚ ɩɪɨɢɡɜɟɞɟɧɢɸ ɩɨɩɪɚɜɤɢ ɤ ɜɵɯɨɞɧɨɦɭ ɫɢɝɧɚɥɭ ɧɚ ɫɭɦɦɭ ɜɫɟɯ ɩɚɪɚɦɟɬɪɨɜ, ɱɟɪɟɡ ɤɨɬɨɪɵɟ ɷɬɨɬ ɫɢɝɧɚɥ ɩɪɨɯɨɞɢɥ ɩɪɢ ɩɪɹɦɨɦ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɢ, ɭɦɧɨɠɟɧɧɵɯ ɧɚ ɞɪɭɝɢɟ ɜɯɨɞɧɵɟ ɫɢɝɧɚɥɵ, ɬɚɤ ɠɟ ɩɪɨɲɟɞɲɢɟ ɱɟɪɟɡ ɷɬɢ ɩɚɪɚɦɟɬɪɵ ɩɪɢ ɩɪɹɦɨɦ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɢ} Back.InSignals[1] = W[I] End {Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɩɪɹɦɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ} End Square_Sum {Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɤɜɚɞɪɚɬɢɱɧɨɝɨ ɫɭɦɦɚɬɨɪɚ} Element Square_Sum_Plus( N : Long) InSignals N OutSignals 1 Parameters (Sqr(N) + 3 * N) Div 2 + 1 Forw
CHAP5-3.DOC
{Ⱥɞɚɩɬɢɜɧɵɣ ɤɜɚɞɪɚɬɢɱɧɵɣ ɫɭɦɦɚɬɨɪ ɧɚ N ɜɯɨɞɨɜ} {N ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ} {Ɉɞɢɧ ɜɵɯɨɞɧɨɣ ɫɢɝɧɚɥ} {N(N+3)/2+1 ɩɚɪɚɦɟɬɪɨɜ – ɜɟɫɨɜ ɫɜɹɡɟɣ} {ɇɚɱɚɥɨ ɨɩɢɫɚɧɢɹ ɩɪɹɦɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ}
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Var {Ɉɩɢɫɚɧɢɟ ɥɨɤɚɥɶɧɵɯ ɩɟɪɟɦɟɧɧɵɯ} Long I, J, K; {I,J,K – ɩɟɪɟɦɟɧɧɵɟ ɬɢɩɚ ɞɥɢɧɧɨɟ ɰɟɥɨɟ } Real R; {R – ɞɟɣɫɬɜɢɬɟɥɶɧɨɟ – ɞɥɹ ɧɚɤɨɩɥɟɧɢɹ ɫɭɦɦɵ} Begin K = 2 * N+1; {K – ɧɨɦɟɪ ɨɛɪɚɛɚɬɵɜɚɟɦɨɝɨ ɩɚɪɚɦɟɬɪɚ} R = Parameters[Sqr(N) + 3 * N) Div 2 + 1]; For I = 1 To N Do Begin R = R + InSignals[I] * Parameters[I] + Sqr(InSignals[I]) * Parameters[N + I]; For J = I + 1 To N Do Begin R = R + InSignals[I] * InSignals[J] * Parameters[K]; K=K+1 End End {ȼɵɯɨɞɧɨɣ ɫɢɝɧɚɥ ɪɚɜɟɧ ɫɭɦɦɟ ɜɫɟɯ ɩɨɩɚɪɧɵɯ ɩɪɨɢɡɜɟɞɟɧɢɣ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ, ɭɦɧɨɠɟɧɧɵɯ ɧɚ ɫɨɨɬɜɟɬɫɬɜɭɸɳɢɟ ɩɚɪɚɦɟɬɪɵ, ɩɥɸɫ ɫɭɦɦɟ ɜɫɟɯ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɭɦɧɨɠɟɧɧɵɯ ɧɚ ɫɨɨɬɜɟɬɫɬɜɭɸɳɢɟ ɩɚɪɚɦɟɬɪɵ, ɩɥɸɫ ɩɨɫɥɟɞɧɢɣ ɩɚɪɚɦɟɬɪ} OutSignals[1] = R End {Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɩɪɹɦɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ} Back {ɇɚɱɚɥɨ ɨɩɢɫɚɧɢɹ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ } Var {Ɉɩɢɫɚɧɢɟ ɥɨɤɚɥɶɧɵɯ ɩɟɪɟɦɟɧɧɵɯ} Long I, J, K; {I,J,K – ɩɟɪɟɦɟɧɧɵɟ ɬɢɩɚ ɞɥɢɧɧɨɟ ɰɟɥɨɟ } Real R; {R – ɞɟɣɫɬɜɢɬɟɥɶɧɨɟ – ɞɥɹ ɧɚɤɨɩɥɟɧɢɹ ɫɭɦɦɵ} Vector W; {Ɇɚɫɫɢɜ ɞɥɹ ɧɚɤɨɩɥɟɧɢɹ ɩɪɨɦɟɠɭɬɨɱɧɵɯ ɜɟɥɢɱɢɧ} Begin For I = 1 To N Do W[I] = 0; K = 2 * N + 1; {K – ɧɨɦɟɪ ɨɛɪɚɛɚɬɵɜɚɟɦɨɝɨ ɩɚɪɚɦɟɬɪɚ} For I = 1 To N Do Begin Back.Parameters[I] = Back.Parameters[I] + Back.OutSignals[1] * InSignals[I]; Back.Parameters[N + I] = Back.Parameters[N + I] + Back.OutSignals[1] * Sqr(InSignals[I]); W[I] = W[I] + Back.OutSignals[1] * (Parameters[I] + 2 * Parameters[N + I] * InSignals[I]) For J = I + 1 To N Do Begin Back.Parameters[K] = Back.Parameters[K] + Back.OutSignals[1] * InSignals[I] * InSignals[J]; R = Back.OutSignals[1] * Parameters[K]; W[I] = W[I] + R * InSignals[J]; W[J] = W[J] + R * InSignals[I]; K=K+1 End End; For I = 1 To N Do Back.InSignals[1] = W[I] End {Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ} End Square_Sum_Plus {Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɚɞɚɩɬɢɜɧɨɝɨ ɤɜɚɞɪɚɬɢɱɧɨɝɨ ɫɭɦɦɚɬɨɪɚ} End NetBibl
5.3.5.3.5 Ɉɩɢɫɚɧɢɟ ɛɥɨɤɨɜ Ɉɩɢɫɚɧɢɟ ɛɥɨɤɚ ɫɨɫɬɨɢɬ ɢɡ ɩɹɬɢ ɨɫɧɨɜɧɵɯ ɪɚɡɞɟɥɨɜ: ɡɚɝɨɥɨɜɤɚ ɨɩɢɫɚɧɢɹ ɛɥɨɤɚ, ɨɩɢɫɚɧɢɹ ɫɢɝɧɚɥɨɜ ɢ ɩɚɪɚɦɟɬɪɨɜ, ɨɩɢɫɚɧɢɹ ɫɨɫɬɚɜɚ, ɨɩɢɫɚɧɢɹ ɫɜɹɡɟɣ ɢ ɤɨɧɰɚ ɨɩɢɫɚɧɢɹ ɛɥɨɤɚ. ɋɭɳɟɫɬɜɭɟɬ ɞɜɚ ɬɢɩɚ ɛɥɨɤɨɜ – ɤɚɫɤɚɞ ɢ ɫɥɨɣ (Layer). Ɋɚɡɥɢɱɢɟ ɦɟɠɞɭ ɷɬɢɦɢ ɞɜɭɦɹ ɬɢɩɚɦɢ ɛɥɨɤɨɜ ɫɨɫɬɨɢɬ ɜ ɬɨɦ, ɱɬɨ ɩɨɞɫɟɬɢ, ɜɯɨɞɹɳɢɟ ɜ ɫɨɫɬɚɜ ɫɥɨɹ, ɮɭɧɤɰɢɨɧɢɪɭɸɬ ɩɚɪɚɥɥɟɥɶɧɨ ɢ ɧɟɡɚɜɢɫɢɦɨ ɞɪɭɝ ɨɬ ɞɪɭɝɚ, ɬɨɝɞɚ ɤɚɤ ɫɨɫɬɚɜɥɹɸɳɢɟ ɤɚɫɤɚɞ ɩɨɞɫɟɬɢ ɮɭɧɤɰɢɨɧɢɪɭɸɬ ɩɨɫɥɟɞɨɜɚɬɟɥɶɧɨ, ɩɪɢɱɟɦ ɤɚɠɞɚɹ ɫɥɟɞɭɸɳɚɹ ɩɨɞɫɟɬɶ ɢɫɩɨɥɶɡɭɟɬ ɪɟɡɭɥɶɬɚɬɵ ɪɚɛɨɬɵ ɩɪɟɞɵɞɭɳɢɯ ɩɨɞɫɟɬɟɣ. ȼ ɫɜɨɸ ɨɱɟɪɟɞɶ ɫɭɳɟɫɬɜɭɟɬ ɬɪɢ ɜɢɞɚ ɤɚɫɤɚɞɨɜ – ɩɪɨɫɬɨɣ ɤɚɫɤɚɞ (Cascad), ɰɢɤɥ ɫ ɮɢɤɫɢɪɨɜɚɧɧɵɦ ɱɢɫɥɨɦ ɲɚɝɨɜ (Loop) ɰɢɤɥ ɩɨ ɭɫɥɨɜɢɸ (Until). Ɋɚɡɥɢɱɢɟ ɦɟɠɞɭ ɬɪɟɦɹ ɜɢɞɚɦɢ ɤɚɫɤɚɞɨɜ ɨɱɟɜɢɞɧɨ – ɩɪɨɫɬɨɣ ɤɚɫɤɚɞ ɮɭɧɤɰɢɨɧɢɪɭɟɬ ɨɞɢɧ ɪɚɡ, ɰɢɤɥ Loop ɮɭɧɤɰɢɨɧɢɪɭɟɬ ɭɤɚɡɚɧɧɨɟ ɜ ɨɩɢɫɚɧɢɢ ɱɢɫɥɨ ɪɚɡ, ɚ ɰɢɤɥ Until ɮɭɧɤɰɢɨɧɢɪɭɟɬ ɞɨ ɬɟɯ ɩɨɪ, ɩɨɤɚ ɧɟ ɜɵɩɨɥɧɢɬɫɹ ɭɤɚɡɚɧɧɨɟ ɜ ɨɩɢɫɚɧɢɢ ɭɫɥɨɜɢɟ. ȼ ɭɫɥɨɜɢɢ, ɭɤɚɡɵɜɚɟɦɨɦ ɜ ɡɚɝɨɥɨɜɤɟ ɰɢɤɥɚ Until, ɜɨɡɦɨɠɧɨ ɢɫɩɨɥɶɡɨɜɚɧɢɟ ɫɪɚɜɧɟɧɢɣ ɦɚɫɫɢɜɨɜ ɢɥɢ ɢɧɬɟɪɜɚɥɨɜ ɦɚɫɫɢɜɨɜ ɫɢɝɧɚɥɨɜ. ɇɚɩɪɢɦɟɪ, ɡɚɩɢɫɶ
CHAP5-3.DOC
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InSignals=OutSignals ɷɤɜɢɜɚɥɟɧɬɧɚ ɫɥɟɞɭɸɳɟɣ ɡɚɩɢɫɢ InSignals[1..N]=OutSignals[1..N] ɤɨɬɨɪɚɹ ɷɤɜɢɜɚɥɟɧɬɧɚ ɜɵɱɢɫɥɟɧɢɸ ɫɥɟɞɭɸɳɟɣ ɥɨɝɢɱɟɫɤɨɣ ɮɭɧɤɰɢɢ: Function Equal(InSignals, OutSignals : RealArray) : Logic; Var Long I; Logic L Begin L = True For I = 1 To N Do L = L And (InSignals[I] = OutSignals[I]); Equal = L End Ɋɚɡɞɟɥ ɨɩɢɫɚɧɢɹ ɫɨɫɬɚɜɚ ɫɥɟɞɭɟɬ ɫɪɚɡɭ ɩɨɫɥɟ ɡɚɝɨɥɨɜɤɚ ɛɥɨɤɚ ɡɚ ɪɚɡɞɟɥɨɦ ɨɩɢɫɚɧɢɹ ɫɢɝɧɚɥɨɜ ɢ ɩɚɪɚɦɟɬɪɨɜ ɢ ɧɚɱɢɧɚɟɬɫɹ ɫ ɤɥɸɱɟɜɨɝɨ ɫɥɨɜɚ Contents, ɡɚ ɤɨɬɨɪɵɦ ɫɥɟɞɭɸɬ ɢɦɟɧɚ ɩɨɞɫɟɬɟɣ (ɛɥɨɤɨɜ ɢɥɢ ɷɥɟɦɟɧɬɨɜ) ɫɨ ɫɩɢɫɤɚɦɢ ɮɚɤɬɢɱɟɫɤɢɯ ɚɪɝɭɦɟɧɬɨɜ, ɪɚɡɞɟɥɟɧɧɵɟ ɡɚɩɹɬɵɦɢ. ȼɫɟ ɢɦɟɧɚ ɩɨɞɫɟɬɟɣ ɞɨɥɠɧɵ ɩɪɟɞɜɚɪɹɬɶɫɹ ɩɫɟɜɞɨɧɢɦɚɦɢ. ȼ ɞɚɥɶɧɟɣɲɟɦ ɭɤɚɡɚɧɢɟ ɩɫɟɜɞɨɧɢɦɚ ɩɨɥɧɨɫɬɶɸ ɷɤɜɢɜɚɥɟɧɬɧɨ ɭɤɚɡɚɧɢɸ ɢɦɟɧɢ ɩɨɞɫɟɬɢ ɫɨ ɫɩɢɫɤɨɦ ɮɚɤɬɢɱɟɫɤɢɯ ɚɪɝɭɦɟɧɬɨɜ ɢɥɢ ɛɟɡ, ɜ ɡɚɜɢɫɢɦɨɫɬɢ ɨɬ ɤɨɧɬɟɤɫɬɚ. ɉɪɢɡɧɚɤɨɦ ɤɨɧɰɚ ɪɚɡɞɟɥɚ ɨɩɢɫɚɧɢɹ ɫɨɫɬɚɜɚ ɩɨɞɫɟɬɢ ɫɥɭɠɢɬ ɢɦɹ ɩɨɞɫɟɬɢ ɡɚ ɫɩɢɫɤɨɦ ɮɚɤɬɢɱɟɫɤɢɯ ɚɪɝɭɦɟɧɬɨɜ ɤɨɬɨɪɨɝɨ ɧɟ ɫɥɟɞɭɟɬ ɡɚɩɹɬɚɹ. Ɋɚɡɞɟɥ ɨɩɢɫɚɧɢɹ ɫɢɝɧɚɥɨɜ ɢ ɩɚɪɚɦɟɬɪɨɜ ɫɥɟɞɭɟɬ ɡɚ ɪɚɡɞɟɥɨɦ ɨɩɢɫɚɧɢɹ ɫɨɫɬɚɜɚ ɢ ɫɨɫɬɨɢɬ ɢɡ ɭɤɚɡɚɧɢɹ ɱɢɫɥɚ ɜɯɨɞɧɵɯ ɢ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɢ ɱɢɫɥɚ ɩɚɪɚɦɟɬɪɨɜ ɛɥɨɤɚ. ȼ ɤɨɧɫɬɚɧɬɧɵɯ ɜɵɪɚɠɟɧɢɹɯ, ɭɤɚɡɵɜɚɸɳɢɯ ɱɢɫɥɨ ɜɯɨɞɧɵɯ ɢ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɢ ɩɚɪɚɦɟɬɪɨɜ ɦɨɠɧɨ ɢɫɩɨɥɶɡɨɜɚɬɶ ɞɨɩɨɥɧɢɬɟɥɶɧɨ ɮɭɧɤɰɢɸ NumberOf ɫ ɞɜɭɦɹ ɩɚɪɚɦɟɬɪɚɦɢ. ɉɟɪɜɵɦ ɩɚɪɚɦɟɬɪɨɦ ɹɜɥɹɟɬɫɹ ɨɞɧɨ ɢɡ ɤɥɸɱɟɜɵɯ ɫɥɨɜ InSignals, OutSignals, Parameters, ɚ ɜɬɨɪɵɦ – ɢɦɹ ɩɨɞɫɟɬɢ ɫɨ ɫɩɢɫɤɨɦ ɮɚɤɬɢɱɟɫɤɢɯ ɚɪɝɭɦɟɧɬɨɜ. Ɏɭɧɤɰɢɹ NumberOf ɜɨɡɜɪɚɳɚɟɬ ɱɢɫɥɨ ɜɯɨɞɧɵɯ ɢɥɢ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɢɥɢ ɩɚɪɚɦɟɬɪɨɜ (ɜ ɡɚɜɢɫɢɦɨɫɬɢ ɨɬ ɩɟɪɜɨɝɨ ɚɪɝɭɦɟɧɬɚ) ɜ ɩɨɞɫɟɬɢ, ɭɤɚɡɚɧɧɨɣ ɜɨ ɜɬɨɪɨɦ ɚɪɝɭɦɟɧɬɟ. ɂɫɩɨɥɶɡɨɜɚɧɢɟ ɷɬɨɣ ɮɭɧɤɰɢɢ ɧɟɨɛɯɨɞɢɦɨ ɜ ɫɥɭɱɚɟ ɢɫɩɨɥɶɡɨɜɚɧɢɹ ɛɥɨɤɨɦ ɚɪɝɭɦɟɧɬɨɜ-ɩɨɞɫɟɬɟɣ. Ʉɨɧɰɨɦ ɪɚɡɞɟɥɚ ɨɩɢɫɚɧɢɹ ɫɢɝɧɚɥɨɜ ɢ ɩɚɪɚɦɟɬɪɨɜ ɫɥɭɠɢɬ ɨɞɧɨ ɢɡ ɤɥɸɱɟɜɵɯ ɫɥɨɜ ParamDef, Static ɢɥɢ Connections. Ɋɚɡɞɟɥ ɨɩɪɟɞɟɥɟɧɢɹ ɬɢɩɨɜ ɩɚɪɚɦɟɬɪɨɜ ɹɜɥɹɟɬɫɹ ɧɟɨɛɹɡɚɬɟɥɶɧɵɦ ɪɚɡɞɟɥɨɦ ɜ ɨɩɢɫɚɧɢɢ ɛɥɨɤɚ ɢ ɧɚɱɢɧɚɟɬɫɹ ɫ ɤɥɸɱɟɜɨɝɨ ɫɥɨɜɚ ParamDef. ȼ ɤɚɠɞɨɣ ɫɬɪɨɤɟ ɷɬɨɝɨ ɪɚɡɞɟɥɚ ɦɨɠɧɨ ɡɚɞɚɬɶ ɦɢɧɢɦɚɥɶɧɭɸ ɢ ɦɚɤɫɢɦɚɥɶɧɭɸ ɝɪɚɧɢɰɵ ɢɡɦɟɧɟɧɢɹ ɨɞɧɨɝɨ ɬɢɩɚ ɩɚɪɚɦɟɬɪɨɜ. ȿɫɥɢ ɜ ɨɩɢɫɚɧɢɢ ɫɟɬɢ ɜɫɬɪɟɱɚɸɬɫɹ ɩɚɪɚɦɟɬɪɵ ɧɟɨɩɪɟɞɟɥɟɧɧɨɝɨ ɬɢɩɚ, ɬɨ ɷɬɨɬ ɬɢɩ ɫɱɢɬɚɟɬɫɹ ɫɨɜɩɚɞɚɸɳɢɦ ɫ ɬɢɩɨɦ DefaultType. Ɉɩɢɫɚɧɢɟ ɬɢɩɚ ɧɟ ɨɛɹɡɚɧɨ ɩɪɟɞɲɟɫɬɜɨɜɚɬɶ ɨɩɢɫɚɧɢɸ ɩɚɪɚɦɟɬɪɨɜ ɷɬɨɝɨ ɬɢɩɚ. Ɍɚɤ, ɧɚɩɪɢɦɟɪ, ɨɩɪɟɞɟɥɟɧɢɟ ɬɢɩɚ ɩɚɪɚɦɟɬɪɨɜ ɦɨɠɟɬ ɧɚɯɨɞɢɬɶɫɹ ɜ ɨɩɢɫɚɧɢɢ ɝɥɚɜɧɨɣ ɫɟɬɢ. Ʉɨɧɰɨɦ ɷɬɨɝɨ ɪɚɡɞɟɥɚ ɫɥɭɠɢɬ ɨɞɧɨ ɢɡ ɤɥɸɱɟɜɵɯ ɫɥɨɜ Connections. Ɋɚɡɞɟɥ ɨɩɢɫɚɧɢɹ ɫɜɹɡɟɣ ɫɥɟɞɭɟɬ ɡɚ ɪɚɡɞɟɥɨɦ ɨɩɢɫɚɧɢɹ ɫɢɝɧɚɥɨɜ ɢ ɩɚɪɚɦɟɬɪɨɜ ɢ ɧɚɱɢɧɚɟɬɫɹ ɫ ɤɥɸɱɟɜɨɝɨ ɫɥɨɜɚ Connections. ȼ ɪɚɡɞɟɥɟ «Ɉɩɢɫɚɧɢɟ ɪɚɫɩɪɟɞɟɥɟɧɢɹ ɫɢɝɧɚɥɨɜ» ɝɥɚɜɵ «Ɉɛɳɢɣ ɫɬɚɧɞɚɪɬ» ɞɟɬɚɥɶɧɨ ɨɩɢɫɚɧɨ ɪɚɫɩɪɟɞɟɥɟɧɢɟ ɫɜɹɡɟɣ. Ɋɚɡɞɟɥ ɤɨɧɰɚ ɨɩɢɫɚɧɢɹ ɛɥɨɤɚ ɫɨɫɬɨɢɬ ɢɡ ɤɥɸɱɟɜɨɝɨ ɫɥɨɜɚ End, ɡɚ ɤɨɬɨɪɵɦ ɫɥɟɞɭɟɬ ɢɦɹ ɛɥɨɤɚ.
5.3.5.3.6 ɉɪɢɦɟɪ ɨɩɢɫɚɧɢɹ ɛɥɨɤɨɜ ɉɪɢ ɨɩɢɫɚɧɢɢ ɛɥɨɤɨɜ ɢɫɩɨɥɶɡɭɸɬɫɹ ɷɥɟɦɟɧɬɵ, ɨɩɢɫɚɧɧɵɟ ɜ ɛɢɛɥɢɨɬɟɤɟ Elements, ɩɪɢɜɟɞɟɧɧɨɣ ɜ ɪɚɡɞ. «ɉɪɢɦɟɪ ɨɩɢɫɚɧɢɹ ɷɥɟɦɟɧɬɨɜ». NetBibl SubNets Used Elements; {Ȼɢɛɥɢɨɬɟɤɚ ɩɨɞɫɟɬɟɣ, ɢɫɩɨɥɶɡɭɸɳɚɹ ɛɢɛɥɢɨɬɟɤɭ Elements} {ɋɢɝɦɨɢɞɧɵɣ ɧɟɣɪɨɧ ɫ ɩɪɨɢɡɜɨɥɶɧɵɦ ɫɭɦɦɚɬɨɪɨɦ ɧɚ N ɜɯɨɞɨɜ} Cascad NSigm(aSum : Block; N : Long; Char : Real) {ȼ ɫɨɫɬɚɜ ɤɚɫɤɚɞɚ ɜɯɨɞɢɬ ɩɪɨɢɡɜɨɥɶɧɵɣ ɫɭɦɦɚɬɨɪ ɧɚ N ɜɯɨɞɨɜ ɢ ɫɢɝɦɨɢɞɧɵɣ ɧɟɣɪɨɧ ɫ ɧɟɨɛɭɱɚɟɦɨɣ ɯɚɪɚɤɬɟɪɢɫɬɢɤɨɣ} Contents aSum(N), S_NotTrain(Char) InSignals NumberOf(InSignals, aSum(N)) {ɑɢɫɥɨ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɨɩɪɟɞɟɥɹɟɬ ɫɭɦɦɚɬɨɪ} OutSignals 1 {Ɉɞɢɧ ɜɵɯɨɞɧɨɣ ɫɢɝɧɚɥ} Parameters NumberOf(Parameters, aSum(N)) {ɑɢɫɥɨ ɩɚɪɚɦɟɬɪɨɜ ɨɩɪɟɞɟɥɹɟɬ ɫɭɦɦɚɬɨɪ} Connections {ȼɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɧɟɣɪɨɧɚ – ɜɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɫɭɦɦɚɬɨɪɚ} InSignals[1.. NumberOf(InSignals, aSum(N))] <=> aSum.InSignals[1.. NumberOf(InSignals, aSum(N))] aSum.OutSignals <=> S_NotTrain.InSignals {ȼɵɯɨɞ ɫɭɦɦɚɬɨɪɚ – ɜɯɨɞ ɩɪɟɨɛɪɚɡɨɜɚɬɟɥɹ}
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End
OutSignals <=> S_NotTrain.OutSignals {ɉɚɪɚɦɟɬɪɵ ɧɟɣɪɨɧɚ – ɩɚɪɚɦɟɬɪɵ ɫɭɦɦɚɬɨɪɚ } Parameters[1.. NumberOf(Parameters, aSum(N))] <=> aSum.Parameters[1.. NumberOf(Parameters, aSum(N))] {Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɫɢɝɦɨɢɞɧɨɝɨ ɧɟɣɪɨɧɚ ɫ ɩɪɨɢɡɜɨɥɶɧɵɦ ɫɭɦɦɚɬɨɪɨɦ}
{ɋɥɨɣ ɫɢɝɦɨɢɞɧɵɯ ɧɟɣɪɨɧɨɜ ɫ ɩɪɨɢɡɜɨɥɶɧɵɦɢ ɫɭɦɦɚɬɨɪɚɦɢ ɧɚ N ɜɯɨɞɨɜ} Layer Lay1(aSum : Block; N,M : Long; Char : Real) Contents Sigm: NSigm(aSum,N,Char)[M] {ȼ ɫɨɫɬɚɜ ɫɥɨɹ ɜɯɨɞɢɬ M ɧɟɣɪɨɧɨɜ} InSignals M * NumberOf(InSignals, Sigm) {ɑɢɫɥɨ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɨɩɪɟɞɟɥɹɟɬɫɹ ɤɚɤ ɜɡɹɬɨɟ M ɪɚɡ ɱɢɫɥɨ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɧɟɣɪɨɧɨɜ. ȼɦɟɫɬɨ ɢɦɟɧɢ ɧɟɣɪɨɧɚ ɢɫɩɨɥɶɡɭɟɦ ɩɫɟɜɞɨɧɢɦ} OutSignals M {Ɉɞɢɧ ɜɵɯɨɞɧɨɣ ɫɢɝɧɚɥ ɧɚ ɧɟɣɪɨɧ} Parameters M * NumberOf(Parameters, Sigm) {ɑɢɫɥɨ ɩɚɪɚɦɟɬɪɨɜ ɨɩɪɟɞɟɥɹɟɬɫɹ ɤɚɤ ɜɡɹɬɨɟ M ɪɚɡ ɱɢɫɥɨ ɩɚɪɚɦɟɬɪɨɜ ɧɟɣɪɨɧɨɜ} Connections {ɉɟɪɜɵɟ NumberOf(InSignals, NSigm(aSum,N,Char)) ɫɢɝɧɚɥɨɜ ɩɟɪɜɨɦɭ ɧɟɣɪɨɧɭ, ɢ ɬ.ɞ.} InSignals[1..M * NumberOf(InSignals, Sigm)] <=> Sigm[1..M].InSignals[1.. NumberOf(InSignals, Sigm)] {ȼɵɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɧɟɣɪɨɧɨɜ - ɜɵɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɫɟɬɢ} OutSignals[1..M] <=> Sigm[1..M].OutSignals {ɉɚɪɚɦɟɬɪɵ ɫɥɨɹ – ɩɚɪɚɦɟɬɪɵ ɧɟɣɪɨɧɨɜ} Parameters[1..M * NumberOf(Parameters, Sigm)] <=> Sigm[1..M].Parameters[1.. NumberOf(Parameters, Sigm)] End {Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɫɥɨɹ ɫɢɝɦɨɢɞɧɵɯ ɧɟɣɪɨɧɨɜ ɫ ɩɪɨɢɡɜɨɥɶɧɵɦ ɫɭɦɦɚɬɨɪɨɦ} {ɋɥɨɣ ɬɨɱɟɤ ɜɟɬɜɥɟɧɢɹ} Layer BLay( N,M : Long) Contents Branch(N)[M] {ȼ ɫɨɫɬɚɜ ɫɥɨɹ ɜɯɨɞɢɬ M ɬɨɱɟɤ ɜɟɬɜɥɟɧɢɹ} InSignals M {ɉɨ ɨɞɧɨɦɭ ɜɯɨɞɧɨɦɭ ɫɢɝɧɚɥɭ ɧɚ ɬɨɱɤɭ ɜɟɬɜɥɟɧɢɹ} OutSignals M * N {N ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɭ ɤɚɠɞɨɣ ɬɨɱɤɢ ɜɟɬɜɥɟɧɢɹ} Connections InSignals[1..M] <=> Branch[1..M].InSignals {ɉɨ ɨɞɧɨɦɭ ɜɯɨɞɭ ɧɚ ɬɨɱɤɭ ɜɟɬɜɥɟɧɢɹ} {ȼɵɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɜ ɩɨɪɹɞɤɟ ɩɟɪɜɵɣ ɫ ɤɚɠɞɨɣ ɬɨɱɤɢ ɜɟɬɜɥɟɧɢɹ, ɡɚɬɟɦ ɜɬɨɪɨɣ ɢ ɬ.ɞ. } OutSignals[1..N * M] <=> Branch[+:1..M].OutSignals[1..N] End {Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɫɥɨɹ Ɍɨɱɟɤ ɜɟɬɜɥɟɧɢɹ} {ɉɨɥɧɵɣ ɫɥɨɣ ɫɢɝɦɨɢɞɧɵɯ ɧɟɣɪɨɧɨɜ ɫ ɩɪɨɢɡɜɨɥɶɧɵɦɢ ɫɭɦɦɚɬɨɪɚɦɢ ɧɚ N ɜɯɨɞɨɜ} Cascad FullLay(aSum : Block; N,M : Long; Char : Real) Contents Br: BLay1(M,N), Ne: Lay1(aSum,N,M,Char) {ɋɥɨɣ ɬɨɱɟɤ ɜɟɬɜɥɟɧɢɹ ɢ ɫɥɨɣ ɧɟɣɪɨɧɨɜ} InSignals N {ɑɢɫɥɨ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ – ɱɢɫɥɨ ɬɨɱɟɤ ɜɟɬɜɥɟɧɢɹ} OutSignals M {Ɉɞɢɧ ɜɵɯɨɞɧɨɣ ɫɢɝɧɚɥ ɧɚ ɧɟɣɪɨɧ} Parameters NumberOf(Parameters, Ne) {ɑɢɫɥɨ ɩɚɪɚɦɟɬɪɨɜ ɨɩɪɟɞɟɥɹɟɬɫɹ ɤɚɤ ɜɡɹɬɨɟ M ɪɚɡ ɱɢɫɥɨ ɩɚɪɚɦɟɬɪɨɜ ɧɟɣɪɨɧɨɜ} Connections InSignals[1..N]<=> Br.InSignals[1..N] {ȼɯɨɞɧɵɟ ɫɢɝɧɚɥɵ – ɫɥɨɸ ɬɨɱɟɤ ɜɟɬɜɥɟɧɢɹ} {ȼɵɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɧɟɣɪɨɧɨɜ - ɜɵɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɫɟɬɢ} OutSignals[1..M] <=> Ne.OutSignals[1..M] {ɉɚɪɚɦɟɬɪɵ ɫɥɨɹ – ɩɚɪɚɦɟɬɪɵ ɧɟɣɪɨɧɨɜ} Parameters[1..NumberOf(Parameters, Ne)] <=> Ne.Parameters[1.. NumberOf(Parameters, Ne)] {ȼɵɯɨɞ ɫɥɨɹ ɬɨɱɟɤ ɜɟɬɜɥɟɧɢɹ – ɜɯɨɞ ɫɥɨɹ ɧɟɣɪɨɧɨɜ} Br.OutSignals[1..N * M] <=> Ne.InSignals[1..N * M] End {Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɫɥɨɹ ɫɢɝɦɨɢɞɧɵɯ ɧɟɣɪɨɧɨɜ ɫ ɩɪɨɢɡɜɨɥɶɧɵɦ ɫɭɦɦɚɬɨɪɨɦ} {ɋɟɬɶ ɫ ɫɢɝɦɨɢɞɧɵɦɢ ɧɟɣɪɨɧɚɦɢ ɢ ɩɪɨɢɡɜɨɥɶɧɵɦɢ ɫɭɦɦɚɬɨɪɚɦɢ, ɫɨɞɟɪɠɚɳɚɹ Input – ɱɢɫɥɨ ɧɟɣɪɨɧɨɜ ɧɚ ɜɯɨɞɧɨɦ ɫɥɨɟ; Output – ɱɢɫɥɨ ɧɟɣɪɨɧɨɜ ɧɚ ɜɵɯɨɞɧɨɦ ɫɥɨɟ (ɱɢɫɥɨ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ); Hidden – ɱɢɫɥɨ ɧɟɣɪɨɧɨɜ ɧɚ H>0 ɫɤɪɵɬɵɯ ɫɥɨɹɯ; N – ɱɢɫɥɨ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɜɫɟ ɜɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɩɨɞɚɸɬɫɹ ɧɚ ɜɫɟ ɧɟɣɪɨɧɵ ɜɯɨɞɧɨɝɨ ɫɥɨɹ}
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Cascad Net1(aSum : Block; Char : Real; Input, Output, Hidden, H, N : Long) {ɉɨɞ ɬɪɟɦɹ ɪɚɡɧɵɦɢ ɩɫɟɜɞɨɧɢɦɚɦɢ ɢɫɩɨɥɶɡɭɟɬɫɹ ɨɞɧɚ ɢ ɬɚ ɠɟ ɩɨɞɫɟɬɶ ɫ ɪɚɡɧɵɦɢ ɩɚɪɚɦɟɬɪɚɦɢ} Contents In: FullLay(aSum,N,Input,Char), Hid1: FullLay(aSum,Input,Hidden,Char) Hid2: FullLay(aSum,Hidden,Hidden,Char)[H-1] {ɉɭɫɬɨ ɩɪɢ H=1} Out: FullLay(aSum,Hidden,Output,Char) InSignals N {ɑɢɫɥɨ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ – N} OutSignals Output {Ɉɞɢɧ ɜɵɯɨɞɧɨɣ ɫɢɝɧɚɥ ɧɚ ɧɟɣɪɨɧ} {ɑɢɫɥɨ ɩɚɪɚɦɟɬɪɨɜ ɨɩɪɟɞɟɥɹɟɬɫɹ ɤɚɤ ɫɭɦɦɚ ɱɢɫɟɥ ɩɚɪɚɦɟɬɪɨɜ ɜɫɟɯ ɩɨɞɫɟɬɟɣ} Parameters NumberOf(Parameters, In)+ NumberOf(Parameters, Hid1)+ (H-1) * NumberOf(Parameters, Hid2)+ NumberOf(Parameters, Out) Connections InSignals[1..N]<=> In.InSignals[1..N] {ȼɯɨɞɧɵɟ ɫɢɝɧɚɥɵ – ɜɯɨɞɧɨɦɭ ɫɥɨɸ} {ȼɵɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɧɟɣɪɨɧɨɜ - ɫ ɜɵɯɨɞɧɨɝɨ ɫɥɨɹ ɫɟɬɢ} OutSignals[1..Output] <=> Out.OutSignals[1.. Output] {ɉɚɪɚɦɟɬɪɵ ɫeɬɢ ɩɨɫɥɟɞɨɜɚɬɟɥɶɧɨ ɜɫɟɦ ɩɨɞɫɟɬɹɦ} Parameters[1..NumberOf(Parameters,In)] <=> In.Parameters[1.. NumberOf(Parameters, In)] Parameters[NumberOf(Parameters,In)+1..NumberOf(Parameters,In)+ NumberOf(Parameters, Hid1)] <=> Hid1.Parameters[1.. NumberOf(Parameters, Hid1)] Parameters[NumberOf(Parameters,In)+ NumberOf(Parameters, Hid1)]+1 ..NumberOf(Parameters,In)+NumberOf(Parameters, Hid1)+ (H-1) * NumberOf(Parameters, Hid2)] <=> Hid2[1..H-1].Parameters[1.. NumberOf(Parameters, Hid2)] Parameters[NumberOf(Parameters,In)+ NumberOf(Parameters, Hid1)]+ (H-1) * NumberOf(Parameters, Hid2)+1..NumberOf(Parameters,In)+ NumberOf(Parameters,Hid1)+(H-1)*NumberOf(Parameters,Hid2)+ NumberOf(Parameters, Out)] <=> Out.Parameters[1.. NumberOf(Parameters, Out)] {ɉɟɪɟɞɚɱɚ ɫɢɝɧɚɥɨɜ ɨɬ ɫɥɨɹ ɤ ɫɥɨɸ} In.OutSignals[1..Input] <=> Hid1.InSignals[1..Input] {Ɉɬ ɜɯɨɞɧɨɝɨ ɤ ɩɟɪɜɨɦɭ ɫɤɪɵɬɨɦɭ ɫɥɨɸ} Hid1.OutSignals[1..Hidden] <=> Hid2[1].InSignals[1..Hidden] {Ɉɬ ɩɟɪɜɨɝɨ ɫɤɪɵɬɨɝɨ ɫɥɨɹ} {ɦɟɠɞɭ ɫɤɪɵɬɵɦɢ ɫɥɨɹɦɢ. ɉɪɢ H=1 ɷɬɚ ɡɚɩɢɫɶ ɩɭɫɬɚ} Hid2[1..H-2].OutSignals[1.. Hidden] <=> Hid2[2..H-1].InSignals[1.. Hidden] Hid2[H-1].OutSignals[1.. Hidden] <=> Out.InSignals[1.. Hidden]{Ɉɬ ɫɤɪɵɬɵɯ – ɤ ɜɵɯɨɞɧɨɦɭ} End {ɉɨɥɧɨɫɜɹɡɧɚɹ ɫɟɬɶ ɫ M ɫɢɝɦɨɢɞɧɵɦɢ ɧɟɣɪɨɧɚɦɢ ɧɚ Ʉ ɬɚɤɬɨɜ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ ɫ ɧɟɜɵɞɟɥɟɧɧɵɦ ɜɯɨɞɧɵɦ ɫɥɨɟɦ ɧɚ M ɫɢɝɧɚɥɨɜ} Loop Circle(aSum : Block; Char : Real; M, K : Long) K Contents Net: FullLay(aSum,M,M,Char) InSignals M {ɑɢɫɥɨ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ – N} OutSignals M {Ɉɞɢɧ ɜɵɯɨɞɧɨɣ ɫɢɝɧɚɥ ɧɚ ɧɟɣɪɨɧ} Parameters NumberOf(Parameters, Net) {ɑɢɫɥɨ ɩɚɪɚɦɟɬɪɨɜ ɨɩɪɟɞɟɥɹɟɬɫɹ ɫɥɨɟɦ FullLay} Connections InSignals[1..M]<=> Net.InSignals[1..M] {ȼɯɨɞɧɵɟ ɫɢɝɧɚɥɵ – ɧɚ ɜɯɨɞ ɫɥɨɹ} OutSignals[1..M] <=> Net.OutSignals[1.. M] {ȼɵɯɨɞɧɵɟ ɫɢɝɧɚɥɵ – ɧɚ ɜɵɯɨɞɟ ɫɥɨɹ} {ȼɫɟ ɩɚɪɚɦɟɬɪɵ ɫɥɨɸ} Parameters[1..NumberOf(Parameters,Net)] <=> Net.Parameters[1.. NumberOf(Parameters,Net)] Net.OutSignals[1..M] <=> Net.InSignals[1..M] {Ɂɚɦɵɤɚɟɦ ɜɵɯɨɞ ɧɚ ɜɯɨɞ} End {Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɫɥɨɹ ɫɢɝɦɨɢɞɧɵɯ ɧɟɣɪɨɧɨɜ ɫ ɩɪɨɢɡɜɨɥɶɧɵɦ ɫɭɦɦɚɬɨɪɨɦ} {ɉɨɥɧɨɫɜɹɡɧɚɹ ɫɟɬɶ ɫ Ɇ ɫɢɝɦɨɢɞɧɵɦɢ ɧɟɣɪɨɧɚɦɢ ɧɚ Ʉ ɬɚɤɬɨɜ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ ɫ ɜɵɞɟɥɟɧɧɵɦ ɜɯɨɞɧɵɦ ɫɥɨɟɦ ɧɚ N ɫɢɝɧɚɥɨɜ. ȼɫɟ ɜɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɩɨɞɚɸɬɫɹ ɧɚ ɜɯɨɞ ɤɚɠɞɨɝɨ ɧɟɣɪɨɧɚ ɜɯɨɞɧɨɝɨ ɫɥɨɹ. ȼɫɟ ɩɚɪɚɦɟɬɪɵ ɨɝɪɚɧɢɱɟɧɵ ɩɨ ɚɛɫɨɥɸɬɧɨɦɭ ɡɧɚɱɟɧɢɸ ɟɞɢɧɢɰɟɣ} Cascad Net2: (aSum : Block; Char : Real; M, K, N : Long) Contents In: FullLay(aSum,N,M,Char), {ȼɯɨɞɧɨɣ ɫɥɨɣ}
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Net: Circle(aSum,Char,M,K) {ɉɨɥɧɨɫɜɹɡɧɚɹ ɫɟɬɶ} InSignals N {ɑɢɫɥɨ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ – N} OutSignals M {Ɉɞɢɧ ɜɵɯɨɞɧɨɣ ɫɢɝɧɚɥ ɧɚ ɧɟɣɪɨɧ} {ɑɢɫɥɨ ɩɚɪɚɦɟɬɪɨɜ ɨɩɪɟɞɟɥɹɟɬɫɹ ɤɚɤ ɫɭɦɦɚ ɱɢɫɟɥ ɩɚɪɚɦɟɬɪɨɜ ɜɫɟɯ ɩɨɞɫɟɬɟɣ} Parameters NumberOf(Parameters, In)+ NumberOf(Parameters, Net) ParamDef DefaultType -1 1 Connections InSignals[1..N]<=> In.InSignals[1..N] {ȼɯɨɞɧɵɟ ɫɢɝɧɚɥɵ – ɜɯɨɞɧɨɦɭ ɫɥɨɸ} {ȼɵɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɧɟɣɪɨɧɨɜ - c ɜɵɯɨɞɧɨɝɨ ɫɥɨɹ ɫɟɬɢ} OutSignals[1..M] <=> Net.OutSignals[1.. M] {ɉɚɪɚɦɟɬɪɵ ɫɟɬɢ ɩɨɫɥɟɞɨɜɚɬɟɥɶɧɨ ɜɫɟɦ ɩɨɞɫɟɬɹɦ} Parameters[1..NumberOf(Parameters, In)] <=> In.Parameters[1.. NumberOf(Parameters, In)] Parameters[NumberOf(Parameters,In)+1.. NumberOf(Parameters,In)+NumberOf(Parameters, Net)] <=> Net.Parameters[1.. NumberOf(Parameters, Net)] {ɉɟɪɟɞɚɱɚ ɫɢɝɧɚɥɨɜ ɨɬ ɫɥɨɹ ɤ ɫɥɨɸ} In.OutSignals[1..M] <=> Net.InSignals[1..M] {Ɉɬ ɜɯɨɞɧɨɝɨ ɤ ɰɢɤɥɭ} Net.OutSignals[1..M] <=> Net.InSignals[1..M] {Ɉɬ ɩɟɪɜɨɝɨ ɫɤɪɵɬɨɝɨ ɫɥɨɹ} End {ɇɟɣɪɨɧ ɫɟɬɢ ɏɨɩɮɢɥɞɚ ɢɡ N ɧɟɣɪɨɧɨɜ} Cascad Hopf(N : Long) Contents Sum(N),Sign_Easy {ɋɭɦɦɚɬɨɪ ɢ ɩɨɪɨɝɨɜɵɣ ɷɥɟɦɟɧɬ} InSignals N {ɑɢɫɥɨ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ – N} OutSignals 1 {ɑɢɫɥɨ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ – 1} Parameters NumberOf(Parameters,Sum(N)) {ɑɢɫɥɨ ɩɚɪɚɦɟɬɪɨɜ – N} Connections InSignals[1..N]<=> Sum.InSignals[1..N] {ȼɯɨɞɧɵɟ ɫɢɝɧɚɥɵ – ɫɭɦɦɚɬɨɪɭ} {ȼɵɯɨɞɧɨɣ ɫɢɝɧɚɥ ɧɟɣɪɨɧɚ – ɜɵɯɨɞɧɨɣ ɫɢɝɧɚɥ ɩɨɪɨɝɨɜɨɝɨ ɷɥɟɦɟɬɚ} OutSignals <=> Sign_Easy.OutSignals {ɉɚɪɚɦɟɬɪɵ ɧɟɣɪɨɧɚ – ɩɚɪɚɦɟɬɪɵ ɫɭɦɦɚɬɨɪɚ} Parameters[1..NumberOf(Parameters, Sum(N))] <=> Sum.Parameters[1.. NumberOf(Parameters, Sum(N))] Sum.OutSignals <=> Sign_Easy.InSignals {ȼɵɯɨɞ ɫɭɦɦɚɬɨɪɚ ɧɚ ɜɯɨɞ ɩɨɪɨɝɚ} End {ɋɥɨɣ ɧɟɣɪɨɧɨɜ ɏɨɩɮɢɥɞɚ} Layer HLay(N : Long) Contents Hop: Hopf(N)[N] {ȼ ɫɨɫɬɚɜ ɫɥɨɹ ɜɯɨɞɢɬ N ɧɟɣɪɨɧɨɜ} InSignals N * N {N ɧɟɣɪɨɧɨɜ ɩɨ N ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ} OutSignals N {Ɉɞɢɧ ɜɵɯɨɞɧɨɣ ɫɢɝɧɚɥ ɧɚ ɧɟɣɪɨɧ} Parameters N * NumberOf(Parameters, Hop) Connections {ɉɟɪɜɵɟ NumberOf(InSignals, Hop) ɫɢɝɧɚɥɨɜ ɩɟɪɜɨɦɭ ɧɟɣɪɨɧɭ, ɢ ɬ.ɞ.} InSignals[1..Sqr(N)] <=> Hop[1..N].InSignals[1..N] {ȼɵɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɧɟɣɪɨɧɨɜ - ɜɵɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɫɟɬɢ} OutSignals[1..N] <=> Hop[1..N].OutSignals {ɉɚɪɚɦɟɬɪɵ ɫɥɨɹ – ɩɚɪɚɦɟɬɪɵ ɧɟɣɪɨɧɨɜ} Parameters[1..N * NumberOf(Parameters, Hop)] <=> Hop[1..N].Parameters[1.. NumberOf(Parameters, Hop)] End {ɋɟɬɶ ɏɨɩɮɢɥɞɚ ɢɡ N ɧɟɣɪɨɧɨɜ} Until Hopfield(N : Long) InSignals=OutSignals Contents BLay(N,N),HLay(N) {ɋɥɨɣ ɬɨɱɟɤ ɜɟɬɜɥɟɧɢɹ ɢ ɫɥɨɣ ɧɟɣɪɨɧɨɜ} InSignals N {ɑɢɫɥɨ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ – N} OutSignals N {ɑɢɫɥɨ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ – N} Parameters N * NumberOf(Parameters,HLay(N)) {ɑɢɫɥɨ ɩɚɪɚɦɟɬɪɨɜ – N*N} Connections InSignals[1..N]<=> BLay.InSignals[1..N] {ȼɯɨɞɧɵɟ ɫɢɝɧɚɥɵ – ɬɨɱɤɚɦ ɜɟɬɜɥɟɧɢɹ}
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{ȼɵɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɧɟɣɪɨɧɨɜ – ɜɵɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɫɟɬɢ} OutSignals[1..N] <=> HLay.OutSignals[1..N] Parameters[1..N*NumberOf(Parameters, HLay(N))] <=> HLay.Parameters[1..N*NumberOf(Parameters, HLay(N))] {ȼɵɯɨɞ ɬɨɱɟɤ ɜɟɬɜɥɟɧɢɹ ɧɚ ɜɯɨɞ ɧɟɣɪɨɧɨɜ} BLay.OutSignals[1..Sqr(N)] <=> HLay.InSignals[1..Sqr(N)] HLay.OutSignals[1..N] <=> BLay.InSignals[1..N] {Ɂɚɦɵɤɚɟɦ ɤɨɧɟɰ ɧɚ ɧɚɱɚɥɨ} End End NetLib NetWork Hop Used SubNets; {ɋɟɬɶ ɏɨɩɮɢɥɞɚ ɧɚ ɩɹɬɶ ɧɟɣɪɨɧɨɜ} MainNet Hopfield(5) Parameters 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0; ParamMask -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1; End NetWork
5.3.5.4 ɋɨɤɪɚɳɟɧɢɟ ɨɩɢɫɚɧɢɹ ɫɟɬɢ ɉɪɟɞɥɨɠɟɧɧɵɣ ɜ ɩɪɟɞɵɞɭɳɢɯ ɪɚɡɞɟɥɚɯ ɹɡɵɤ ɨɩɢɫɚɧɢɹ ɦɧɨɝɨɫɥɨɜɟɧ. ȼ ɛɨɥɶɲɢɧɫɬɜɟ ɫɥɭɱɚɟɜ ɡɚ ɫɱɟɬ ɯɨɪɨɲɟɣ ɫɬɪɭɤɬɭɪɢɡɚɰɢɢ ɫɟɬɢ ɦɨɠɧɨ ɨɩɭɫɬɢɬɶ ɜɫɟ ɪɚɡɞɟɥɵ ɨɩɢɫɚɧɢɹ ɛɥɨɤɚ ɤɪɨɦɟ ɪɚɡɞɟɥɚ ɫɨɫɬɚɜɚ. ȼ ɞɚɧɧɨɦ ɪɚɡɞɟɥɟ ɨɩɢɫɵɜɚɟɬɫɹ ɝɟɧɟɪɚɰɢɹ ɩɨ ɭɦɨɥɱɚɧɢɸ ɪɚɡɞɟɥɨɜ ɨɩɢɫɚɧɢɹ ɫɢɝɧɚɥɨɜ ɢ ɩɚɪɚɦɟɬɪɨɜ, ɢ ɨɩɢɫɚɧɢɹ ɫɜɹɡɟɣ. ɂɫɩɨɥɶɡɨɜɚɧɢɟ ɦɟɯɚɧɢɡɦɨɜ ɭɦɨɥɱɚɧɢɹ ɩɨɡɜɨɥɹɟɬ ɫɢɥɶɧɨ ɫɨɤɪɚɬɢɬɶ ɬɟɤɫɬ ɨɩɢɫɚɧɢɹ ɫɟɬɢ.
5.3.5.4.1 Ɋɚɡɞɟɥ ɨɩɢɫɚɧɢɹ ɫɢɝɧɚɥɨɜ ɢ ɩɚɪɚɦɟɬɪɨɜ Ⱦɥɹ ɜɫɟɯ ɜɢɞɨɜ ɛɥɨɤɨɜ ɱɢɫɥɨ ɩɚɪɚɦɟɬɪɨɜ ɨɩɪɟɞɟɥɹɟɬɫɹ ɤɚɤ ɫɭɦɦɚ ɱɢɫɟɥ ɩɚɪɚɦɟɬɪɨɜ ɜɫɟɯ ɩɨɞɫɟɬɟɣ, ɩɟɪɟɱɢɫɥɟɧɧɵɯ ɜ ɪɚɡɞɟɥɟ ɨɩɢɫɚɧɢɹ ɫɨɫɬɚɜɚ. ɗɬɨ ɦɨɠɟɬ ɩɪɢɜɨɞɢɬɶ ɤ ɥɢɲɧɢɦ ɡɚɩɢɫɹɦ, ɧɨ ɧɟ ɩɨɜɥɢɹɟɬ ɧɚ ɪɚɛɨɬɭ ɫɟɬɢ. ɉɪɢɦɟɪɨɦ ɥɢɲɧɟɣ ɡɚɩɢɫɢ ɦɨɠɟɬ ɫɥɭɠɢɬɶ ɝɟɧɟɪɢɪɭɟɦɚɹ ɡɚɩɢɫɶ: Parameters M * NumberOf(Parameters,Branch(N)) ɜ ɨɩɢɫɚɧɢɢ ɫɥɨɹ ɬɨɱɟɤ ɜɟɬɜɥɟɧɢɹ, ɩɨɫɤɨɥɶɤɭ ɬɨɱɤɢ ɜɟɬɜɥɟɧɢɹ ɧɟ ɢɦɟɸɬ ɩɚɪɚɦɟɬɪɨɜ. ɑɢɫɥɨ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɛɥɨɤɚ ɨɩɪɟɞɟɥɹɟɬɫɹ ɩɨ ɫɥɟɞɭɸɳɢɦ ɩɪɚɜɢɥɚɦ: · ɞɥɹ ɫɥɨɹ ɱɢɫɥɨ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɪɚɜɧɨ ɫɭɦɦɟ ɱɢɫɥɚ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɜɫɟɯ ɩɨɞɫɟɬɟɣ, ɩɟɪɟɱɢɫɥɟɧɧɵɯ ɜ ɪɚɡɞɟɥɟ ɨɩɢɫɚɧɢɹ ɫɨɫɬɚɜɚ; · ɞɥɹ ɤɚɫɤɚɞɨɜ ɜɫɟɯ ɜɢɞɨɜ ɱɢɫɥɨ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɛɥɨɤɚ ɪɚɜɧɨ ɱɢɫɥɭ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɩɨɞɫɟɬɢ, ɫɬɨɹɳɟɣ ɩɟɪɜɨɣ ɜ ɫɩɢɫɤɟ ɩɨɞɫɟɬɟɣ ɜ ɪɚɡɞɟɥɟ ɨɩɢɫɚɧɢɹ ɫɨɫɬɚɜɚ ɑɢɫɥɨ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɛɥɨɤɚ ɨɩɪɟɞɟɥɹɟɬɫɹ ɩɨ ɫɥɟɞɭɸɳɢɦ ɩɪɚɜɢɥɚɦ: · ɞɥɹ ɫɥɨɹ ɱɢɫɥɨ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɪɚɜɧɨ ɫɭɦɦɟ ɱɢɫɥɚ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɜɫɟɯ ɩɨɞɫɟɬɟɣ, ɩɟɪɟɱɢɫɥɟɧɧɵɯ ɜ ɪɚɡɞɟɥɟ ɨɩɢɫɚɧɢɹ ɫɨɫɬɚɜɚ; · ɞɥɹ ɤɚɫɤɚɞɨɜ ɜɫɟɯ ɜɢɞɨɜ ɱɢɫɥɨ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɛɥɨɤɚ ɪɚɜɧɨ ɱɢɫɥɭ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɩɨɞɫɟɬɢ, ɫɬɨɹɳɟɣ ɩɨɫɥɟɞɧɟɣ ɜ ɫɩɢɫɤɟ ɩɨɞɫɟɬɟɣ ɜ ɪɚɡɞɟɥɟ ɨɩɢɫɚɧɢɹ ɫɨɫɬɚɜɚ; Ɉɩɢɫɚɧɢɹ ɜɫɟɯ ɫɟɬɟɣ, ɩɪɢɜɟɞɟɧɧɵɟ ɜ ɩɪɟɞɵɞɭɳɟɦ ɪɚɡɞɟɥɟ ɩɨɥɧɨɫɬɶɸ ɫɨɨɬɜɟɬɫɬɜɭɸɬ ɩɪɚɜɢɥɚɦ ɝɟɧɟɪɚɰɢɢ. ȼ ɤɚɱɟɫɬɜɟ ɛɨɥɟɟ ɨɛɳɟɝɨ ɩɪɢɦɟɪɚ ɩɪɢɜɟɞɟɦ ɪɚɡɞɟɥ ɨɩɢɫɚɧɢɹ ɫɢɝɧɚɥɨɜ ɢ ɩɚɪɚɦɟɬɪɨɜ ɞɜɭɯ ɭɫɥɨɜɧɵɯ ɛɥɨɤɨɜ. Layer A Contents Net1, Net2[K], Net3 InSignals NumberOf(InSignals,Net1)+K*NumberOf(InSignals,Net2) +NumberOf(InSignals,Net3) OutSignals NumberOf(OutSignals,Net1)+K*NumberOf(OutSignals,Net2) +NumberOf(OutSignals,Net3) Parameters NumberOf(Parameters,Net1)+K*NumberOf(Parameters,Net2) +NumberOf(Parameters,Net3) Cascad B Contents Net1, Net2[K], Net3 InSignals NumberOf(InSignals,Net1) OutSignals NumberOf(OutSignals,Net3) Parameters NumberOf(Parameters,Net1)+K*NumberOf(Parameters,Net2) +NumberOf(Parameters,Net3)
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5.3.5.4.2 Ɋɚɡɞɟɥ ɨɩɢɫɚɧɢɹ ɫɜɹɡɟɣ Ɋɚɡɞɟɥ ɨɩɢɫɚɧɢɹ ɫɜɹɡɟɣ ɦɨɠɟɬ ɛɵɬɶ ɪɚɡɛɢɬ ɧɚ ɩɹɬɶ ɩɨɞɪɚɡɞɟɥɨɜ. 1. ɍɫɬɚɧɨɜɥɟɧɢɟ ɫɜɹɡɢ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɛɥɨɤɚ ɫ ɜɯɨɞɧɵɦɢ ɫɢɝɧɚɥɚɦɢ ɩɨɞɫɟɬɟɣ. 2. ɍɫɬɚɧɨɜɥɟɧɢɟ ɫɜɹɡɢ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɛɥɨɤɚ ɫ ɜɵɯɨɞɧɵɦɢ ɫɢɝɧɚɥɚɦɢ ɩɨɞɫɟɬɟɣ. 3. ɍɫɬɚɧɨɜɥɟɧɢɟ ɫɜɹɡɢ ɩɚɪɚɦɟɬɪɨɜ ɛɥɨɤɚ ɫ ɩɚɪɚɦɟɬɪɚɦɢ ɩɨɞɫɟɬɟɣ. 4. ɍɫɬɚɧɨɜɥɟɧɢɟ ɫɜɹɡɢ ɦɟɠɞɭ ɜɵɯɨɞɧɵɦɢ ɫɢɝɧɚɥɚɦɢ ɨɞɧɢɯ ɩɨɞɫɟɬɟɣ ɢ ɜɯɨɞɧɵɦɢ ɫɢɝɧɚɥɚɦɢ ɞɪɭɝɢɯ ɩɨɞɫɟɬɟɣ. 5. Ɂɚɦɵɤɚɧɢɟ ɜɵɯɨɞɚ ɛɥɨɤɚ ɧɚ ɜɯɨɞ ɛɥɨɤɚ. Ⱦɥɹ ɫɥɨɹ ɪɚɡɞɟɥ ɨɩɢɫɚɧɢɹ ɫɜɹɡɟɣ ɫɬɪɨɢɬɫɹ ɩɨ ɫɥɟɞɭɸɳɢɦ ɩɪɚɜɢɥɚɦ. 1. ȼɫɟ ɩɨɞɫɟɬɢ ɩɨɥɭɱɚɸɬ ɜɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɜ ɩɨɪɹɞɤɟ ɩɟɪɟɱɢɫɥɟɧɢɹ ɩɨɞɫɟɬɟɣ ɜ ɪɚɡɞɟɥɟ ɨɩɢɫɚɧɢɹ ɫɨɫɬɚɜɚ – ɩɟɪɜɚɹ ɱɚɫɬɶ ɦɚɫɫɢɜɚ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɫɥɨɹ ɨɬɞɚɟɬɫɹ ɩɟɪɜɨɣ ɩɨɞɫɟɬɢ, ɫɥɟɞɭɸɳɚɹ – ɜɬɨɪɨɣ ɢ ɬ.ɞ. ȿɫɥɢ ɤɚɤɚɹ-ɥɢɛɨ ɩɨɞɫɟɬɶ ɜ ɪɚɡɞɟɥɟ ɨɩɢɫɚɧɢɹ ɫɨɫɬɚɜɚ ɭɤɚɡɚɧɚ ɫ ɧɟɤɨɬɨɪɵɦ ɧɟ ɪɚɜɧɵɦ ɟɞɢɧɢɰɟ ɱɢɫɥɨɦ ɷɤɡɟɦɩɥɹɪɨɜ, ɬɨ ɫɱɢɬɚɟɬɫɹ, ɱɬɨ ɷɤɡɟɦɩɥɹɪɵ ɷɬɨɣ ɩɨɞɫɟɬɢ ɩɟɪɟɱɢɫɥɟɧɵ ɜ ɫɩɢɫɤɟ ɜ ɩɨɪɹɞɤɟ ɜɨɡɪɚɫɬɚɧɢɹ ɧɨɦɟɪɚ. 2. ȼɵɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɩɨɞɫɟɬɟɣ ɨɛɪɚɡɭɸɬ ɦɚɫɫɢɜ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɫɥɨɹ ɬɚɤɠɟ ɜ ɩɨɪɹɞɤɟ ɩɟɪɟɱɢɫɥɟɧɢɹ ɩɨɞɫɟɬɟɣ ɜ ɪɚɡɞɟɥɟ ɨɩɢɫɚɧɢɹ ɫɨɫɬɚɜɚ – ɩɟɪɜɚɹ ɱɚɫɬɶ ɦɚɫɫɢɜɚ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɫɥɨɹ ɫɨɫɬɨɢɬ ɢɡ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɩɟɪɜɨɣ ɩɨɞɫɟɬɢ, ɫɥɟɞɭɸɳɚɹ – ɜɬɨɪɨɣ ɢ ɬ.ɞ. ȿɫɥɢ ɤɚɤɚɹ-ɥɢɛɨ ɩɨɞɫɟɬɶ ɜ ɪɚɡɞɟɥɟ ɨɩɢɫɚɧɢɹ ɫɨɫɬɚɜɚ ɭɤɚɡɚɧɚ ɫ ɧɟɤɨɬɨɪɵɦ ɧɟ ɪɚɜɧɵɦ ɟɞɢɧɢɰɟ ɱɢɫɥɨɦ ɷɤɡɟɦɩɥɹɪɨɜ, ɬɨ ɫɱɢɬɚɟɬɫɹ, ɱɬɨ ɷɤɡɟɦɩɥɹɪɵ ɷɬɨɣ ɩɨɞɫɟɬɢ ɩɟɪɟɱɢɫɥɟɧɵ ɜ ɫɩɢɫɤɟ ɜ ɩɨɪɹɞɤɟ ɜɨɡɪɚɫɬɚɧɢɹ ɧɨɦɟɪɚ. 3. ɉɨɞɪɚɡɞɟɥɵ ɭɫɬɚɧɨɜɥɟɧɢɹ ɫɜɹɡɢ ɦɟɠɞɭ ɜɵɯɨɞɧɵɦɢ ɫɢɝɧɚɥɚɦɢ ɨɞɧɢɯ ɩɨɞɫɟɬɟɣ ɢ ɜɯɨɞɧɵɦɢ ɫɢɝɧɚɥɚɦɢ ɞɪɭɝɢɯ ɩɨɞɫɟɬɟɣ ɢ ɡɚɦɵɤɚɧɢɹ ɜɵɯɨɞɚ ɛɥɨɤɚ ɧɚ ɜɯɨɞ ɞɥɹ ɫɥɨɹ ɨɬɫɭɬɫɬɜɭɸɬ. Ⱦɥɹ ɤɚɫɤɚɞɨɜ ɪɚɡɞɟɥ ɨɩɢɫɚɧɢɹ ɫɜɹɡɟɣ ɫɬɪɨɢɬɫɹ ɩɨ ɫɥɟɞɭɸɳɢɦ ɩɪɚɜɢɥɚɦ: 1. ȼɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɛɥɨɤɚ ɫɜɹɡɵɜɚɸɬɫɹ ɫ ɜɯɨɞɧɵɦɢ ɫɢɝɧɚɥɚɦɢ ɩɟɪɜɨɣ ɩɨɞɫɟɬɢ ɜ ɫɩɢɫɤɟ ɩɨɞɫɟɬɟɣ ɜ ɪɚɡɞɟɥɟ ɨɩɢɫɚɧɢɹ ɫɨɫɬɚɜɚ. ȿɫɥɢ ɞɥɹ ɩɟɪɜɨɣ ɩɨɞɫɟɬɢ ɭɤɚɡɚɧɨ ɧɟ ɟɞɢɧɢɱɧɨɟ ɱɢɫɥɨ ɷɤɡɟɦɩɥɹɪɨɜ, ɬɨ ɜɫɟ ɜɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɫɜɹɡɵɜɚɸɬɫɹ ɫ ɜɯɨɞɧɵɦɢ ɫɢɝɧɚɥɚɦɢ ɩɟɪɜɨɝɨ ɷɤɡɟɦɩɥɹɪɚ ɩɨɞɫɟɬɢ. 2. ȼɵɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɛɥɨɤɚ ɫɜɹɡɵɜɚɸɬɫɹ ɫ ɜɵɯɨɞɧɵɦɢ ɫɢɝɧɚɥɚɦɢ ɩɨɫɥɟɞɧɟɣ ɩɨɞɫɟɬɢ ɜ ɫɩɢɫɤɟ ɩɨɞɫɟɬɟɣ ɜ ɪɚɡɞɟɥɟ ɨɩɢɫɚɧɢɹ ɫɨɫɬɚɜɚ. ȿɫɥɢ ɞɥɹ ɩɨɫɥɟɞɧɟɣ ɩɨɞɫɟɬɢ ɭɤɚɡɚɧɨ ɧɟ ɟɞɢɧɢɱɧɨɟ ɱɢɫɥɨ ɷɤɡɟɦɩɥɹɪɨɜ, ɬɨ ɜɫɟ ɜɵɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɫɜɹɡɵɜɚɸɬɫɹ ɫ ɜɵɯɨɞɧɵɦɢ ɫɢɝɧɚɥɚɦɢ ɩɨɫɥɟɞɧɟɝɨ (ɫ ɦɚɤɫɢɦɚɥɶɧɵɦ ɧɨɦɟɪɨɦ) ɷɤɡɟɦɩɥɹɪɚ ɩɨɞɫɟɬɢ. 3. Ɇɚɫɫɢɜ ɩɚɪɚɦɟɬɪɨɜ ɛɥɨɤɚ ɨɛɪɚɡɭɟɬɫɹ ɢɡ ɦɚɫɫɢɜɨɜ ɩɚɪɚɦɟɬɪɨɜ ɩɨɞɫɟɬɟɣ ɜ ɩɨɪɹɞɤɟ ɩɟɪɟɱɢɫɥɟɧɢɹ ɩɨɞɫɟɬɟɣ ɜ ɪɚɡɞɟɥɟ ɨɩɢɫɚɧɢɹ ɫɨɫɬɚɜɚ – ɩɟɪɜɚɹ ɱɚɫɬɶ ɦɚɫɫɢɜɚ ɩɚɪɚɦɟɬɪɨɜ ɛɥɨɤɚ ɫɨɫɬɨɢɬ ɢɡ ɩɚɪɚɦɟɬɪɨɜ ɩɟɪɜɨɣ ɩɨɞɫɟɬɢ, ɫɥɟɞɭɸɳɚɹ – ɜɬɨɪɨɣ ɢ ɬ.ɞ. ȿɫɥɢ ɤɚɤɚɹ-ɥɢɛɨ ɩɨɞɫɟɬɶ ɜ ɪɚɡɞɟɥɟ ɨɩɢɫɚɧɢɹ ɫɨɫɬɚɜɚ ɭɤɚɡɚɧɚ ɫ ɧɟɤɨɬɨɪɵɦ ɧɟ ɪɚɜɧɵɦ ɟɞɢɧɢɰɟ ɱɢɫɥɨɦ ɷɤɡɟɦɩɥɹɪɨɜ, ɬɨ ɫɱɢɬɚɟɬɫɹ, ɱɬɨ ɷɤɡɟɦɩɥɹɪɵ ɷɬɨɣ ɩɨɞɫɟɬɢ ɩɟɪɟɱɢɫɥɟɧɵ ɜ ɫɩɢɫɤɟ ɜ ɩɨɪɹɞɤɟ ɜɨɡɪɚɫɬɚɧɢɹ ɧɨɦɟɪɚ. 4. ȼɵɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɤɚɠɞɨɣ ɩɨɞɫɟɬɢ, ɤɪɨɦɟ ɩɨɫɥɟɞɧɟɣ ɫɜɹɡɵɜɚɸɬɫɹ ɫ ɜɯɨɞɧɵɦɢ ɫɢɝɧɚɥɚɦɢ ɫɥɟɞɭɸɳɟɣ ɩɨɞɫɟɬɢ ɜ ɫɩɢɫɤɟ ɩɨɞɫɟɬɟɣ ɜ ɪɚɡɞɟɥɟ ɨɩɢɫɚɧɢɹ ɫɨɫɬɚɜɚ. ȿɫɥɢ ɤɚɤɚɹ-ɥɢɛɨ ɩɨɞɫɟɬɶ ɜ ɪɚɡɞɟɥɟ ɨɩɢɫɚɧɢɹ ɫɨɫɬɚɜɚ ɭɤɚɡɚɧɚ ɫ ɧɟɤɨɬɨɪɵɦ ɧɟ ɪɚɜɧɵɦ ɟɞɢɧɢɰɟ ɱɢɫɥɨɦ ɷɤɡɟɦɩɥɹɪɨɜ, ɬɨ ɫɱɢɬɚɟɬɫɹ, ɱɬɨ ɷɤɡɟɦɩɥɹɪɵ ɷɬɨɣ ɩɨɞɫɟɬɢ ɩɟɪɟɱɢɫɥɟɧɵ ɜ ɫɩɢɫɤɟ ɜ ɩɨɪɹɞɤɟ ɜɨɡɪɚɫɬɚɧɢɹ ɧɨɦɟɪɚ. 5. Ⱦɥɹ ɛɥɨɤɨɜ ɬɢɩɚ Cascad ɡɚɦɵɤɚɧɢɟ ɜɵɯɨɞɚ ɛɥɨɤɚ ɧɚ ɜɯɨɞ ɛɥɨɤɚ ɨɬɫɭɬɫɬɜɭɟɬ. Ⱦɥɹ ɛɥɨɤɨɜ ɬɢɩɨɜ Loop ɢ Until ɡɚɦɵɤɚɧɢɟ ɜɵɯɨɞɚ ɛɥɨɤɚ ɧɚ ɜɯɨɞ ɛɥɨɤɚ ɞɨɫɬɢɝɚɟɬɫɹ ɩɭɬɟɦ ɭɫɬɚɧɨɜɥɟɧɢɹ ɫɜɹɡɟɣ ɦɟɠɞɭ ɜɵɯɨɞɧɵɦɢ ɫɢɝɧɚɥɚɦɢ ɩɨɫɥɟɞɧɟɣ ɩɨɞɫɟɬɢ ɜ ɫɩɢɫɤɟ ɩɨɞɫɟɬɟɣ ɜ ɪɚɡɞɟɥɟ ɨɩɢɫɚɧɢɹ ɫɨɫɬɚɜɚ ɫ ɜɯɨɞɧɵɦɢ ɫɢɝɧɚɥɚɦɢ ɩɟɪɜɨɣ ɩɨɞɫɟɬɢ ɜ ɫɩɢɫɤɟ ɩɨɞɫɟɬɟɣ ɜ ɪɚɡɞɟɥɟ ɨɩɢɫɚɧɢɹ ɫɨɫɬɚɜɚ. ȿɫɥɢ ɤɚɤɚɹɥɢɛɨ ɩɨɞɫɟɬɶ ɜ ɪɚɡɞɟɥɟ ɨɩɢɫɚɧɢɹ ɫɨɫɬɚɜɚ ɭɤɚɡɚɧɚ ɫ ɧɟɤɨɬɨɪɵɦ ɧɟ ɪɚɜɧɵɦ ɟɞɢɧɢɰɟ ɱɢɫɥɨɦ ɷɤɡɟɦɩɥɹɪɨɜ, ɬɨ ɫɱɢɬɚɟɬɫɹ, ɱɬɨ ɷɤɡɟɦɩɥɹɪɵ ɷɬɨɣ ɩɨɞɫɟɬɢ ɩɟɪɟɱɢɫɥɟɧɵ ɜ ɫɩɢɫɤɟ ɜ ɩɨɪɹɞɤɟ ɜɨɡɪɚɫɬɚɧɢɹ ɧɨɦɟɪɚ. Ɉɩɢɫɚɧɢɹ ɜɫɟɯ ɫɟɬɟɣ, ɩɪɢɜɟɞɟɧɧɵɟ ɜ ɩɪɟɞɵɞɭɳɟɦ ɪɚɡɞɟɥɟ ɩɨɥɧɨɫɬɶɸ ɫɨɨɬɜɟɬɫɬɜɭɸɬ ɩɪɚɜɢɥɚɦ ɝɟɧɟɪɚɰɢɢ. ȼ ɤɚɱɟɫɬɜɟ ɛɨɥɟɟ ɨɛɳɟɝɨ ɩɪɢɦɟɪɚ ɩɪɢɜɟɞɟɦ ɪɚɡɞɟɥ ɨɩɢɫɚɧɢɹ ɫɢɝɧɚɥɨɜ ɢ ɩɚɪɚɦɟɬɪɨɜ ɬɪɟɯ ɭɫɥɨɜɧɵɯ ɛɥɨɤɨɜ. Layer A Contents Net1, Net2[K], Net3 InSignals[1..NumberOf(InSignals,Net1)+K*NumberOf(InSignals,Net2) +NumberOf(InSignals,Net3)] <=> Net1. InSignals[1..NumberOf(InSignals,Net1)], Net2[1..K].InSignals[1..NumberOf(InSignals,Net2)], Net3.InSignals[1..NumberOf(InSignals,Net3)] OutSignals[1..NumberOf(OutSignals,Net1)+K*NumberOf(OutSignals,Net2) +NumberOf(OutSignals,Net3)] <=> Net1. OutSignals[1..NumberOf(OutSignals,Net1)], Net2[1..K].OutSignals[1..NumberOf(OutSignals,Net2)], Net3.OutSignals[1..NumberOf(OutSignals,Net3)]
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Parameters[1..NumberOf(Parameters,Net1)+K*NumberOf(Parameters,Net2) +NumberOf(Parameters,Net3)] <=> Net1. Parameters[1..NumberOf(Parameters,Net1)], Net2[1..K].Parameters[1..NumberOf(Parameters,Net2)], Net3.Parameters[1..NumberOf(Parameters,Net3)] Cascad B Contents Net1, Net2[K], Net3 InSignals[1..NumberOf(InSignals,Net1)] <=> Net1. InSignals[1..NumberOf(InSignals,Net1)] OutSignals[1..NumberOf(OutSignals,Net3)] <=> Net3.OutSignals[1..NumberOf(OutSignals,Net3)] Parameters[1..NumberOf(Parameters,Net1)+K*NumberOf(Parameters,Net2) +NumberOf(Parameters,Net3)] <=> Net1. Parameters[1..NumberOf(Parameters,Net1)], Net2[1..K].Parameters[1..NumberOf(Parameters,Net2)], Net[3].Parameters[1..NumberOf(Parameters,Net3)] Net1. OutSignals[1..NumberOf(OutSignals,Net1)], Net2[1..K].OutSignals[1..NumberOf(OutSignals,Net2)] <=> Net2[1..K].InSignals[1..NumberOf(InSignals,Net2)], Net3.InSignals[1..NumberOf(InSignals,Net3)] Loop C N Contents Net1, Net2[K], Net3 InSignals[1..NumberOf(InSignals,Net1)] <=> Net1. InSignals[1..NumberOf(InSignals,Net1)] OutSignals[1..NumberOf(OutSignals,Net3)] <=> Net3.OutSignals[1..NumberOf(OutSignals,Net3)] Parameters[1..NumberOf(Parameters,Net1)+K*NumberOf(Parameters,Net2) +NumberOf(Parameters,Net3)] <=> Net1. Parameters[1..NumberOf(Parameters,Net1)], Net2[1..K].Parameters[1..NumberOf(Parameters,Net2)], Net[3].Parameters[1..NumberOf(Parameters,Net3)] Net1. OutSignals[1..NumberOf(OutSignals,Net1)], Net2[1..K].OutSignals[1..NumberOf(OutSignals,Net2)] <=> Net2[1..K].InSignals[1..NumberOf(InSignals,Net2)], Net3.InSignals[1..NumberOf(InSignals,Net3)] Net3.OutSignals[1..NumberOf(OutSignals,Net3)] <=> Net1. InSignals[1..NumberOf(InSignals,Net1)]
5.3.5.4.3 ɑɚɫɬɢɱɧɨ ɫɨɤɪɚɳɟɧɧɨɟ ɨɩɢɫɚɧɢɟ ȿɫɥɢ ɨɩɢɫɵɜɚɟɦɵɣ ɛɥɨɤ ɞɨɥɠɟɧ ɢɦɟɬɶ ɫɜɹɡɢ, ɭɫɬɚɧɚɜɥɢɜɚɟɦɵɟ ɧɟ ɬɚɤ, ɤɚɤ ɨɩɢɫɚɧɨ ɜ ɪɚɡɞ. «Ɋɚɡɞɟɥ ɨɩɢɫɚɧɢɹ ɫɜɹɡɟɣ», ɬɨ ɫɨɨɬɜɟɬɫɬɜɭɸɳɢɣ ɪɚɡɞɟɥ ɨɩɢɫɚɧɢɹ ɛɥɨɤɚ ɦɨɠɟɬ ɛɵɬɶ ɨɩɢɫɚɧ ɹɜɧɨ ɩɨɥɧɨɫɬɶɸ ɢɥɢ ɱɚɫɬɢɱɧɨ. ȿɫɥɢ ɤɚɤɨɣ ɥɢɛɨ ɪɚɡɞɟɥ ɨɩɢɫɚɧ ɱɚɫɬɢɱɧɨ, ɬɨ ɞɟɣɫɬɜɭɟɬ ɫɥɟɞɭɸɳɟɟ ɩɪɚɜɢɥɨ: ɬɟ ɫɢɝɧɚɥɵ, ɩɚɪɚɦɟɬɪɵ ɢ ɢɯ ɫɜɹɡɢ, ɤɨɬɨɪɵɟ ɨɩɢɫɚɧɵ ɹɜɧɨ, ɛɟɪɭɬɫɹ ɢɡ ɹɜɧɨɝɨ ɨɩɢɫɚɧɢɹ, ɚ ɬɟ ɫɢɝɧɚɥɵ, ɩɚɪɚɦɟɬɪɵ ɢ ɢɯ ɫɜɹɡɢ, ɤɨɬɨɪɵɟ ɧɟ ɮɢɝɭɪɢɪɭɸɬ ɜ ɹɜɧɨɦ ɨɩɢɫɚɧɢɢ ɛɟɪɭɬɫɹ ɢɡ ɨɩɢɫɚɧɢɹ ɩɨ ɭɦɨɥɱɚɧɢɸ. Ɍɚɤ, ɜ ɩɪɢɜɟɞɟɧɧɨɦ ɜ ɪɚɡɞ. «ɉɪɢɦɟɪ ɨɩɢɫɚɧɢɹ ɛɥɨɤɨɜ» ɨɩɢɫɚɧɢɢ ɫɥɨɹ ɬɨɱɟɤ ɜɟɬɜɥɟɧɢɹ BLay ɧɟɜɨɡɦɨɠɧɨ ɢɫɩɨɥɶɡɨɜɚɧɢɟ ɝɟɧɟɪɢɪɭɟɦɨɝɨ ɩɨ ɭɦɨɥɱɚɧɢɸ ɩɨɞɪɚɡɞɟɥɚ ɭɫɬɚɧɨɜɥɟɧɢɹ ɫɜɹɡɢ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɛɥɨɤɚ ɫ ɜɯɨɞɧɵɦɢ ɫɢɝɧɚɥɚɦɢ ɩɨɞɫɟɬɟɣ. ȼɨɡɦɨɠɧɨ ɫɥɟɞɭɸɳɟɟ ɫɨɤɪɚɳɟɧɧɨɟ ɨɩɢɫɚɧɢɟ. {ɋɥɨɣ ɬɨɱɟɤ ɜɟɬɜɥɟɧɢɹ} Layer BLay( N,M : Long) Contents Branch(N)[M] {ȼ ɫɨɫɬɚɜ ɫɥɨɹ ɜɯɨɞɢɬ M ɬɨɱɟɤ ɜɟɬɜɥɟɧɢɹ} Connections {ȼɵɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɜ ɩɨɪɹɞɤɟ ɩɟɪɜɵɣ ɫ ɤɚɠɞɨɣ ɬɨɱɤɢ ɜɟɬɜɥɟɧɢɹ, ɡɚɬɟɦ ɜɬɨɪɨɣ ɢ ɬ.ɞ. } OutSignals[1..N * M] <=> Branch[+:1..M].OutSignals[1..N] End {Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɫɥɨɹ Ɍɨɱɟɤ ɜɟɬɜɥɟɧɢɹ}
5.3.5.4.4 ɉɪɢɦɟɪ ɫɨɤɪɚɳɟɧɧɨɝɨ ɨɩɢɫɚɧɢɹ ɛɥɨɤɨɜ ɉɪɢ ɨɩɢɫɚɧɢɢ ɛɥɨɤɨɜ ɢɫɩɨɥɶɡɭɸɬɫɹ ɷɥɟɦɟɧɬɵ, ɨɩɢɫɚɧɧɵɟ ɜ ɛɢɛɥɢɨɬɟɤɟ Elements, ɩɪɢɜɟɞɟɧɧɨɣ ɜ ɪɚɡɞ. "ɉɪɢɦɟɪ ɨɩɢɫɚɧɢɹ ɷɥɟɦɟɧɬɨɜ". NetBibl SubNets Used Elements; {Ȼɢɛɥɢɨɬɟɤɚ ɩɨɞɫɟɬɟɣ, ɢɫɩɨɥɶɡɭɸɳɚɹ ɛɢɛɥɢɨɬɟɤɭ Elements} {ɋɢɝɦɨɢɞɧɵɣ ɧɟɣɪɨɧ ɫ ɩɪɨɢɡɜɨɥɶɧɵɦ ɫɭɦɦɚɬɨɪɨɦ ɧɚ N ɜɯɨɞɨɜ} Cascad NSigm(aSum : Block; N : Long; Char : Real)
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{ȼ ɫɨɫɬɚɜ ɤɚɫɤɚɞɚ ɜɯɨɞɢɬ ɩɪɨɢɡɜɨɥɶɧɵɣ ɫɭɦɦɚɬɨɪ ɧɚ N ɜɯɨɞɨɜ ɢ ɫɢɝɦɨɢɞɧɵɣ ɧɟɣɪɨɧ ɫ ɧɟɨɛɭɱɚɟɦɨɣ ɯɚɪɚɤɬɟɪɢɫɬɢɤɨɣ} Contents aSum(N), S_NotTrain(Char) End {ɋɥɨɣ ɫɢɝɦɨɢɞɧɵɯ ɧɟɣɪɨɧɨɜ ɫ ɩɪɨɢɡɜɨɥɶɧɵɦɢ ɫɭɦɦɚɬɨɪɚɦɢ ɧɚ N ɜɯɨɞɨɜ} Layer Lay1(aSum : Block; N,M : Long; Char : Real) Contents Sigm: NSigm(aSum,N,Char)[M] {ȼ ɫɨɫɬɚɜ ɫɥɨɹ ɜɯɨɞɢɬ M ɧɟɣɪɨɧɨɜ} End {ɋɥɨɣ ɬɨɱɟɤ ɜɟɬɜɥɟɧɢɹ} Layer BLay( N,M : Long) Contents Branch(N)[M] {ȼ ɫɨɫɬɚɜ ɫɥɨɹ ɜɯɨɞɢɬ M ɬɨɱɟɤ ɜɟɬɜɥɟɧɢɹ} Connections {ȼɵɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɜ ɩɨɪɹɞɤɟ ɩɟɪɜɵɣ ɫ ɤɚɠɞɨɣ ɬɨɱɤɢ ɜɟɬɜɥɟɧɢɹ, ɡɚɬɟɦ ɜɬɨɪɨɣ ɢ ɬ.ɞ. } OutSignals[1..N * M] <=> Branch[+:1..M].OutSignals[1..N] End {ɉɨɥɧɵɣ ɫɥɨɣ ɫɢɝɦɨɢɞɧɵɯ ɧɟɣɪɨɧɨɜ ɫ ɩɪɨɢɡɜɨɥɶɧɵɦɢ ɫɭɦɦɚɬɨɪɚɦɢ ɧɚ N ɜɯɨɞɨɜ} Cascad FullLay(aSum : Block; N,M : Long; Char : Real) Contents BLay1(M,N), Lay1(aSum,N,M,Char) {ɋɥɨɣ ɬɨɱɟɤ ɜɟɬɜɥɟɧɢɹ ɢ ɫɥɨɣ ɧɟɣɪɨɧɨɜ} End {Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɫɥɨɹ ɫɢɝɦɨɢɞɧɵɯ ɧɟɣɪɨɧɨɜ ɫ ɩɪɨɢɡɜɨɥɶɧɵɦ ɫɭɦɦɚɬɨɪɨɦ} {ɋɟɬɶ ɫ ɫɢɝɦɨɢɞɧɵɦɢ ɧɟɣɪɨɧɚɦɢ ɢ ɩɪɨɢɡɜɨɥɶɧɵɦɢ ɫɭɦɦɚɬɨɪɚɦɢ, ɫɨɞɟɪɠɚɳɚɹ Input – ɱɢɫɥɨ ɧɟɣɪɨɧɨɜ ɧɚ ɜɯɨɞɧɨɦ ɫɥɨɟ; Output – ɱɢɫɥɨ ɧɟɣɪɨɧɨɜ ɧɚ ɜɵɯɨɞɧɨɦ ɫɥɨɟ (ɱɢɫɥɨ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ); Hidden – ɱɢɫɥɨ ɧɟɣɪɨɧɨɜ ɧɚ H>0 ɫɤɪɵɬɵɯ ɫɥɨɹɯ; N – ɱɢɫɥɨ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɜɫɟ ɜɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɩɨɞɚɸɬɫɹ ɧɚ ɜɫɟ ɧɟɣɪɨɧɵ ɜɯɨɞɧɨɝɨ ɫɥɨɹ} Cascad Net1(aSum : Block; Char : Real; Input, Output, Hidden, H, N : Long) {ɉɨɞ ɬɪɟɦɹ ɪɚɡɧɵɦɢ ɩɫɟɜɞɨɧɢɦɚɦɢ ɢɫɩɨɥɶɡɭɟɬɫɹ ɨɞɧɚ ɢ ɬɚɠɟ ɩɨɞɫɟɬɶ ɫ ɪɚɡɧɵɦɢ ɩɚɪɚɦɟɬɪɚɦɢ. ɂɫɩɨɥɶɡɨɜɚɧɢɟ ɩɫɟɜɞɨɧɢɦɨɜ ɧɟɨɛɯɨɞɢɦɨ ɞɚɠɟ ɩɪɢ ɫɨɤɪɚɳɟɧɧɨɦ ɨɩɢɫɚɧɢɢ} Contents In: FullLay(aSum,N,Input,Char), Hid1: FullLay(aSum,Input,Hidden,Char) Hid2: FullLay(aSum,Hidden,Hidden,Char)[H-1] {ɉɭɫɬɨ ɩɪɢ H=1} Out: FullLay(aSum,Hidden,Output,Char) End {ɉɨɥɧɨɫɜɹɡɧɚɹ ɫɟɬɶ ɫ M ɫɢɝɦɨɢɞɧɵɦɢ ɧɟɣɪɨɧɚɦɢ ɧɚ Ʉ ɬɚɤɬɨɜ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ ɫ ɧɟɜɵɞɟɥɟɧɧɵɦ ɜɯɨɞɧɵɦ ɫɥɨɟɦ ɧɚ M ɫɢɝɧɚɥɨɜ. ȼɫɟ ɩɚɪɚɦɟɬɪɵ ɨɝɪɚɧɢɱɟɧɵ ɩɨ ɚɛɫɨɥɸɬɧɨɦɭ ɡɧɚɱɟɧɢɸ ɟɞɢɧɢɰɟɣ} Loop Circle(aSum : Block; Char : Real; M, K : Long) K Contents FullLay(aSum,M,M,Char) ParamDef DefaultType -1 1 End {ɉɨɥɧɨɫɜɹɡɧɚɹ ɫɟɬɶ ɫ Ɇ ɫɢɝɦɨɢɞɧɵɦɢ ɧɟɣɪɨɧɚɦɢ ɧɚ Ʉ ɬɚɤɬɨɜ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ ɫ ɜɵɞɟɥɟɧɧɵɦ ɜɯɨɞɧɵɦ ɫɥɨɟɦ ɧɚ N ɫɢɝɧɚɥɨɜ. ȼɫɟ ɜɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɩɨɞɚɸɬɫɹ ɧɚ ɜɯɨɞ ɤɚɠɞɨɝɨ ɧɟɣɪɨɧɚ ɜɯɨɞɧɨɝɨ ɫɥɨɹ} Cascad Net2: (aSum : Block; Char : Real; M, K, N : Long) Contents In: FullLay(aSum,N,M,Char), {ȼɯɨɞɧɨɣ ɫɥɨɣ} Net: Circle(aSum,Char,M,K) {ɉɨɥɧɨɫɜɹɡɧɚɹ ɫɟɬɶ} End Cascad Hopf(N : Long) Contents Sum(N),Sign_Easy End
{ɇɟɣɪɨɧ ɫɟɬɢ ɏɨɩɮɢɥɞɚ ɢɡ N ɧɟɣɪɨɧɨɜ} {ɋɭɦɦɚɬɨɪ ɢ ɩɨɪɨɝɨɜɵɣ ɷɥɟɦɟɧɬ}
{ɋɥɨɣ ɧɟɣɪɨɧɨɜ ɏɨɩɮɢɥɞɚ}
CHAP5-3.DOC
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Layer HLay(N : Long) Contents Hop: Hopf(N)[N] End
{ȼ ɫɨɫɬɚɜ ɫɥɨɹ ɜɯɨɞɢɬ N ɧɟɣɪɨɧɨɜ}
{ɋɟɬɶ ɏɨɩɮɢɥɞɚ ɢɡ N ɧɟɣɪɨɧɨɜ} Until Hopfield(N : Long) InSignals=OutSignals Contents BLay(N,N),HLay(N) End
{ɋɥɨɣ ɬɨɱɟɤ ɜɟɬɜɥɟɧɢɹ ɢ ɫɥɨɣ ɧɟɣɪɨɧɨɜ}
End NetLib
5.4 ɋɬɚɧɞɚɪɬ ɜɬɨɪɨɝɨ ɭɪɨɜɧɹ ɤɨɦɩɨɧɟɧɬɚ ɫɟɬɶ ȼ ɞɚɧɧɨɦ ɪɚɡɞɟɥɟ ɝɥɚɜɵ ɪɚɫɫɦɨɬɪɟɧɵ ɜɫɟ ɡɚɩɪɨɫɵ, ɢɫɩɨɥɧɹɟɦɵɟ ɤɨɦɩɨɧɟɧɬɨɦ ɫɟɬɶ. ɉɪɟɠɞɟ ɱɟɦ ɩɪɢɫɬɭɩɚɬɶ ɤ ɨɩɢɫɚɧɢɸ ɫɬɚɧɞɚɪɬɚ ɡɚɩɪɨɫɨɜ ɤɨɦɩɨɧɟɧɬɚ ɫɟɬɶ ɫɥɟɞɭɟɬ ɜɵɞɟɥɢɬɶ ɜɵɩɨɥɧɹɟɦɵɟ ɢɦ ɮɭɧɤɰɢɢ. ɑɬɨ ɞɨɥɠɟɧ ɞɟɥɚɬɶ ɤɨɦɩɨɧɟɧɬ ɫɟɬɶ? Ɉɱɟɜɢɞɧɨ, ɱɬɨ ɩɪɟɠɞɟ ɜɫɟɝɨ ɨɧ ɞɨɥɠɟɧ ɭɦɟɬɶ ɜɵɩɨɥɧɹɬɶ ɬɚɤɢɟ ɮɭɧɤɰɢɢ, ɤɚɤ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɟ ɜɩɟɪɟɞ (ɪɚɛɨɬɚ ɨɛɭɱɟɧɧɨɣ ɫɟɬɢ) ɢ ɧɚɡɚɞ (ɜɵɱɢɫɥɟɧɢɟ ɜɟɤɬɨɪɚ ɩɨɩɪɚɜɨɤ ɢɥɢ ɝɪɚɞɢɟɧɬɚ ɞɥɹ ɨɛɭɱɟɧɢɹ), ɦɨɞɟɪɧɢɡɚɰɢɸ ɩɚɪɚɦɟɬɪɨɜ (ɨɛɭɱɟɧɢɟ ɫɟɬɢ) ɢ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ (ɨɛɭɱɟɧɢɟ ɩɪɢɦɟɪɚ). Ʉɪɨɦɟ ɬɨɝɨ ɤɨɦɩɨɧɟɧɬ ɫɟɬɶ ɞɨɥɠɟɧ ɭɦɟɬɶ ɱɢɬɚɬɶ ɫɟɬɶ ɫ ɞɢɫɤɚ ɢ ɡɚɩɢɫɵɜɚɬɶ ɟɟ ɧɚ ɞɢɫɤ. ɇɟɨɛɯɨɞɢɦɨ ɬɚɤ ɠɟ ɩɪɟɞɭɫɦɨɬɪɟɬɶ ɜɨɡɦɨɠɧɨɫɬɶ ɫɨɡɞɚɜɚɬɶ ɫɟɬɶ ɢ ɪɟɞɚɤɬɢɪɨɜɚɬɶ ɟɟ ɫɬɪɭɤɬɭɪɭ. ɗɬɢ ɞɜɟ ɮɭɧɤɰɢɨɧɚɥɶɧɵɟ ɜɨɡɦɨɠɧɨɫɬɢ ɧɟ ɫɜɹɡɚɧɵ ɧɚɩɪɹɦɭɸ ɫ ɪɚɛɨɬɨɣ (ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɟɦ ɢ ɨɛɭɱɟɧɢɟɦ) ɫɟɬɢ. Ɍɚɤɢɦ ɨɛɪɚɡɨɦ, ɧɟɨɛɯɨɞɢɦɨ ɜɵɞɟɥɢɬɶ ɫɟɪɜɢɫɧɭɸ ɤɨɦɩɨɧɟɧɬɭ – ɪɟɞɚɤɬɨɪ ɫɟɬɟɣ. Ʉɨɦɩɨɧɟɧɬ ɪɟɞɚɤɬɨɪ ɫɟɬɟɣ ɩɨɡɜɨɥɹɟɬ ɫɨɡɞɚɜɚɬɶ ɢ ɢɡɦɟɧɹɬɶ ɫɬɪɭɤɬɭɪɭ ɫɟɬɢ, ɦɨɞɟɪɧɢɡɢɪɨɜɚɬɶ ɨɛɭɱɚɟɦɵɟ ɩɚɪɚɦɟɬɪɵ ɜ «ɪɭɱɧɨɦ» ɪɟɠɢɦɟ.
5.4.1 Ɂɚɩɪɨɫɵ ɤ ɤɨɦɩɨɧɟɧɬɟ ɫɟɬɶ Ɂɚɩɪɨɫɵ ɤ ɤɨɦɩɨɧɟɧɬɭ ɫɟɬɶ ɦɨɠɧɨ ɪɚɡɛɢɬɶ ɧɚ ɩɹɬɶ ɝɪɭɩɩ: 1. Ɏɭɧɤɰɢɨɧɢɪɨɜɚɧɢɟ. 2. ɂɡɦɟɧɟɧɢɟ ɩɚɪɚɦɟɬɪɨɜ. 3. Ɋɚɛɨɬɚ ɫɨ ɫɬɪɭɤɬɭɪɨɣ. 4. ɂɧɢɰɢɚɰɢɹ ɪɟɞɚɤɬɨɪɚ ɢ ɤɨɧɫɬɪɭɤɬɨɪɚ ɫɟɬɟɣ. 5. Ɉɛɪɚɛɨɬɤɚ ɨɲɢɛɨɤ. ɉɨɫɤɨɥɶɤɭ ɤɨɦɩɨɧɟɧɬ ɫɟɬɶ ɦɨɠɟɬ ɪɚɛɨɬɚɬɶ ɨɞɧɨɜɪɟɦɟɧɧɨ ɫ ɧɟɫɤɨɥɶɤɢɦɢ ɫɟɬɹɦɢ, ɛɨɥɶɲɢɧɫɬɜɨ ɡɚɩɪɨɫɨɜ ɤ ɫɟɬɢ ɫɨɞɟɪɠɚɬ ɹɜɧɨɟ ɭɤɚɡɚɧɢɟ ɢɦɟɧɢ ɫɟɬɢ. Ɉɬɦɟɬɢɦ, ɱɬɨ ɩɪɢ ɝɟɧɟɪɚɰɢɢ ɡɚɩɪɨɫɨɜ ɜ ɤɚɱɟɫɬɜɟ ɢɦɟɧɢ ɫɟɬɢ ɦɨɠɧɨ ɭɤɚɡɵɜɚɬɶ ɢɦɹ ɥɸɛɨɣ ɩɨɞɫɟɬɢ. Ɍɚɤɢɦ ɨɛɪɚɡɨɦ, ɢɟɪɚɪɯɢɱɟɫɤɚɹ ɫɬɪɭɤɬɭɪɚ ɫɟɬɢ, ɨɩɢɫɚɧɧɚɹ ɜ ɫɬɚɧɞɚɪɬɟ ɹɡɵɤɚ ɨɩɢɫɚɧɢɹ ɫɟɬɟɣ, ɩɨɡɜɨɥɹɟɬ ɪɚɛɨɬɚɬɶ ɫ ɤɚɠɞɵɦ ɛɥɨɤɨɦ ɢɥɢ ɷɥɟɦɟɧɬɨɦ ɫɟɬɢ ɤɚɤ ɫ ɨɬɞɟɥɶɧɨɣ ɫɟɬɶɸ. ɇɢɠɟ ɩɪɢɜɟɞɟɧɨ ɨɩɢɫɚɧɢɟ ɜɫɟɯ ɡɚɩɪɨɫɨɜ ɤ ɤɨɦɩɨɧɟɧɬɭ ɫɟɬɶ. Ʉɚɠɞɵɣ ɡɚɩɪɨɫ ɹɜɥɹɟɬɫɹ ɥɨɝɢɱɟɫɤɨɣ ɮɭɧɤɰɢɟɣ, ɜɨɡɜɪɚɳɚɸɳɟɣ ɡɧɚɱɟɧɢɟ ɢɫɬɢɧɚ, ɟɫɥɢ ɡɚɩɪɨɫ ɜɵɩɨɥɧɟɧ ɭɫɩɟɲɧɨ, ɢ ɥɨɠɶ – ɩɪɢ Ɍɚɛɥɢɰɚ 3. Ɂɧɚɱɟɧɢɹ ɩɪɟɞɨɩɪɟɞɟɥɟɧɧɵɯ ɤɨɧɫɬɚɧɬ ɇɚɡɜɚɧɢɟȼɟɥɢɱɢɧɚɁɧɚɱɟɧɢɟ InSignals 0 ȼɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɩɪɹɦɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ OutSignals 1 ȼɵɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɩɪɹɦɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ Ɋarameters 2 ɉɚɪɚɦɟɬɪɵ InSignalMask 3 Ɇɚɫɤɚ ɨɛɭɱɚɟɦɨɫɬɢ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ParamMask 4 Ɇɚɫɤɚ ɨɛɭɱɚɟɦɨɫɬɢ ɩɚɪɚɦɟɬɪɨɜ BackInSignals 5 ȼɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ (ɩɨɩɪɚɜɤɢ) BackOutSignals 6 ȼɵɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ (ɩɨɩɪɚɜɤɢ) BackɊarameters 7 ɉɨɩɪɚɜɤɢ ɤ ɩɚɪɚɦɟɬɪɚɦ Element 0 Ɍɢɩ ɩɨɞɫɟɬɢ – ɷɥɟɦɟɧɬ Layer 1 Ɍɢɩ ɩɨɞɫɟɬɢ – ɫɥɨɣ Cascad 2 Ɍɢɩ ɩɨɞɫɟɬɢ – ɩɪɨɫɬɨɣ ɤɚɫɤɚɞ CicleFor 3 Ɍɢɩ ɩɨɞɫɟɬɢ – ɰɢɤɥ ɫ ɡɚɞɚɧɧɵɦ ɱɢɫɥɨɦ ɩɪɨɯɨɞɨɜ CicleUntil 4 Ɍɢɩ ɩɨɞɫɟɬɢ – ɰɢɤɥ ɩɨ ɭɫɥɨɜɢɸ ɨɲɢɛɨɱɧɨɦ ɡɚɜɟɪɲɟɧɢɢ ɢɫɩɨɥɧɟɧɢɹ ɡɚɩɪɨɫɚ.
CHAP5-3.DOC
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ɉɪɢ ɜɵɡɨɜɟ ɪɹɞɚ ɡɚɩɪɨɫɨɜ ɢɫɩɨɥɶɡɭɸɬɫɹ ɩɪɟɞɨɩɪɟɞɟɥɟɧɧɵɟ ɤɨɧɫɬɚɧɬɵ. ɂɯ ɡɧɚɱɟɧɢɹ ɩɪɢɜɟɞɟɧɵ ɜ ɬɚɛɥ. 3.
5.4.2 Ɂɚɩɪɨɫɵ ɧɚ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɟ Ⱦɜɚ ɡɚɩɪɨɫɚ ɩɟɪɜɨɣ ɝɪɭɩɩɵ ɩɨɡɜɨɥɹɸɬ ɩɪɨɜɨɞɢɬɶ ɩɪɹɦɨɟ ɢ ɨɛɪɚɬɧɨɟ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɟ ɫɟɬɢ. ɉɨ ɫɭɬɢ ɷɬɢ ɡɚɩɪɨɫɵ ɷɤɜɢɜɚɥɟɧɬɧɵ ɜɵɡɨɜɭ ɦɟɬɨɞɨɜ Forw ɢ Back ɫɟɬɢ ɢɥɢ ɟɟ ɷɥɟɦɟɧɬɚ.
5.4.2.1 ȼɵɩɨɥɧɢɬɶ ɩɪɹɦɨɟ Ɏɭɧɤɰɢɨɧɢɪɨɜɚɧɢɟ (Forw)
1. 2. 3.
4. 5.
Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function Forw ( Net : PString; InSignals : PRealArray ) : Logic; C: Logic Forw(PString Net, PRealArray InSignals) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɚ: Net – ɭɤɚɡɚɬɟɥɶ ɧɚ ɫɬɪɨɤɭ ɫɢɦɜɨɥɨɜ, ɫɨɞɟɪɠɚɳɭɸ ɢɦɹ ɫɟɬɢ. InSignals – ɦɚɫɫɢɜ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɫɟɬɢ. ɇɚɡɧɚɱɟɧɢɟ – ɩɪɨɜɨɞɢɬ ɩɪɹɦɨɟ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɟ ɫɟɬɢ, ɭɤɚɡɚɧɧɨɣ ɜ ɩɚɪɚɦɟɬɪɟ Net. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. ȿɫɥɢ Error <> 0, ɬɨ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. ȿɫɥɢ ɜ ɤɚɱɟɫɬɜɟ ɚɪɝɭɦɟɧɬɚ Net ɞɚɧ ɩɭɫɬɨɣ ɭɤɚɡɚɬɟɥɶ, ɢɥɢ ɭɤɚɡɚɬɟɥɶ ɧɚ ɩɭɫɬɭɸ ɫɬɪɨɤɭ, ɬɨ ɢɫɩɨɥɧɹɸɳɢɦ ɡɚɩɪɨɫ ɨɛɴɟɤɬɨɦ ɹɜɥɹɟɬɫɹ ɩɟɪɜɚɹ ɫɟɬɶ ɜ ɫɩɢɫɤɟ ɫɟɬɟɣ ɤɨɦɩɨɧɟɧɬɚ ɫɟɬɶ. ȿɫɥɢ ɫɩɢɫɨɤ ɫɟɬɟɣ ɤɨɦɩɨɧɟɧɬɚ ɫɟɬɶ ɩɭɫɬ ɢɥɢ ɢɦɹ ɫɟɬɢ, ɩɟɪɟɞɚɧɧɨɟ ɜ ɚɪɝɭɦɟɧɬɟ Net ɜ ɷɬɨɦ ɫɩɢɫɤɟ ɧɟ ɧɚɣɞɟɧɨ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 301 – ɧɟɜɟɪɧɨɟ ɢɦɹ ɫɟɬɢ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. ȼɵɡɵɜɚɟɬɫɹ ɦɟɬɨɞ Forw ɫɟɬɢ, ɢɦɹ ɤɨɬɨɪɨɣ ɛɵɥɨ ɭɤɚɡɚɧɨ ɜ ɚɪɝɭɦɟɧɬɟ Net. ȿɫɥɢ ɜɨ ɜɪɟɦɹ ɜɵɩɨɥɧɟɧɢɹ ɡɚɩɪɨɫɚ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ, ɬɨ ɝɟɧɟɪɢɪɭɟɬɫɹ ɜɧɭɬɪɟɧɧɹɹ ɨɲɢɛɤɚ 304 ɨɲɢɛɤɚ ɩɪɹɦɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ. ɍɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ. ȼɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. ȼ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɭɫɩɟɲɧɨ ɡɚɜɟɪɲɚɟɬɫɹ.
5.4.2.2 ȼɵɩɨɥɧɢɬɶ ɨɛɪɚɬɧɨɟ Ɏɭɧɤɰɢɨɧɢɪɨɜɚɧɢɟ (Back)
1. 2. 3.
4. 5.
Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function Back( Net : PString; BackOutSignals : PRealArray) : Logic; C: Logic Back(PString Net, PRealArray BackOutSignals) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɚ: Net – ɭɤɚɡɚɬɟɥɶ ɧɚ ɫɬɪɨɤɭ ɫɢɦɜɨɥɨɜ, ɫɨɞɟɪɠɚɳɭɸ ɢɦɹ ɫɟɬɢ. BackOutSignals – ɦɚɫɫɢɜ ɩɪɨɢɡɜɨɞɧɵɯ ɮɭɧɤɰɢɢ ɨɰɟɧɤɢ ɩɨ ɜɵɯɨɞɧɵɦ ɫɢɝɧɚɥɚɦ ɫɟɬɢ. ɇɚɡɧɚɱɟɧɢɟ – ɩɪɨɜɨɞɢɬ ɨɛɪɚɬɧɨɟ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɟ ɫɟɬɢ, ɭɤɚɡɚɧɧɨɣ ɜ ɩɚɪɚɦɟɬɪɟ Net. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. ȿɫɥɢ Error <> 0, ɬɨ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. ȿɫɥɢ ɜ ɤɚɱɟɫɬɜɟ ɚɪɝɭɦɟɧɬɚ Net ɞɚɧ ɩɭɫɬɨɣ ɭɤɚɡɚɬɟɥɶ, ɢɥɢ ɭɤɚɡɚɬɟɥɶ ɧɚ ɩɭɫɬɭɸ ɫɬɪɨɤɭ, ɬɨ ɢɫɩɨɥɧɹɸɳɢɦ ɡɚɩɪɨɫ ɨɛɴɟɤɬɨɦ ɹɜɥɹɟɬɫɹ ɩɟɪɜɚɹ ɫɟɬɶ ɜ ɫɩɢɫɤɟ ɫɟɬɟɣ ɤɨɦɩɨɧɟɧɬɚ ɫɟɬɶ. ȿɫɥɢ ɫɩɢɫɨɤ ɫɟɬɟɣ ɤɨɦɩɨɧɟɧɬɚ ɫɟɬɶ ɩɭɫɬ ɢɥɢ ɢɦɹ ɫɟɬɢ, ɩɟɪɟɞɚɧɧɨɟ ɜ ɚɪɝɭɦɟɧɬɟ Net ɜ ɷɬɨɦ ɫɩɢɫɤɟ ɧɟ ɧɚɣɞɟɧɨ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 301 – ɧɟɜɟɪɧɨɟ ɢɦɹ ɫɟɬɢ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. ȼɵɡɵɜɚɟɬɫɹ ɦɟɬɨɞ Back ɫɟɬɢ, ɢɦɹ ɤɨɬɨɪɨɣ ɛɵɥɨ ɭɤɚɡɚɧɨ ɜ ɚɪɝɭɦɟɧɬɟ Net. ȿɫɥɢ ɜɨ ɜɪɟɦɹ ɜɵɩɨɥɧɟɧɢɹ ɡɚɩɪɨɫɚ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ, ɬɨ ɝɟɧɟɪɢɪɭɟɬɫɹ ɜɧɭɬɪɟɧɧɹɹ ɨɲɢɛɤɚ 305 ɨɲɢɛɤɚ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ. ɍɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ. ȼɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. ȼ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɭɫɩɟɲɧɨ ɡɚɜɟɪɲɚɟɬɫɹ.
5.4.3 Ɂɚɩɪɨɫɵ ɧɚ ɢɡɦɟɧɟɧɢɟ ɩɚɪɚɦɟɬɪɨɜ. Ʉɨ ɜɬɨɪɨɣ ɝɪɭɩɩɟ ɡɚɩɪɨɫɨɜ ɨɬɧɨɫɹɬɫɹ ɱɟɬɵɪɟ ɡɚɩɪɨɫɚ: Modify – ɦɨɞɢɮɢɤɚɰɢɹ ɩɚɪɚɦɟɬɪɨɜ, ɨɛɵɱɧɨ ɧɚɡɵɜɚɟɦɚɹ ɨɛɭɱɟɧɢɟɦ, ModifyMask – ɦɨɞɢɮɢɤɚɰɢɹ ɦɚɫɤɢ ɨɛɭɱɚɟɦɵɯ ɫɢɧɚɩɫɨɜ, NullGradient – ɨɛɧɭɥɟɧɢɟ ɝɪɚɞɢɟɧɬɚ ɢ RandomDirection – ɫɝɟɧɟɪɢɪɨɜɚɬɶ ɫɥɭɱɚɣɧɨɟ ɧɚɩɪɚɜɥɟɧɢɟ ɫɩɭɫɤɚ.
5.4.3.1 ɉɪɨɜɟɫɬɢ ɨɛɭɱɟɧɢɟ (Modify) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal:
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Function Modify( Net : PString; OldStep, NewStep : Real; Tipe : Integer; Grad : PRealArray ) : Logic; C: Logic Modify(PString Net, Real OldStep, Real NewStep, Integer Tipe, PRealArray Grad) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: Net – ɭɤɚɡɚɬɟɥɶ ɧɚ ɫɬɪɨɤɭ ɫɢɦɜɨɥɨɜ, ɫɨɞɟɪɠɚɳɭɸ ɢɦɹ ɫɟɬɢ. OldStep, NewStep – ɩɚɪɚɦɟɬɪɵ ɨɛɭɱɟɧɢɹ. Tipe – ɨɞɧɚ ɢɡ ɤɨɧɫɬɚɧɬ InSignals ɢɥɢ Parameters. Grad – ɚɞɪɟɫ ɦɚɫɫɢɜɚ ɩɨɩɪɚɜɨɤ ɢɥɢ ɩɭɫɬɨɣ ɭɤɚɡɚɬɟɥɶ. ɇɚɡɧɚɱɟɧɢɟ – ɩɪɨɜɨɞɢɬ ɨɛɭɱɟɧɢɟ ɩɚɪɚɦɟɬɪɨɜ ɢɥɢ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɫɟɬɢ, ɭɤɚɡɚɧɧɨɣ ɜ ɩɚɪɚɦɟɬɪɟ Net. 1. 2. 3.
4. 5.
Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. ȿɫɥɢ Error <> 0, ɬɨ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. ȿɫɥɢ ɜ ɤɚɱɟɫɬɜɟ ɚɪɝɭɦɟɧɬɚ Net ɞɚɧ ɩɭɫɬɨɣ ɭɤɚɡɚɬɟɥɶ, ɢɥɢ ɭɤɚɡɚɬɟɥɶ ɧɚ ɩɭɫɬɭɸ ɫɬɪɨɤɭ, ɬɨ ɢɫɩɨɥɧɹɸɳɢɦ ɡɚɩɪɨɫ ɨɛɴɟɤɬɨɦ ɹɜɥɹɟɬɫɹ ɩɟɪɜɚɹ ɫɟɬɶ ɜ ɫɩɢɫɤɟ ɫɟɬɟɣ ɤɨɦɩɨɧɟɧɬɚ ɫɟɬɶ. ȿɫɥɢ ɫɩɢɫɨɤ ɫɟɬɟɣ ɤɨɦɩɨɧɟɧɬɚ ɫɟɬɶ ɩɭɫɬ ɢɥɢ ɢɦɹ ɫɟɬɢ, ɩɟɪɟɞɚɧɧɨɟ ɜ ɚɪɝɭɦɟɧɬɟ Net ɜ ɷɬɨɦ ɫɩɢɫɤɟ ɧɟ ɧɚɣɞɟɧɨ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 301 – ɧɟɜɟɪɧɨɟ ɢɦɹ ɫɟɬɢ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. ȿɫɥɢ ɚɪɝɭɦɟɧɬ Grad ɫɨɞɟɪɠɢɬ ɩɭɫɬɨɣ ɭɤɚɡɚɬɟɥɶ, ɬɨ ɩɨɩɪɚɜɤɢ ɛɟɪɭɬɫɹ ɢɡ ɦɚɫɫɢɜɚ Back.Parameters ɢɥɢ Back.InputSignals ɜ ɡɚɜɢɫɢɦɨɫɬɢ ɨɬ ɡɧɚɱɟɧɢɹ ɚɪɝɭɦɟɧɬɚ Tipe. ȼ ɡɚɜɢɫɢɦɨɫɬɢ ɨɬ ɡɧɚɱɟɧɢɹ ɚɪɝɭɦɟɧɬɚ Tipe ɞɥɹ ɤɚɠɞɨɝɨ ɩɚɪɚɦɟɬɪɚ ɢɥɢ ɜɯɨɞɧɨɝɨ ɫɢɝɧɚɥɚ P, ɩɪɢ ɭɫɥɨɜɢɢ, ɱɬɨ ɫɨɨɬɜɟɬɫɬɜɭɸɳɢɣ ɟɦɭ ɷɥɟɦɟɧɬ ɦɚɫɤɢ ɨɛɭɱɚɟɦɨɫɬɢ, ɫɨɨɬɜɟɬɫɬɜɭɸɳɟɣ ɚɪɝɭɦɟɧɬɭ Tipe ɪɚɜɟɧ -1 (ɡɧɚɱɟɧɢɟ ɢɫɬɢɧɚ) ɜɵɩɨɥɧɹɟɬɫɹ ɫɥɟɞɭɸɳɚɹ ɩɪɨɰɟɞɭɪɚ: P1=P*OldStep+DP*NewStep. ȿɫɥɢ ɞɥɹ ɬɢɩɚ, ɤɨɬɨɪɵɦ ɨɩɢɫɚɧ ɩɚɪɚɦɟɬɪ P, ɡɚɞɚɧɵ ɦɢɧɢɦɚɥɶɧɨɟ ɢ ɦɚɤɫɢɦɚɥɶɧɨɟ ɡɧɚɱɟɧɢɹ, ɬɨ: P2=Pmin, ɩɪɢ P1
Pmax P2=P1 ɜ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ
5.4.3.2 ɂɡɦɟɧɢɬɶ ɦɚɫɤɭ ɨɛɭɱɚɟɦɨɫɬɢ (ModifyMask) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function ModifyMask( Net : PString; Tipe : Integer; NewMask: PLogicArray ) : Logic; C: Logic Modify(PString Net, Integer Tipe, PLogicArray NewMask) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: Net – ɭɤɚɡɚɬɟɥɶ ɧɚ ɫɬɪɨɤɭ ɫɢɦɜɨɥɨɜ, ɫɨɞɟɪɠɚɳɭɸ ɢɦɹ ɫɟɬɢ. Tipe – ɨɞɧɚ ɢɡ ɤɨɧɫɬɚɧɬ InSignals ɢɥɢ Parameters. NewMask – ɧɨɜɚɹ ɦɚɫɤɚ ɨɛɭɱɚɟɦɨɫɬɢ. ɇɚɡɧɚɱɟɧɢɟ – Ɂɚɦɟɧɹɟɬ ɦɚɫɤɭ ɨɛɭɱɚɟɦɨɫɬɢ ɩɚɪɚɦɟɬɪɨɜ ɢɥɢ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɫɟɬɢ, ɭɤɚɡɚɧɧɨɣ ɜ ɩɚɪɚɦɟɬɪɟ Net. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. 1. ȿɫɥɢ Error <> 0, ɬɨ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 2. ȿɫɥɢ ɜ ɤɚɱɟɫɬɜɟ ɚɪɝɭɦɟɧɬɚ Net ɞɚɧ ɩɭɫɬɨɣ ɭɤɚɡɚɬɟɥɶ, ɢɥɢ ɭɤɚɡɚɬɟɥɶ ɧɚ ɩɭɫɬɭɸ ɫɬɪɨɤɭ, ɬɨ ɢɫɩɨɥɧɹɸɳɢɦ ɡɚɩɪɨɫ ɨɛɴɟɤɬɨɦ ɹɜɥɹɟɬɫɹ ɩɟɪɜɚɹ ɫɟɬɶ ɜ ɫɩɢɫɤɟ ɫɟɬɟɣ ɤɨɦɩɨɧɟɧɬɚ ɫɟɬɶ. 3. ȿɫɥɢ ɫɩɢɫɨɤ ɫɟɬɟɣ ɤɨɦɩɨɧɟɧɬɚ ɫɟɬɶ ɩɭɫɬ ɢɥɢ ɢɦɹ ɫɟɬɢ, ɩɟɪɟɞɚɧɧɨɟ ɜ ɚɪɝɭɦɟɧɬɟ Net ɜ ɷɬɨɦ ɫɩɢɫɤɟ ɧɟ ɧɚɣɞɟɧɨ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 301 – ɧɟɜɟɪɧɨɟ ɢɦɹ ɫɟɬɢ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 4. ȼ ɡɚɜɢɫɢɦɨɫɬɢ ɨɬ ɡɧɚɱɟɧɢɹ ɩɚɪɚɦɟɬɪɚ Tipe ɡɚɦɟɧɹɟɬ ɦɚɫɤɭ ɨɛɭɱɚɟɦɨɫɬɢ ɩɚɪɚɦɟɬɪɨɜ ɢɥɢ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɧɚ ɩɟɪɟɞɚɧɧɭɸ ɜ ɩɚɪɚɦɟɬɪɟ NewMask.
5.4.3.3 Ɉɛɧɭɥɢɬɶ ɝɪɚɞɢɟɧɬ (NullGradient) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function NullGradient( Net : PString ) : Logic; C: Logic NullGradient(PString Net)
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Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: Net – ɭɤɚɡɚɬɟɥɶ ɧɚ ɫɬɪɨɤɭ ɫɢɦɜɨɥɨɜ, ɫɨɞɟɪɠɚɳɭɸ ɢɦɹ ɫɟɬɢ.
1. 2. 3.
4.
ɇɚɡɧɚɱɟɧɢɟ – ɩɪɨɢɡɜɨɞɢɬ ɨɛɧɭɥɟɧɢɟ ɝɪɚɞɢɟɧɬɚ ɫɟɬɢ, ɭɤɚɡɚɧɧɨɣ ɜ ɩɚɪɚɦɟɬɪɟ Net. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. ȿɫɥɢ Error <> 0, ɬɨ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. ȿɫɥɢ ɜ ɤɚɱɟɫɬɜɟ ɚɪɝɭɦɟɧɬɚ Net ɞɚɧ ɩɭɫɬɨɣ ɭɤɚɡɚɬɟɥɶ, ɢɥɢ ɭɤɚɡɚɬɟɥɶ ɧɚ ɩɭɫɬɭɸ ɫɬɪɨɤɭ, ɬɨ ɢɫɩɨɥɧɹɸɳɢɦ ɡɚɩɪɨɫ ɨɛɴɟɤɬɨɦ ɹɜɥɹɟɬɫɹ ɩɟɪɜɚɹ ɫɟɬɶ ɜ ɫɩɢɫɤɟ ɫɟɬɟɣ ɤɨɦɩɨɧɟɧɬɚ ɫɟɬɶ. ȿɫɥɢ ɫɩɢɫɨɤ ɫɟɬɟɣ ɤɨɦɩɨɧɟɧɬɚ ɫɟɬɶ ɩɭɫɬ ɢɥɢ ɢɦɹ ɫɟɬɢ, ɩɟɪɟɞɚɧɧɨɟ ɜ ɚɪɝɭɦɟɧɬɟ Net ɜ ɷɬɨɦ ɫɩɢɫɤɟ ɧɟ ɧɚɣɞɟɧɨ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 301 – ɧɟɜɟɪɧɨɟ ɢɦɹ ɫɟɬɢ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. Ɉɛɧɭɥɹɸɬɫɹ ɦɚɫɫɢɜɵ Back.Parameters ɢ Back.OutSignals.
5.4.3.4 ɋɥɭɱɚɣɧɨɟ ɧɚɩɪɚɜɥɟɧɢɟ ɫɩɭɫɤɚ (RandomDirection) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function RandomDirection( Net : PString; Range : Real ) : Logic; C: Logic RandomDirection(PString Net, Real Range) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: Net – ɭɤɚɡɚɬɟɥɶ ɧɚ ɫɬɪɨɤɭ ɫɢɦɜɨɥɨɜ, ɫɨɞɟɪɠɚɳɭɸ ɢɦɹ ɫɟɬɢ. Range – ɨɬɧɨɫɢɬɟɥɶɧɚɹ ɲɢɪɢɧɚ ɢɧɬɟɪɜɚɥɚ, ɧɚ ɤɨɬɨɪɨɦ ɞɨɥɠɧɵ ɛɵɬɶ ɪɚɫɩɪɟɞɟɥɟɧɵ ɡɧɚɱɟɧɢɹ ɫɥɭɱɚɣɧɨɣ ɜɟɥɢɱɢɧɵ.
1. 2. 3.
4.
ɇɚɡɧɚɱɟɧɢɟ – ɝɟɧɟɪɢɪɭɟɬ ɜɟɤɬɨɪ ɫɥɭɱɚɣɧɵɯ ɩɨɩɪɚɜɨɤ ɤ ɩɚɪɚɦɟɬɪɚɦ ɫɟɬɢ. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. ȿɫɥɢ Error <> 0, ɬɨ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. ȿɫɥɢ ɜ ɤɚɱɟɫɬɜɟ ɚɪɝɭɦɟɧɬɚ Net ɞɚɧ ɩɭɫɬɨɣ ɭɤɚɡɚɬɟɥɶ, ɢɥɢ ɭɤɚɡɚɬɟɥɶ ɧɚ ɩɭɫɬɭɸ ɫɬɪɨɤɭ, ɬɨ ɢɫɩɨɥɧɹɸɳɢɦ ɡɚɩɪɨɫ ɨɛɴɟɤɬɨɦ ɹɜɥɹɟɬɫɹ ɩɟɪɜɚɹ ɫɟɬɶ ɜ ɫɩɢɫɤɟ ɫɟɬɟɣ ɤɨɦɩɨɧɟɧɬɚ ɫɟɬɶ. ȿɫɥɢ ɫɩɢɫɨɤ ɫɟɬɟɣ ɤɨɦɩɨɧɟɧɬɚ ɫɟɬɶ ɩɭɫɬ ɢɥɢ ɢɦɹ ɫɟɬɢ, ɩɟɪɟɞɚɧɧɨɟ ɜ ɚɪɝɭɦɟɧɬɟ Net ɜ ɷɬɨɦ ɫɩɢɫɤɟ ɧɟ ɧɚɣɞɟɧɨ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 301 – ɧɟɜɟɪɧɨɟ ɢɦɹ ɫɟɬɢ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. Ɂɚɦɟɳɚɸɬ ɜɫɟ ɡɧɚɱɟɧɢɹ ɦɚɫɫɢɜɚ Back.Parameters ɧɚ ɫɥɭɱɚɣɧɵɟ ɜɟɥɢɱɢɧɵ. ɂɧɬɟɪɜɚɥ ɪɚɫɩɪɟɞɟɥɟɧɢɹ ɫɥɭɱɚɣɧɨɣ ɜɟɥɢɱɢɧɵ ɡɚɜɢɫɢɬ ɨɬ ɬɢɩɚ ɩɚɪɚɦɟɬɪɚ, ɭɤɚɡɚɧɧɨɝɨ ɩɪɢ ɨɩɢɫɚɧɢɢ ɫɟɬɢ (ParamType) ɢ ɚɪɝɭɦɟɧɬɚ Range. ɉɨɥɭɲɢɪɢɧɚ ɢɧɬɟɪɜɚɥɚ ɨɩɪɟɞɟɥɹɟɬɫɹ ɤɚɤ ɩɪɨɢɡɜɟɞɟɧɢɟ ɩɨɥɭɲɢɪɢɧɵ ɢɧɬɟɪɜɚɥɚ ɞɨɩɭɫɬɢɦɵɯ ɡɧɚɱɟɧɢɣ ɩɚɪɚɦɟɬɪɚ, ɭɤɚɡɚɧɧɵɯ ɜ ɪɚɡɞɟɥɟ ParamDef ɨɩɢɫɚɧɢɹ ɫɟɬɢ ɧɚ ɜɟɥɢɱɢɧɭ Range. ɂɧɬɟɪɜɚɥ ɪɚɫɩɪɟɞɟɥɟɧɢɹ ɫɥɭɱɚɣɧɨɣ ɜɟɥɢɱɢɧɵ ɨɩɪɟɞɟɥɹɟɬɫɹ ɤɚɤ [-ɉɨɥɭɲɢɪɢɧɚ; ɉɨɥɭɲɢɪɢɧɚ].
5.4.4 Ɂɚɩɪɨɫɵ, ɪɚɛɨɬɚɸɳɢɟ ɫɨ ɫɬɪɭɤɬɭɪɨɣ ɫɟɬɢ. Ʉ ɬɪɟɬɶɟɣ ɝɪɭɩɩɟ ɨɬɧɨɫɹɬɫɹ ɡɚɩɪɨɫɵ, ɩɨɡɜɨɥɹɸɳɢɟ ɢɡɦɟɧɹɬɶ ɫɬɪɭɤɬɭɪɭ ɫɟɬɢ. ɑɚɫɬɶ ɡɚɩɪɨɫɨɜ ɷɬɨɣ ɝɪɭɩɩɵ ɨɩɢɫɚɧɚ ɜ ɪɚɡɞ. "Ɉɫɬɚɥɶɧɵɟ ɡɚɩɪɨɫɵ".
5.4.4.1 ȼɟɪɧɭɬɶ ɩɚɪɚɦɟɬɪɵ ɫɟɬɢ (nwGetData) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function nwGetData(Net : PString; DataType : Integer; Var Data : PRealArray) : Logic; C: Logic nwGetData(PString Net, Integer DataType, PRealArray* Data) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: Net – ɭɤɚɡɚɬɟɥɶ ɧɚ ɫɬɪɨɤɭ ɫɢɦɜɨɥɨɜ, ɫɨɞɟɪɠɚɳɭɸ ɢɦɹ ɫɟɬɢ. DataType – ɨɞɧɚ ɢɡ ɜɨɫɶɦɢ ɩɪɟɞɨɩɪɟɞɟɥɟɧɧɵɯ ɤɨɧɫɬɚɧɬ, ɨɩɢɫɵɜɚɸɳɢɯ ɬɢɩ ɞɚɧɧɵɯ ɫɟɬɢ. Data – ɜɨɡɜɪɚɳɚɟɦɵɣ ɦɚɫɫɢɜ ɩɚɪɚɦɟɬɪɨɜ ɫɟɬɢ. ɇɚɡɧɚɱɟɧɢɟ – ɜɨɡɜɪɚɳɚɟɬ ɩɚɪɚɦɟɬɪɵ, ɜɯɨɞɧɵɟ ɢɥɢ ɜɵɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɫɟɬɢ, ɭɤɚɡɚɧɧɨɣ ɜ ɚɪɝɭɦɟɧɬɟ Net. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. 1. ȿɫɥɢ ɜ ɤɚɱɟɫɬɜɟ ɚɪɝɭɦɟɧɬɚ Net ɞɚɧ ɩɭɫɬɨɣ ɭɤɚɡɚɬɟɥɶ, ɢɥɢ ɭɤɚɡɚɬɟɥɶ ɧɚ ɩɭɫɬɭɸ ɫɬɪɨɤɭ, ɬɨ ɢɫɩɨɥɧɹɸɳɢɦ ɡɚɩɪɨɫ ɨɛɴɟɤɬɨɦ ɹɜɥɹɟɬɫɹ ɩɟɪɚɜɹ ɫɟɬɶ ɜ ɫɩɢɫɤɟ ɫɟɬɟɣ ɤɨɦɩɨɧɟɧɬɚ ɫɟɬɶ.
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2.
3.
4.
ȿɫɥɢ ɢɦɹ ɫɟɬɢ, ɩɟɪɟɞɚɧɧɨɟ ɜ ɚɪɝɭɦɟɧɬɟ Net ɧɟ ɧɚɣɞɟɧɨ ɜ ɫɩɢɫɤɟ ɫɟɬɟɣ ɤɨɦɩɨɧɟɧɬɚ ɫɟɬɶ ɢɥɢ ɷɬɨɬ ɫɩɢɫɨɤ ɩɭɫɬ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 301 – ɧɟɜɟɪɧɨɟ ɢɦɹ ɫɟɬɢ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. ȿɫɥɢ ɡɧɚɱɟɧɢɟ, ɩɟɪɟɞɚɧɧɨɟ ɜ ɚɪɝɭɦɟɧɬɟ DataType ɛɨɥɶɲɟ ɫɟɦɢ ɢɥɢ ɦɟɧɶɲɟ ɧɭɥɹ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 306 – ɨɲɢɛɨɱɧɵɣ ɬɢɩ ɩɚɪɚɦɟɬɪɚ ɫɟɬɢ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. ȼ ɦɚɫɫɢɜɟ Data ɜɨɡɜɪɚɳɚɸɬɫɹ ɭɤɚɡɚɧɧɵɟ ɜ ɚɪɝɭɦɟɧɬɟ DataType ɩɚɪɚɦɟɬɪɵ ɫɟɬɢ.
5.4.4.2 ɍɫɬɚɧɨɜɢɬɶ ɩɚɪɚɦɟɬɪɵ ɫɟɬɢ (nwSetData) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function nwSetData(Net : PString; DataType : Integer; Var Data : RealArray) : Logic; C: Logic nwSetData(PString Net, Integer DataType, RealArray* Data) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: Net – ɭɤɚɡɚɬɟɥɶ ɧɚ ɫɬɪɨɤɭ ɫɢɦɜɨɥɨɜ, ɫɨɞɟɪɠɚɳɭɸ ɢɦɹ ɫɟɬɢ. DataType – ɨɞɧɚ ɢɡ ɜɨɫɶɦɢ ɩɪɟɞɨɩɪɟɞɟɥɟɧɧɵɯ ɤɨɧɫɬɚɧɬ, ɨɩɢɫɵɜɚɸɳɢɯ ɬɢɩ ɞɚɧɧɵɯ ɫɟɬɢ. Data – ɦɚɫɫɢɜ ɩɚɪɚɦɟɬɪɨɜ ɞɥɹ ɡɚɦɟɳɟɧɢɹ ɬɟɤɭɳɟɝɨ ɦɚɫɫɢɜɚ ɩɚɪɚɦɟɬɪɨɜ ɫɟɬɢ. ɇɚɡɧɚɱɟɧɢɟ – ɡɚɦɟɳɚɟɬ ɩɚɪɚɦɟɬɪɵ, ɜɯɨɞɧɵɟ ɢɥɢ ɜɵɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɫɟɬɢ, ɭɤɚɡɚɧɧɨɣ ɜ ɚɪɝɭɦɟɧɬɟ Net ɧɚ ɡɧɚɱɟɧɢɹ ɢɡ ɦɚɫɫɢɜɚ Data. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. 1. ȿɫɥɢ ɜ ɤɚɱɟɫɬɜɟ ɚɪɝɭɦɟɧɬɚ Net ɞɚɧ ɩɭɫɬɨɣ ɭɤɚɡɚɬɟɥɶ, ɢɥɢ ɭɤɚɡɚɬɟɥɶ ɧɚ ɩɭɫɬɭɸ ɫɬɪɨɤɭ, ɬɨ ɢɫɩɨɥɧɹɸɳɢɦ ɡɚɩɪɨɫ ɨɛɴɟɤɬɨɦ ɹɜɥɹɟɬɫɹ ɩɟɪɜɚɹ ɫɟɬɶ ɜ ɫɩɢɫɤɟ ɫɟɬɟɣ ɤɨɦɩɨɧɟɧɬɚ ɫɟɬɶ. 2. ȿɫɥɢ ɢɦɹ ɫɟɬɢ, ɩɟɪɟɞɚɧɧɨɟ ɜ ɚɪɝɭɦɟɧɬɟ Net ɧɟ ɧɚɣɞɟɧɨ ɜ ɫɩɢɫɤɟ ɫɟɬɟɣ ɤɨɦɩɨɧɟɧɬɚ ɫɟɬɶ ɢɥɢ ɷɬɨɬ ɫɩɢɫɨɤ ɩɭɫɬ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 301 – ɧɟɜɟɪɧɨɟ ɢɦɹ ɫɟɬɢ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 3. ȿɫɥɢ ɡɧɚɱɟɧɢɟ, ɩɟɪɟɞɚɧɧɨɟ ɜ ɚɪɝɭɦɟɧɬɟ DataType ɛɨɥɶɲɟ ɫɟɦɢ ɢɥɢ ɦɟɧɶɲɟ ɧɭɥɹ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 306 – ɨɲɢɛɨɱɧɵɣ ɬɢɩ ɩɚɪɚɦɟɬɪɚ ɫɟɬɢ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 4. Ɂɧɚɱɟɧɢɹ ɩɚɪɚɦɟɬɪɨɜ (ɜɯɨɞɧɵɯ ɢɥɢ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ) ɫɟɬɢ ɡɚɦɟɧɹɸɬɫɹ ɧɚ ɡɧɚɱɟɧɢɹ ɢɡ ɦɚɫɫɢɜɚ Data. ȿɫɥɢ ɞɥɢɧɧɵ ɦɚɫɫɢɜɚ Data ɧɟɞɨɫɬɚɬɨɱɧɨ ɞɥɹ ɡɚɦɟɧɵ ɡɧɚɱɟɧɢɣ ɜɫɟɯ ɩɚɪɚɦɟɬɪɨɜ (ɜɯɨɞɧɵɯ ɢɥɢ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ), ɬɨ ɡɚɦɟɳɚɸɬɫɹ ɬɨɥɶɤɨ ɫɬɨɥɶɤɨ ɷɥɟɦɟɧɬɨɜ ɦɚɫɫɢɜɚ ɩɚɪɚɦɟɬɪɨɜ (ɜɯɨɞɧɵɯ ɢɥɢ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ) ɫɤɨɥɶɤɨ ɷɥɟɦɟɧɬɨɜ ɜ ɦɚɫɫɢɜɟ Data. ȿɫɥɢ ɞɥɢɧɧɚ ɦɚɫɫɢɜɚ Data ɛɨɥɶɲɟ ɞɥɢɧɧɵ ɦɚɫɫɢɜɚ ɩɚɪɚɦɟɬɪɨɜ (ɜɯɨɞɧɵɯ ɢɥɢ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ), ɬɨ ɡɚɦɟɧɹɸɬɫɹ ɜɫɟ ɷɥɟɦɟɧɬɵ ɜɟɤɬɨɪɚ ɩɚɪɚɦɟɬɪɨɜ (ɜɯɨɞɧɵɯ ɢɥɢ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ), ɚ ɥɢɲɧɢɟ ɷɥɟɦɟɧɬɵ ɦɚɫɫɢɜɚ Data ɢɝɧɨɪɢɪɭɸɬɫɹ.
5.4.4.3 ɇɨɪɦɚɥɢɡɨɜɚɬɶ ɫɟɬɶ (NormalizeNet) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function NormalizeNet(Net : PString) : Logic; C: Logic NormalizeNet(PString Net) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɚ: Net – ɭɤɚɡɚɬɟɥɶ ɧɚ ɫɬɪɨɤɭ ɫɢɦɜɨɥɨɜ, ɫɨɞɟɪɠɚɳɭɸ ɢɦɹ ɫɟɬɢ.
1. 2.
3. 4.
ɇɚɡɧɚɱɟɧɢɟ – ɧɨɪɦɚɥɢɡɚɰɢɹ ɫɟɬɢ, ɭɤɚɡɚɧɧɨɣ ɜ ɚɪɝɭɦɟɧɬɟ Net. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. ȿɫɥɢ ɜ ɤɚɱɟɫɬɜɟ ɚɪɝɭɦɟɧɬɚ Net ɞɚɧ ɩɭɫɬɨɣ ɭɤɚɡɚɬɟɥɶ, ɢɥɢ ɭɤɚɡɚɬɟɥɶ ɧɚ ɩɭɫɬɭɸ ɫɬɪɨɤɭ, ɬɨ ɢɫɩɨɥɧɹɸɳɢɦ ɡɚɩɪɨɫ ɨɛɴɟɤɬɨɦ ɹɜɥɹɟɬɫɹ ɩɟɪɜɚɹ ɫɟɬɶ ɜ ɫɩɢɫɤɟ ɫɟɬɟɣ ɤɨɦɩɨɧɟɧɬɚ ɫɟɬɶ. ȿɫɥɢ ɢɦɹ ɫɟɬɢ, ɩɟɪɟɞɚɧɧɨɟ ɜ ɚɪɝɭɦɟɧɬɟ Net ɧɟ ɧɚɣɞɟɧɨ ɜ ɫɩɢɫɤɟ ɫɟɬɟɣ ɤɨɦɩɨɧɟɧɬɚ ɫɟɬɶ ɢɥɢ ɷɬɨɬ ɫɩɢɫɨɤ ɩɭɫɬ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 301 – ɧɟɜɟɪɧɨɟ ɢɦɹ ɫɟɬɢ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. ɂɡ ɫɟɬɢ ɭɞɚɥɹɸɬɫɹ ɫɜɹɡɢ, ɢɦɟɸɳɢɟ ɧɭɥɟɜɨɣ ɜɟɫ ɢ ɢɫɤɥɸɱɟɧɧɵɟ ɢɡ ɨɛɭɱɟɧɢɹ. ɇɭɦɟɪɚɰɢɹ ɫɢɝɧɚɥɨɜ ɢ ɩɚɪɚɦɟɬɪɨɜ ɫɨɯɪɚɧɹɟɬɫɹ. ɂɡ ɫɬɪɭɤɬɭɪɵ ɫɟɬɢ ɭɞɚɥɹɸɬɫɹ «ɧɟɦɵɟ» ɭɱɚɫɬɤɢ – ɷɥɟɦɟɧɬɵ ɢ ɛɥɨɤɢ, ɜɵɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɤɨɬɨɪɵɯ ɧɟ ɹɜɥɹɸɬɫɹ ɜɵɯɨɞɧɵɦɢ ɫɢɝɧɚɥɚɦɢ ɫɟɬɢ ɜ ɰɟɥɨɦ ɢ ɧɟ ɢɫɩɨɥɶɡɭɸɬɫɹ ɜ ɤɚɱɟɫɬɜɟ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɞɪɭɝɢɦɢ ɩɨɞɫɟɬɹɦɢ. ɇɭɦɟɪɚɰɢɹ ɫɢɝɧɚɥɨɜ ɢ ɩɚɪɚɦɟɬɪɨɜ ɫɨɯɪɚɧɹɟɬɫɹ.
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5.
6. 7.
ɉɪɨɢɡɜɨɞɢɬɫɹ ɡɚɦɟɧɚ ɷɥɟɦɟɧɬɨɜ, ɫɬɚɜɲɢɯ «ɩɪɨɡɪɚɱɧɵɦɢ» – ɩɭɬɟɦ ɡɚɦɵɤɚɧɢɹ ɜɯɨɞɧɨɝɨ ɫɢɝɧɚɥɚ ɧɚ ɜɵɯɨɞɧɨɣ, ɭɞɚɥɹɸɬɫɹ ɩɪɨɫɬɵɟ ɨɞɧɨɪɨɞɧɵɟ ɫɭɦɦɚɬɨɪɵ ɫ ɨɞɧɢɦ ɜɯɨɞɨɦ ɢ ɬɨɱɤɢ ɜɟɬɜɥɟɧɢɹ ɫ ɨɞɧɢɦ ɜɵɯɨɞɨɦ; ɚɞɚɩɬɢɜɧɵɟ ɨɞɧɨɪɨɞɧɵɟ ɫɭɦɦɚɬɨɪɵ ɫ ɨɞɧɢɦ ɜɯɨɞɨɦ ɡɚɦɟɧɹɸɬɫɹ ɫɢɧɚɩɫɚɦɢ. ɇɭɦɟɪɚɰɢɹ ɫɢɝɧɚɥɨɜ ɢ ɩɚɪɚɦɟɬɪɨɜ ɫɨɯɪɚɧɹɟɬɫɹ. ȼ ɤɚɠɞɨɦ ɛɥɨɤɟ ɩɪɨɢɡɜɨɞɢɬɫɹ ɡɚɦɟɧɚ ɢɦɟɧ ɩɨɞɫɟɬɟɣ ɧɚ ɩɫɟɜɞɨɧɢɦɵ. ɉɪɨɢɡɜɨɞɢɬɫɹ ɢɡɦɟɧɟɧɢɟ ɧɭɦɟɪɚɰɢɢ ɫɢɝɧɚɥɨɜ ɢ ɩɚɪɚɦɟɬɪɨɜ ɫɟɬɢ.
5.4.5 Ɉɫɬɚɥɶɧɵɟ ɡɚɩɪɨɫɵ ɇɢɠɟ ɩɪɢɜɟɞɟɧ ɫɩɢɫɨɤ ɡɚɩɪɨɫɨɜ, ɢɫɩɨɥɧɟɧɢɟ ɤɨɬɨɪɵɯ ɨɩɢɫɚɧɨ ɜ ɝɥɚɜɟ "Ɉɛɳɢɣ ɫɬɚɧɞɚɪɬ": nwSetCurrent – ɋɞɟɥɚɬɶ ɫɟɬɶ ɬɟɤɭɳɟɣ nwAdd – Ⱦɨɛɚɜɥɟɧɢɟ ɫɟɬɢ nwDelete – ɍɞɚɥɟɧɢɟ ɫɟɬɢ nwWrite – Ɂɚɩɢɫɶ ɫɟɬɢ nwGetStructNames – ȼɟɪɧɭɬɶ ɢɦɟɧɚ ɩɨɞɫɟɬɟɣ nwGetType – ȼɟɪɧɭɬɶ ɬɢɩ ɩɨɞɫɟɬɢ nwEdit – Ɋɟɞɚɤɬɢɪɨɜɚɬɶ ɤɨɦɩɨɧɟɧɬɭ ɫɟɬɶ OnError – ɍɫɬɚɧɨɜɢɬɶ ɨɛɪɚɛɨɬɱɢɤ ɨɲɢɛɨɤ GetError – Ⱦɚɬɶ ɧɨɦɟɪ ɨɲɢɛɤɢ FreeMemory – Ɉɫɜɨɛɨɞɢɬɶ ɩɚɦɹɬɶ ȼ ɡɚɩɪɨɫɟ nwGetType ɜ ɩɟɪɟɦɟɧɧɨɣ TypeId ɜɨɡɜɪɚɳɚɟɬɫɹ ɡɧɚɱɟɧɢɟ ɨɞɧɨɣ ɢɡ ɩɪɟɞɨɩɪɟɞɟɥɟɧɧɵɯ ɤɨɧɫɬɚɧɬ, ɩɟɪɟɱɢɫɥɟɧɧɵɯ ɜ ɬɚɛɥ. 3. ɋɥɟɞɭɟɬ ɡɚɦɟɬɢɬɶ, ɱɬɨ ɞɜɚ ɡɚɩɪɨɫɚ nwGetData (ɉɨɥɭɱɢɬɶ ɩɚɪɚɦɟɬɪɵ) ɢ nwSetData (ɍɫɬɚɧɨɜɢɬɶ ɩɚɪɚɦɟɬɪɵ) ɢɦɟɸɬ ɧɚɡɜɚɧɢɟ, ɫɨɜɩɚɞɚɸɳɟɟ ɫ ɧɚɡɜɚɧɢɟɦ ɡɚɩɪɨɫɨɜ, ɨɩɢɫɚɧɧɵɯ ɜ ɝɥɚɜɟ "Ɉɛɳɢɣ ɫɬɚɧɞɚɪɬ", ɧɨ ɨɧɢ ɢɦɟɸɬ ɞɪɭɝɨɣ ɧɚɛɨɪ ɚɪɝɭɦɟɧɬɨɜ.
5.4.5.1 Ɉɲɢɛɤɢ ɤɨɦɩɨɧɟɧɬɚ ɫɟɬɶ ȼ ɬɚɛɥ. 4 ɩɪɢɜɟɞɟɧ ɩɨɥɧɵɣ ɫɩɢɫɨɤ ɨɲɢɛɨɤ, ɤɨɬɨɪɵɟ ɦɨɝɭɬ ɜɨɡɧɢɤɚɬɶ ɩɪɢ ɜɵɩɨɥɧɟɧɢɢ ɡɚɩɪɨɫɨɜ ɤɨɦɩɨɧɟɧɬɨɦ ɫɟɬɶ, ɢ ɞɟɣɫɬɜɢɹ ɫɬɚɧɞɚɪɬɧɨɝɨ ɨɛɪɚɛɨɬɱɢɤɚ ɨɲɢɛɨɤ. Ɍɚɛɥɢɰɚ 4 Ɉɲɢɛɤɢ ɤɨɦɩɨɧɟɧɬɚ ɫɟɬɶ ɢ ɞɟɣɫɬɜɢɹ ɫɬɚɧɞɚɪɬɧɨɝɨ ɨɛɪɚɛɨɬɱɢɤɚ ɨɲɢɛɨɤ. ʋɇɚɡɜɚɧɢɟ ɨɲɢɛɤɢɋɬɚɧɞɚɪɬɧɚɹ ɨɛɪɚɛɨɬɤɚ 301 ɇɟɜɟɪɧɨɟ ɢɦɹ ɫɟɬɢɁɚɧɟɫɟɧɢɟ ɧɨɦɟɪɚ ɜ Error 302 Ɉɲɢɛɤɚ ɫɱɢɬɵɜɚɧɢɹ ɫɟɬɢɁɚɧɟɫɟɧɢɟ ɧɨɦɟɪɚ ɜ Error 303 Ɉɲɢɛɤɚ ɫɨɯɪɚɧɟɧɢɹ ɫɟɬɢɁɚɧɟɫɟɧɢɟ ɧɨɦɟɪɚ ɜ Error 304 Ɉɲɢɛɤɚ ɩɪɹɦɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹɁɚɧɟɫɟɧɢɟ ɧɨɦɟɪɚ ɜ Error 305 Ɉɲɢɛɤɚ ɨɛɪɚɬɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹɁɚɧɟɫɟɧɢɟ ɧɨɦɟɪɚ ɜ Error 306 Ɉɲɢɛɨɱɧɵɣ ɬɢɩ ɩɚɪɚɦɟɬɪɚ ɫɟɬɢɁɚɧɟɫɟɧɢɟ ɧɨɦɟɪɚ ɜ Error
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6. Ɉɰɟɧɤɚ ɢ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɨɬɜɟɬɚ ɗɬɚ ɝɥɚɜɚ ɩɨɫɜɹɳɟɧɚ ɨɛɡɨɪɭ ɪɚɡɥɢɱɧɵɯ ɜɢɞɨɜ ɨɰɟɧɨɤ, ɫɩɨɫɨɛɚɦ ɢɯ ɜɵɱɢɫɥɟɧɢɹ. ȼ ɧɟɣ ɬɚɤ ɠɟ ɪɚɫɫɦɨɬɪɟɧ ɫɩɨɫɨɛ ɨɩɪɟɞɟɥɟɧɢɹ ɭɪɨɜɧɹ ɭɜɟɪɟɧɧɨɫɬɢ ɫɟɬɢ ɜ ɜɵɞɚɧɧɨɦ ɨɬɜɟɬɟ ɢ ɩɪɢɜɟɞɟɧ ɫɩɨɫɨɛ ɩɨɫɬɪɨɟɧɢɹ ɨɰɟɧɨɤ, ɩɨɡɜɨɥɹɸɳɢɯ ɨɩɪɟɞɟɥɹɬɶ ɭɪɨɜɟɧɶ ɭɜɟɪɟɧɧɨɫɬɢ. ɉɪɢɜɟɞɟɧ ɨɫɧɨɜɧɨɣ ɩɪɢɧɰɢɩ ɩɪɨɟɤɬɢɪɨɜɚɧɢɹ ɨɰɟɧɤɢ - ɧɚɞɨ ɭɱɢɬɶ ɫɟɬɶ ɬɨɦɭ, ɱɬɨ ɦɵ ɯɨɬɢɦ ɨɬ ɧɟɟ ɩɨɥɭɱɢɬɶ. ɇɚɩɨɦɧɢɦ ɨɫɧɨɜɧɵɟ ɮɭɧɤɰɢɢ, ɤɨɬɨɪɵɟ ɞɨɥɠɧɚ ɜɵɩɨɥɧɹɬɶ ɨɰɟɧɤɚ: 1. ȼɵɱɢɫɥɹɬɶ ɨɰɟɧɤɭ ɪɟɲɟɧɢɹ, ɜɵɞɚɧɧɨɝɨ ɫɟɬɶɸ. 2. ȼɵɱɢɫɥɹɬɶ ɩɪɨɢɡɜɨɞɧɵɟ ɷɬɨɣ ɨɰɟɧɤɢ ɩɨ ɜɵɯɨɞɧɵɦ ɫɢɝɧɚɥɚɦ ɫɟɬɢ. Ʉɪɨɦɟ ɨɰɟɧɨɤ, ɜ ɩɟɪɜɨɦ ɪɚɡɞɟɥɟ ɷɬɨɣ ɝɥɚɜɵ ɪɚɫɫɦɨɬɪɟɧ ɞɪɭɝɨɣ, ɬɟɫɧɨ ɫɜɹɡɚɧɧɵɣ ɫ ɧɟɣ ɨɛɴɟɤɬ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɨɬɜɟɬɚ. Ɉɫɧɨɜɧɨɟ ɧɚɡɧɚɱɟɧɢɟ ɷɬɨɝɨ ɨɛɴɟɤɬɚ - ɢɧɬɟɪɩɪɟɬɢɪɨɜɚɬɶ ɜɵɯɨɞɧɨɣ ɜɟɤɬɨɪ ɫɟɬɢ ɤɚɤ ɨɬɜɟɬ, ɩɨɧɹɬɧɵɣ ɩɨɥɶɡɨɜɚɬɟɥɸ. Ɉɞɧɚɤɨ, ɩɪɢ ɨɩɪɟɞɟɥɟɧɧɨɦ ɩɨɫɬɪɨɟɧɢɢ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ ɢ ɩɪɚɜɢɥɶɧɨ ɩɨɫɬɪɨɟɧɧɨɣ ɩɨ ɧɟɦɭ ɨɰɟɧɤɟ, ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɨɬɜɟɬɚ ɦɨɠɟɬ ɬɚɤɠɟ ɨɰɟɧɢɜɚɬɶ ɭɪɨɜɟɧɶ ɭɜɟɪɟɧɧɨɫɬɢ ɫɟɬɢ ɜ ɜɵɞɚɧɧɨɦ ɨɬɜɟɬɟ.
6.1 ɂɧɬɟɪɩɪɟɬɚɬɨɪ ɨɬɜɟɬɚ Ʉɚɤ ɛɵɥɨ ɩɨɤɚɡɚɧɨ ɜ ɝɥɚɜɟ «Ɉɩɢɫɚɧɢɟ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ», ɨɬɜɟɬ, ɜɵɞɚɜɚɟɦɵɣ ɧɟɣɪɨɧɧɨɣ ɫɟɬɶɸ, ɤɚɤ ɩɪɚɜɢɥɨ, ɹɜɥɹɟɬɫɹ ɱɢɫɥɨɦ, ɢɡ ɞɢɚɩɚɡɨɧɚ
[ a , b] . ȿɫɥɢ ɨɬɜɟɬ ɜɵɞɚɟɬɫɹ ɧɟɫɤɨɥɶɤɢɦɢ ɧɟɣɪɨɧɚɦɢ, ɬɨ ɧɚ [ a , b] . ȿɫɥɢ ɜ ɤɚɱɟɫɬɜɟ
ɜɵɯɨɞɟ ɫɟɬɢ ɦɵ ɢɦɟɟɦ ɜɟɤɬɨɪ, ɤɚɠɞɵɣ ɤɨɦɩɨɧɟɧɬ ɤɨɬɨɪɨɝɨ ɥɟɠɢɬ ɜ ɢɧɬɟɪɜɚɥɟ
ɨɬɜɟɬɚ ɬɪɟɛɭɟɬɫɹ ɱɢɫɥɨ ɢɡ ɷɬɨɝɨ ɞɢɚɩɚɡɨɧɚ, ɬɨ ɦɵ ɦɨɠɟɦ ɟɝɨ ɩɨɥɭɱɢɬɶ. Ɉɞɧɚɤɨ, ɜ ɛɨɥɶɲɢɧɫɬɜɟ ɫɥɭɱɚɟɜ ɷɬɨ ɧɟ ɬɚɤ. Ⱦɨɫɬɚɬɨɱɧɨ ɱɚɫɬɨ ɬɪɟɛɭɟɦɚɹ ɜ ɤɚɱɟɫɬɜɟ ɨɬɜɟɬɚ ɜɟɥɢɱɢɧɚ ɥɟɠɢɬ ɜ ɞɪɭɝɨɦ ɞɢɚɩɚɡɨɧɟ. ɇɚɩɪɢɦɟɪ, ɩɪɢ ɩɪɟɞɫɤɚɡɚɧɢɢ ɬɟɦɩɟɪɚɬɭɪɵ ɜɨɡɞɭɯɚ 25 ɢɸɧɹ ɜ Ʉɪɚɫɧɨɹɪɫɤɟ ɨɬɜɟɬ ɞɨɥɠɟɧ ɥɟɠɚɬɶ ɜ ɢɧɬɟɪɜɚɥɟ ɨɬ 5 ɞɨ 35 ɝɪɚɞɭɫɨɜ ɐɟɥɶɫɢɹ. ɋɟɬɶ ɧɟ ɦɨɠɟɬ ɞɚɬɶ ɧɚ ɜɵɯɨɞɟ ɬɚɤɨɝɨ ɫɢɝɧɚɥɚ. Ɂɧɚɱɢɬ, ɩɪɟɠɞɟ ɱɟɦ ɨɛɭɱɚɬɶ ɫɟɬɶ ɧɟɨɛɯɨɞɢɦɨ ɪɟɲɢɬɶ ɜ ɤɚɤɨɦ ɜɢɞɟ ɛɭɞɟɦ ɬɪɟɛɨɜɚɬɶ ɨɬɜɟɬ. ȼ ɞɚɧɧɨɦ ɫɥɭɱɚɟ ɨɬɜɟɬ ɦɨɠɧɨ ɬɪɟɛɨɜɚɬɶ ɜ ɜɢɞɟ a = ( b - a)(T - T min ) / (T max - T min ) + a , ɝɞɟ T - ɬɪɟɛɭɟɦɚɹ ɬɟɦɩɟɪɚɬɭɪɚ, Tmin ɢ Tmax ɦɢɧɢɦɚɥɶɧɚɹ ɢ ɦɚɤɫɢɦɚɥɶɧɚɹ ɬɟɦɩɟɪɚɬɭɪɵ, a - ɨɬɜɟɬ, ɤɨɬɨɪɵɣ ɛɭɞɟɦ ɬɪɟɛɨɜɚɬɶ ɨɬ ɫɟɬɢ. ɉɪɢ ɢɧɬɟɪɩɪɟɬɚɰɢɢ ɨɬɜɟɬɚ ɧɟɨɛɯɨɞɢɦɨ ɩɪɨɞɟɥɚɬɶ ɨɛɪɚɬɧɨɟ ɩɪɟɨɛɪɚɡɨɜɚɧɢɟ. ȿɫɥɢ ɫɟɬɶ ɜɵɞɚɥɚ ɫɢɝɧɚɥ a, ɬɨ ɨɬɜɟɬɨɦ ɹɜɥɹɟɬɫɹ ɜɟɥɢɱɢɧɚ T = (a - a)(T max - T min ) / (b - a) + T min . Ɍɚɤɢɦ ɨɛɪɚɡɨɦ, ɦɨɠɧɨ ɢɧɬɟɪɩɪɟɬɢɪɨɜɚɬɶ ɜɵɞɚɜɚɟɦɵɣ ɫɟɬɶɸ ɫɢɝɧɚɥ, ɤɚɤ ɜɟɥɢɱɢɧɭ ɢɡ ɥɸɛɨɝɨ, ɧɚɩɟɪɟɞ ɡɚɞɚɧɧɨɝɨ ɞɢɚɩɚɡɨɧɚ. ȿɫɥɢ ɩɪɢ ɫɨɫɬɚɜɥɟɧɢɢ ɨɛɭɱɚɸɳɟɝɨ ɦɧɨɠɟɫɬɜɚ ɨɬɜɟɬ ɧɚ ɩɪɢɦɟɪɵ ɨɩɪɟɞɟɥɹɥɫɹ ɫ ɧɟɤɨɬɨɪɨɣ ɩɨɝɪɟɲɧɨɫɬɶɸ, ɬɨ ɨɬ ɫɟɬɢ ɫɥɟɞɭɟɬ ɬɪɟɛɨɜɚɬɶ ɧɟ ɬɨɱɧɨɝɨ ɜɨɫɩɪɨɢɡɜɟɞɟɧɢɹ ɨɬɜɟɬɚ, ɚ ɩɨɩɚɞɚɧɢɹ ɜ ɢɧɬɟɪɜɚɥ ɡɚɞɚɧɧɨɣ ɲɢɪɢɧɵ. ȼ ɷɬɨɦ ɫɥɭɱɚɟ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɨɬɜɟɬɚ ɦɨɠɟɬ ɜɵɞɚɬɶ ɫɨɨɛɳɟɧɢɟ ɨ ɩɪɚɜɢɥɶɧɨɫɬɢ (ɩɨɩɚɞɚɧɢɢ ɜ ɢɧɬɟɪɜɚɥ) ɨɬɜɟɬɚ. Ⱦɪɭɝɢɦ, ɱɚɫɬɨ ɜɫɬɪɟɱɚɸɳɢɦɫɹ ɫɥɭɱɚɟɦ, ɹɜɥɹɟɬɫɹ ɩɪɟɞɫɤɚɡɚɧɢɟ ɫɟɬɶɸ ɩɪɢɧɚɞɥɟɠɧɨɫɬɢ ɜɯɨɞɧɨɝɨ ɜɟɤɬɨɪɚ ɨɞɧɨɦɭ ɢɡ ɡɚɞɚɧɧɵɯ ɤɥɚɫɫɨɜ. Ɍɚɤɢɟ ɡɚɞɚɱɢ ɧɚɡɵɜɚɸɬ ɡɚɞɚɱɚɦɢ ɤɥɚɫɫɢɮɢɤɚɰɢɢ, ɚ ɪɟɲɚɸɳɢɟ ɢɯ ɫɟɬɢ - ɤɥɚɫɫɢɮɢɤɚɬɨɪɚɦɢ. ȼ ɩɪɨɫɬɟɣɲɟɦ ɫɥɭɱɚɟ ɡɚɞɚɱɚ ɤɥɚɫɫɢɮɢɤɚɰɢɢ ɫɬɚɜɢɬɫɹ ɫɥɟɞɭɸɳɢɦ ɨɛɪɚɡɨɦ: ɩɭɫɬɶ ɡɚɞɚɧɨ N ɤɥɚɫɫɨɜ. Ɍɨɝɞɚ ɧɟɣɪɨɫɟɬɶ ɜɵɞɚɟɬ ɜɟɤɬɨɪ ɢɡ N ɫɢɝɧɚɥɨɜ. Ɉɞɧɚɤɨ, ɧɟɬ ɟɞɢɧɨɝɨ ɭɧɢɜɟɪɫɚɥɶɧɨɝɨ ɩɪɚɜɢɥɚ ɢɧɬɟɪɩɪɟɬɚɰɢɢ ɷɬɨɝɨ ɜɟɤɬɨɪɚ. ɇɚɢɛɨɥɟɟ ɱɚɫɬɨ ɢɫɩɨɥɶɡɭɟɬɫɹ ɢɧɬɟɪɩɪɟɬɚɰɢɹ ɩɨ ɦɚɤɫɢɦɭɦɭ: ɧɨɦɟɪ ɧɟɣɪɨɧɚ, ɜɵɞɚɜɲɟɝɨ ɦɚɤɫɢɦɚɥɶɧɵɣ ɩɨ ɜɟɥɢɱɢɧɟ ɫɢɝɧɚɥ, ɹɜɥɹɟɬɫɹ ɧɨɦɟɪɨɦ ɤɥɚɫɫɚ, ɤ ɤɨɬɨɪɨɦɭ ɨɬɧɨɫɢɬɫɹ ɩɪɟɞɴɹɜɥɟɧɧɵɣ ɫɟɬɢ ɜɯɨɞɧɨɣ ɜɟɤɬɨɪ. Ɍɚɤɢɟ ɢɧɬɟɪɩɪɟɬɚɬɨɪɵ ɨɬɜɟɬɚ ɧɚɡɵɜɚɸɬɫɹ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚɦɢ, ɤɨɞɢɪɭɸɳɢɦɢ ɨɬɜɟɬ ɧɨɦɟɪɨɦ ɤɚɧɚɥɚ (ɧɨɦɟɪ ɧɟɣɪɨɧɚ - ɧɨɦɟɪ ɤɥɚɫɫɚ). ȼɫɟ ɢɧɬɟɪɩɪɟɬɚɬɨɪɵ, ɢɫɩɨɥɶɡɭɸɳɢɟ ɤɨɞɢɪɨɜɚɧɢɟ ɧɨɦɟɪɨɦ ɤɚɧɚɥɚ, ɢɦɟɸɬ ɨɞɢɧ ɛɨɥɶɲɨɣ ɧɟɞɨɫɬɚɬɨɤ - ɞɥɹ ɤɥɚɫɫɢɮɢɤɚɰɢɢ ɧɚ N ɤɥɚɫɫɨɜ ɬɪɟɛɭɟɬɫɹ N ɜɵɯɨɞɧɵɯ ɧɟɣɪɨɧɨɜ. ɉɪɢ ɛɨɥɶɲɨɦ N ɬɪɟɛɭɟɬɫɹ ɦɧɨɝɨ ɜɵɯɨɞɧɵɯ ɧɟɣɪɨɧɨɜ ɞɥɹ ɩɨɥɭɱɟɧɢɹ ɨɬɜɟɬɚ. Ɉɞɧɚɤɨ ɫɭɳɟɫɬɜɭɸɬ ɢ ɞɪɭɝɢɟ ɜɢɞɵ ɢɧɬɟɪɩɪɟɬɚɬɨɪɨɜ. Ⱦɜɨɢɱɧɵɣ ɢɧɬɟɪɩɪɟɬɚɬɨɪ . Ɉɫɧɨɜɧɚɹ ɢɞɟɹ ɞɜɨɢɱɧɨɝɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ - ɩɨɥɭɱɟɧɢɟ ɧɚ ɜɵɯɨɞɟ ɧɟɣɪɨɧɧɨɣ ɫɟɬɢ ɞɜɨɢɱɧɨɝɨ ɤɨɞɚ ɧɨɦɟɪɚ ɤɥɚɫɫɚ. ɗɬɨ ɞɨɫɬɢɝɚɟɬɫɹ ɞɜɭɯɷɬɚɩɧɨɣ ɢɧɬɟɪɩɪɟɬɚɰɢɟɣ: 1. Ʉɚɠɞɵɣ ɜɵɯɨɞɧɨɣ ɫɢɝɧɚɥ ɧɟɣɪɨɧɧɨɣ ɫɟɬɢ ɢɧɬɟɪɩɪɟɬɢɪɭɟɬɫɹ ɤɚɤ 1, ɟɫɥɢ ɨɧ ɛɨɥɶɲɟ ( a + b ) / 2 , ɢ ɤɚɤ 0 ɜ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ. 2. ɉɨɥɭɱɟɧɧɚɹ ɩɨɫɥɟɞɨɜɚɬɟɥɶɧɨɫɬɶ ɧɭɥɟɣ ɢ ɟɞɢɧɢɰ ɢɧɬɟɪɩɪɟɬɢɪɭɟɬɫɹ ɤɚɤ ɞɜɨɢɱɧɨɟ ɱɢɫɥɨ. Ⱦɜɨɢɱɧɵɣ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɩɨɡɜɨɥɹɟɬ ɢɧɬɟɪɩɪɟɬɢɪɨɜɚɬɶ N ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɧɟɣɪɨɧɧɨɣ ɫɟɬɢ ɤɚɤ ɧɨɦɟɪ ɨɞɧɨɝɨ ɢɡ 2N ɤɥɚɫɫɨɜ. ɉɨɪɹɞɤɨɜɵɣ ɢɧɬɟɪɩɪɟɬɚɬɨɪ . ɉɨɪɹɞɤɨɜɵɣ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɤɨɞɢɪɭɟɬ ɧɨɦɟɪ ɤɥɚɫɫɚ ɩɨɞɫɬɚɧɨɜɤɨɣ. Ɉɬɫɨɪɬɢɪɭɟɦ ɜɟɤɬɨɪ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɩɨ ɜɨɡɪɚɫɬɚɧɢɸ. ȼɟɤɬɨɪ, ɫɨɫɬɚɜɥɟɧɧɵɣ ɢɡ ɧɨɦɟɪɨɜ ɧɟɣɪɨɧɨɜ ɩɨɫɥɟɞɨɜɚɬɟɥɶɧɨ ɪɚɫɩɨɥɨɠɟɧɧɵɯ ɜ ɨɬɫɨɪɬɢɪɨɜɚɧɧɨɦ ɜɟɤɬɨɪɟ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ, ɛɭɞɟɬ ɩɨɞɫɬɚɧɨɜ-
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ɤɨɣ. ȿɫɥɢ ɤɚɠɞɨɣ ɩɨɞɫɬɚɧɨɜɤɟ ɩɪɢɩɢɫɚɬɶ ɧɨɦɟɪ ɤɥɚɫɫɚ, ɬɨ ɬɚɤɨɣ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɦɨɠɟɬ ɡɚɤɨɞɢɪɨɜɚɬɶ N! ɤɥɚɫɫɨɜ ɢɫɩɨɥɶɡɭɹ N ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ.
6.2 ɍɪɨɜɟɧɶ ɭɜɟɪɟɧɧɨɫɬɢ ɑɚɫɬɨ ɩɪɢ ɪɟɲɟɧɢɢ ɡɚɞɚɱ ɤɥɚɫɫɢɮɢɤɚɰɢɢ ɫ ɢɫɩɨɥɶɡɨɜɚɧɢɟɦ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ɧɟɞɨɫɬɚɬɨɱɧɨ ɩɪɨɫɬɨɝɨ ɨɬɜɟɬɚ «ɜɯɨɞɧɨɣ ɜɟɤɬɨɪ ɩɪɢɧɚɞɥɟɠɢɬ K-ɦɭ ɤɥɚɫɫɭ». ɏɨɬɟɥɨɫɶ ɛɵ ɬɚɤɠɟ ɨɰɟɧɢɬɶ ɭɪɨɜɟɧɶ ɭɜɟɪɟɧɧɨɫɬɢ ɜ ɷɬɨɦ ɨɬɜɟɬɟ. Ⱦɥɹ ɪɚɡɥɢɱɧɵɯ ɢɧɬɟɪɩɪɟɬɚɬɨɪɨɜ ɜɨɩɪɨɫ ɨɩɪɟɞɟɥɟɧɢɹ ɭɪɨɜɧɹ ɭɜɟɪɟɧɧɨɫɬɢ ɪɟɲɚɟɬɫɹ ɩɨɪɚɡɧɨɦɭ. Ɉɞɧɚɤɨ, ɧɟɨɛɯɨɞɢɦɨ ɭɱɟɫɬɶ, ɱɬɨ ɨɬ ɧɟɣɪɨɧɧɨɣ ɫɟɬɢ ɧɟɥɶɡɹ ɬɪɟɛɨɜɚɬɶ ɛɨɥɶɲɟ ɬɨɝɨ, ɱɟɦɭ ɟɟ ɨɛɭɱɢɥɢ. ȼ ɷɬɨɦ ɪɚɡɞɟɥɟ ɛɭɞɟɬ ɪɚɫɫɦɨɬɪɟɧ ɜɨɩɪɨɫ ɨɛ ɨɩɪɟɞɟɥɟɧɢɢ ɭɪɨɜɧɹ ɭɜɟɪɟɧɧɨɫɬɢ ɞɥɹ ɧɟɫɤɨɥɶɤɢɯ ɢɧɬɟɪɩɪɟɬɚɬɨɪɨɜ, ɚ ɜ ɫɥɟɞɭɸɳɟɦ ɛɭɞɟɬ ɩɨɤɚɡɚɧɨ, ɤɚɤ ɩɨɫɬɪɨɢɬɶ ɨɰɟɧɤɭ ɬɚɤ, ɱɬɨɛɵ ɧɟɣɪɨɧɧɚɹ ɫɟɬɶ ɩɨɡɜɨɥɹɥɚ ɟɝɨ ɨɩɪɟɞɟɥɢɬɶ. 1. Ʉɨɞɢɪɨɜɚɧɢɟ ɧɨɦɟɪɨɦ ɤɚɧɚɥɚ. Ɂɧɚɤɨɜɵɣ ɢɧɬɟɪɩɪɟɬɚɬɨɪ. Ɂɧɚɤɨɜɵɣ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɪɚɛɨɬɚɟɬ ɜ ɞɜɚ ɷɬɚɩɚ. 1. Ʉɚɠɞɵɣ ɜɵɯɨɞɧɨɣ ɫɢɝɧɚɥ ɧɟɣɪɨɧɧɨɣ ɫɟɬɢ ɢɧɬɟɪɩɪɟɬɢɪɭɟɬɫɹ ɤɚɤ 1, ɟɫɥɢ ɨɧ ɛɨɥɶɲɟ ( a + b ) / 2 , ɢ ɤɚɤ 0 ɜ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ. 2. ȿɫɥɢ ɜ ɩɨɥɭɱɟɧɧɨɦ ɜɟɤɬɨɪɟ ɬɨɥɶɤɨ ɨɞɧɚ ɟɞɢɧɢɰɚ, ɬɨ ɧɨɦɟɪɨɦ ɤɥɚɫɫɚ ɫɱɢɬɚɟɬɫɹ ɧɨɦɟɪ ɧɟɣɪɨɧɚ, ɫɢɝɧɚɥ ɤɨɬɨɪɨɝɨ ɢɧɬɟɪɩɪɟɬɢɪɨɜɚɧ ɤɚɤ 1. ȼ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ ɨɬɜɟɬɨɦ ɫɱɢɬɚɟɬɫɹ ɧɟɨɩɪɟɞɟɥɟɧɧɵɣ ɧɨɦɟɪ ɤɥɚɫɫɚ (ɨɬɜɟɬ «ɧɟ ɡɧɚɸ»). Ⱦɥɹ ɬɨɝɨ ɱɬɨɛɵ ɜɜɟɫɬɢ ɭɪɨɜɟɧɶ ɭɜɟɪɟɧɧɨɫɬɢ ɞɥɹ ɷɬɨɝɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ ɩɨɬɪɟɛɭɟɦ, ɱɬɨɛɵ ɩɪɢ ɨɛɭɱɟɧɢɢ ɫɟɬɢ ɞɥɹ ɜɫɟɯ ɩɪɢɦɟɪɨɜ ɛɵɥɨ ɜɟɪɧɨ ɧɟɪɚɜɟɧɫɬɜɨ: a i -
( a + b) / 2 £ e , ɝɞɟ i = 1,K , N ; a i
- i-ɵɣ ɜɵɯɨɞɧɨɣ ɫɢɝɧɚɥ. e - ɭɪɨɜɟɧɶ ɧɚɞɟɠɧɨɫɬɢ (ɧɚɫɤɨɥɶɤɨ ɫɢɥɶɧɨ ɫɢɝɧɚɥɵ ɞɨɥɠɧɵ ɛɵɬɶ ɨɬɞɟɥɟɧɵ ɨɬ ( a + b ) / 2 ɩɪɢ ɨɛɭɱɟɧɢɢ). ȼ ɷɬɨɦ ɫɥɭɱɚɟ ɭɪɨɜɟɧɶ ɭɜɟɪɟɧɧɨɫɬɢ R ɨɩɪɟɞɟɥɹɟɬɫɹ ɫɥɟɞɭɸɳɢɦ ɨɛɪɚɡɨɦ:
a i - (a + b) / 2 ïü ïì R = miní1: min ý . Ɍɚɤɢɦ ɨɛɪɚɡɨɦ, ɩɪɢ ɨɩɪɟɞɟɥɟɧɧɨɦ ɨɬɜɟɬɟ ɭɪɨɜɟɧɶ ɭɜɟɪɟɧɧɨɫɬɢ e ïî ïþ
ɩɨɤɚɡɵɜɚɟɬ, ɧɚɫɤɨɥɶɤɨ ɨɬɜɟɬ ɞɚɥɟɤ ɨɬ ɧɟɨɩɪɟɞɟɥɟɧɧɨɝɨ, ɚ ɜ ɫɥɭɱɚɟ ɧɟɨɩɪɟɞɟɥɟɧɧɨɝɨ ɨɬɜɟɬɚ - ɧɚɫɤɨɥɶɤɨ ɨɧ ɞɚɥɟɤ ɨɬ ɨɩɪɟɞɟɥɟɧɧɨɝɨ. 2. Ʉɨɞɢɪɨɜɚɧɢɟ ɧɨɦɟɪɨɦ ɤɚɧɚɥɚ. Ɇɚɤɫɢɦɚɥɶɧɵɣ ɢɧɬɟɪɩɪɟɬɚɬɨɪ. Ɇɚɤɫɢɦɚɥɶɧɵɣ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɜ ɤɚɱɟɫɬɜɟ ɧɨɦɟɪɚ ɤɥɚɫɫɚ ɜɵɞɚɟɬ ɧɨɦɟɪ ɧɟɣɪɨɧɚ, ɜɵɞɚɜɲɟɝɨ ɦɚɤɫɢɦɚɥɶɧɵɣ ɫɢɝɧɚɥ. Ⱦɥɹ ɬɚɤɨɝɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ ɜ ɤɚɱɟɫɬɜɟ ɭɪɨɜɧɹ ɭɜɟɪɟɧɧɨɫɬɢ ɟɫɬɟɫɬɜɟɧɧɨ ɢɫɩɨɥɶɡɨɜɚɬɶ ɧɟɤɨɬɨɪɭɸ ɮɭɧɤɰɢɸ ɨɬ ɪɚɡɧɨɫɬɢ ɦɟɠɞɭ ɦɚɤɫɢɦɚɥɶɧɵɦ ɢ ɜɬɨɪɵɦ ɩɨ ɜɟɥɢɱɢɧɟ ɫɢɝɧɚɥɚɦɢ. Ⱦɥɹ ɷɬɨɝɨ ɩɨɬɪɟɛɭɟɦ, ɱɬɨɛɵ ɩɪɢ ɨɛɭɱɟɧɢɢ ɞɥɹ ɜɫɟɯ ɩɪɢɦɟɪɨɜ ɨɛɭɱɚɸɳɟɝɨ ɦɧɨɠɟɫɬɜɚ ɪɚɡɧɨɫɬɶ ɦɟɠɞɭ ɦɚɤɫɢɦɚɥɶɧɵɦ ɢ ɜɬɨɪɵɦ ɩɨ ɜɟɥɢɱɢɧɟ ɫɢɝɧɚɥɚɦɢ ɛɵɥɚ ɧɟ ɦɟɧɶɲɟ ɭɪɨɜɧɹ ɧɚɞɟɠɧɨɫɬɢ e. ȼ ɷɬɨɦ ɫɥɭɱɚɟ ɭɪɨɜɟɧɶ ɭɜɟɪɟɧɧɨɫɬɢ ɜɵɱɢɫɥɹɟɬɫɹ ɩɨ ɫɥɟɞɭɸ-
{
}
ɳɟɣ ɮɨɪɦɭɥɟ: R = max 1:(a i - a j ) e , ɝɞɟ
ɥɵ.
a i - ɦɚɤɫɢɦɚɥɶɧɵɣ, ɚ a j - ɜɬɨɪɨɣ ɩɨ ɜɟɥɢɱɢɧɟ ɫɢɝɧɚ-
3. Ⱦɜɨɢɱɧɵɣ ɢɧɬɟɪɩɪɟɬɚɬɨɪ. ɍɪɨɜɟɧɶ ɧɚɞɟɠɧɨɫɬɢ ɞɥɹ ɞɜɨɢɱɧɨɝɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ ɜɜɨɞɢɬɫɹ ɬɚɤ ɠɟ, ɤɚɤ ɢ ɞɥɹ ɡɧɚɤɨɜɨɝɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ ɩɪɢ ɤɨɞɢɪɨɜɚɧɢɢ ɧɨɦɟɪɨɦ ɤɚɧɚɥɚ. 4. ɉɨɪɹɞɤɨɜɵɣ ɢɧɬɟɪɩɪɟɬɚɬɨɪ. ɉɪɢ ɢɫɩɨɥɶɡɨɜɚɧɢɢ ɩɨɪɹɞɤɨɜɨɝɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ ɜ ɤɚɱɟɫɬɜɟ ɭɪɨɜɧɹ ɭɜɟɪɟɧɧɨɫɬɢ ɟɫɬɟɫɬɜɟɧɧɨ ɛɪɚɬɶ ɮɭɧɤɰɢɸ ɨɬ ɪɚɡɧɨɫɬɢ ɞɜɭɯ ɫɨɫɟɞɧɢɯ ɫɢɝɧɚɥɨɜ ɜ ɭɩɨɪɹɞɨɱɟɧɧɨɦ ɩɨ ɜɨɡɪɚɫɬɚɧɢɸ ɜɟɤɬɨɪɟ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ. Ⱦɥɹ ɷɬɨɝɨ ɩɨɬɪɟɛɭɟɦ, ɱɬɨɛɵ ɩɪɢ ɨɛɭɱɟɧɢɢ ɞɥɹ ɜɫɟɯ ɩɪɢɦɟɪɨɜ ɨɛɭɱɚɸɳɟɝɨ ɦɧɨɠɟɫɬɜɚ ɜ ɭɩɨɪɹɞɨɱɟɧɧɨɦ ɩɨ ɜɨɡɪɚɫɬɚɧɢɸ ɜɟɤɬɨɪɟ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɪɚɡɧɨɫɬɶ ɦɟɠɞɭ ɞɜɭɦɹ ɫɨɫɟɞɧɢɦɢ ɷɥɟɦɟɧɬɚɦɢ ɛɵɥɚ ɧɟ ɦɟɧɶɲɟ ɭɪɨɜɧɹ ɧɚɞɟɠɧɨɫɬɢ e. ȼ ɷɬɨɦ ɫɥɭɱɚɟ ɭɪɨɜɟɧɶ ɭɜɟɪɟɧɧɨ-
{ (
ɫɬɢ ɦɨɠɧɨ ɜɵɱɢɫɥɢɬɶ ɩɨ ɮɨɪɦɭɥɟ R = min 1; a i +1 - a i i
) e } , ɩɪɢɱɟɦ ɜɟɤɬɨɪ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ
ɩɪɟɞɩɨɥɚɝɚɟɬɫɹ ɨɬɫɨɪɬɢɪɨɜɚɧɧɵɦ ɩɨ ɜɨɡɪɚɫɬɚɧɢɸ. ȼ ɡɚɤɥɸɱɟɧɢɟ ɡɚɦɟɬɢɦ, ɱɬɨ ɞɥɹ ɨɬɜɟɬɚ ɬɢɩɚ ɱɢɫɥɨ, ɜɜɟɫɬɢ ɭɪɨɜɟɧɶ ɭɜɟɪɟɧɧɨɫɬɢ ɩɨɞɨɛɧɵɦ ɨɛɪɚɡɨɦ ɧɟɜɨɡɦɨɠɧɨ. ɉɨɠɚɥɭɣ, ɟɞɢɧɫɬɜɟɧɧɵɦ ɫɩɨɫɨɛɨɦ ɨɰɟɧɤɢ ɞɨɫɬɨɜɟɪɧɨɫɬɢ ɪɟɡɭɥɶɬɚɬɚ ɹɜɥɹɟɬɫɹ ɤɨɧɫɢɥɢɭɦ ɧɟɫɤɨɥɶɤɢɯ ɫɟɬɟɣ - ɟɫɥɢ ɧɟɫɤɨɥɶɤɨ ɫɟɬɟɣ ɨɛɭɱɟɧɵ ɪɟɲɟɧɢɸ ɨɞɧɨɣ ɢ ɬɨɣ ɠɟ ɡɚɞɚɱɢ, ɬɨ ɜ ɤɚɱɟɫɬɜɟ ɨɬɜɟɬɚ ɦɨɠɧɨ ɜɵɛɪɚɬɶ ɫɪɟɞɧɟɟ ɡɧɚɱɟɧɢɟ, ɚ ɩɨ ɨɬɤɥɨɧɟɧɢɸ ɨɬɜɟɬɨɜ ɨɬ ɫɪɟɞɧɟɝɨ ɦɨɠɧɨ ɨɰɟɧɢɬɶ ɞɨɫɬɨɜɟɪɧɨɫɬɶ ɪɟɡɭɥɶɬɚɬɚ.
6.3 ɉɨɫɬɪɨɟɧɢɟ ɨɰɟɧɤɢ ɩɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɭ ȿɫɥɢ ɜ ɤɚɱɟɫɬɜɟ ɨɬɜɟɬɚ ɧɟɣɪɨɧɧɚɹ ɫɟɬɶ ɞɨɥɠɧɚ ɜɵɞɚɬɶ ɱɢɫɥɨ, ɬɨ ɟɫɬɟɫɬɜɟɧɧɨɣ ɨɰɟɧɤɨɣ ɹɜɥɹɟɬɫɹ ɤɜɚɞɪɚɬ ɪɚɡɧɨɫɬɢ ɜɵɞɚɧɧɨɝɨ ɫɟɬɶɸ ɜɵɯɨɞɧɨɝɨ ɫɢɝɧɚɥɚ ɢ ɩɪɚɜɢɥɶɧɨɝɨ ɨɬɜɟɬɚ. ȼɫɟ ɨɫɬɚɥɶɧɵɟ ɨɰɟɧɤɢ ɞɥɹ ɨɛɭɱɟɧɢɹ ɫɟɬɟɣ ɪɟɲɟɧɢɸ ɬɚɤɢɯ ɡɚɞɚɱ ɹɜɥɹɸɬɫɹ ɦɨɞɢɮɢɤɚɰɢɹɦɢ ɞɚɧɧɨɣ. ɉɪɢɜɟɞɟɦ ɩɪɢɦɟɪ ɬɚɤɨɣ ɦɨɞɢ-
CHAP6.DOC
113
ɮɢɤɚɰɢɢ. ɉɭɫɬɶ ɩɪɢ ɫɨɫɬɚɜɥɟɧɢɢ ɡɚɞɚɱɧɢɤɚ ɜɟɥɢɱɢɧɚ a , ɹɜɥɹɸɳɚɹɫɹ ɨɬɜɟɬɨɦ, ɢɡɦɟɪɹɥɚɫɶ ɫ ɧɟɤɨɬɨɪɨɣ ɬɨɱɧɨɫɬɶɸ e. Ɍɨɝɞɚ ɧɟɬ ɫɦɵɫɥɚ ɬɪɟɛɨɜɚɬɶ ɨɬ ɫɟɬɢ ɨɛɭɱɢɬɶɫɹ ɜɵɞɚɜɚɬɶ ɜ ɤɚɱɟɫɬɜɟ ɨɬɜɟɬɚ ɢɦɟɧɧɨ ɜɟɥɢɱɢɧɭ
a . Ⱦɨɫɬɚɬɨɱɧɨ, ɟɫɥɢ ɜɵɞɚɧɧɵɣ ɫɟɬɶɸ ɨɬɜɟɬ ɩɨɩɚɞɟɬ ɜ ɢɧɬɟɪɜɚɥ a - e , a + e . Ɉɰɟɧɤɚ, ɭɞɨɜɥɟɬɜɨ-
[
ɪɹɸɳɚɹ ɷɬɨɦɭ ɬɪɟɛɨɜɚɧɢɸ, ɢɦɟɟɬ ɜɢɞ:
]
ì0, ɩɪɢ a - a £ e , ï 2 ï H = í a - a - e , ɩɪɢ a > a + e , ï 2 ïî a - a + e , ɩɪɢ a < a - e . ɗɬɭ ɨɰɟɧɤɭ ɛɭɞɟɦ ɧɚɡɵɜɚɬɶ ɨɰɟɧɤɨɣ ɱɢɫɥɚ ɫ ɞɨɩɭɫɤɨɦ e.
( (
) )
Ⱦɥɹ ɡɚɞɚɱ ɤɥɚɫɫɢɮɢɤɚɰɢɢ ɬɚɤɠɟ ɦɨɠɧɨ ɩɨɥɶɡɨɜɚɬɶɫɹ ɨɰɟɧɤɨɣ ɬɢɩɚ ɫɭɦɦɵ ɤɜɚɞɪɚɬɨɜ ɨɬɤɥɨɧɟɧɢɣ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɫɟɬɢ ɨɬ ɬɪɟɛɭɟɦɵɯ ɨɬɜɟɬɨɜ. Ɉɞɧɚɤɨ, ɷɬɚ ɨɰɟɧɤɚ ɩɥɨɯɚ ɬɟɦ, ɱɬɨ ɜɨ-ɩɟɪɜɵɯ, ɬɪɟɛɨɜɚɧɢɹ ɩɪɢ ɨɛɭɱɟɧɢɢ ɫɟɬɢ ɧɟ ɫɨɜɩɚɞɚɸɬ ɫ ɬɪɟɛɨɜɚɧɢɹɦɢ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ, ɜɨ-ɜɬɨɪɵɯ - ɬɚɤɚɹ ɨɰɟɧɤɚ ɧɟ ɩɨɡɜɨɥɹɟɬ ɨɰɟɧɢɬɶ ɭɪɨɜɟɧɶ ɭɜɟɪɟɧɧɨɫɬɢ ɫɟɬɢ ɜ ɜɵɞɚɧɧɨɦ ɨɬɜɟɬɟ. Ⱦɨɫɬɨɢɧɫɬɜɨɦ ɬɚɤɨɣ ɨɰɟɧɤɢ ɹɜɥɹɟɬɫɹ ɟɟ ɭɧɢɜɟɪɫɚɥɶɧɨɫɬɶ. Ɉɩɵɬ ɪɚɛɨɬɵ ɫ ɧɟɣɪɨɧɧɵɦɢ ɫɟɬɹɦɢ, ɧɚɤɨɩɥɟɧɧɵɣ ɤɪɚɫɧɨɹɪɫɤɨɣ ɝɪɭɩɩɨɣ ɇɟɣɪɨɄɨɦɩ, ɫɜɢɞɟɬɟɥɶɫɬɜɭɟɬ ɨ ɬɨɦ, ɱɬɨ ɩɪɢ ɢɫɩɨɥɶɡɨɜɚɧɢɢ ɨɰɟɧɤɢ, ɩɨɫɬɪɨɟɧɧɨɣ ɩɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɭ, ɜ ɧɟɫɤɨɥɶɤɨ ɪɚɡ ɜɨɡɪɚɫɬɚɟɬ ɫɤɨɪɨɫɬɶ ɨɛɭɱɟɧɢɹ. Ɋɚɫɫɦɨɬɪɢɦ ɩɨɫɬɪɨɟɧɢɟ ɨɰɟɧɨɤ ɩɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɭ ɞɥɹ ɱɟɬɵɪɟɯ ɪɚɫɫɦɨɬɪɟɧɧɵɯ ɜ ɩɪɟɞɵɞɭɳɟɦ ɪɚɡɞɟɥɟ ɢɧɬɟɪɩɪɟɬɚɬɨɪɨɜ ɨɬɜɟɬɚ. 1. Ʉɨɞɢɪɨɜɚɧɢɟ ɧɨɦɟɪɨɦ ɤɚɧɚɥɚ. Ɂɧɚɤɨɜɵɣ ɢɧɬɟɪɩɪɟɬɚɬɨɪ. ɉɭɫɬɶ ɞɥɹ ɪɚɫɫɦɚɬɪɢɜɚɟɦɨɝɨ ɩɪɢɦɟɪɚ ɩɪɚɜɢɥɶɧɵɦ ɨɬɜɟɬɨɦ ɹɜɥɹɟɬɫɹ k-ɵɣ ɤɥɚɫɫ. Ɍɨɝɞɚ ɜɟɤɬɨɪ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɫɟɬɢ ɞɨɥɠɟɧ ɭɞɨɜɥɟɬɜɨɪɹɬɶ ɫɥɟɞɭɸɳɟɣ ɫɢɫɬɟɦɟ ɧɟɪɚɜɟɧɫɬɜ:
ìa i < ( a + b) / 2 - e , i ¹ k í îa k > ( a + b) / 2 + e ,
ɝɞɟ e- ɭɪɨɜɟɧɶ ɧɚɞɟɠɧɨɫɬɢ. Ɉɰɟɧɤɭ, ɜɵɱɢɫɥɹɸɳɭɸ ɪɚɫɫɬɨɹɧɢɟ ɨɬ ɬɨɱɤɢ a ɜ ɩɪɨɫɬɪɚɧɫɬɜɟ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɞɨ ɦɧɨɠɟɫɬɜɚ ɬɨɱɟɤ, ɭɞɨɜɥɟɬɜɨɪɹɸɳɢɯ ɷɬɨɣ ɫɢɫɬɟɦɟ ɧɟɪɚɜɟɧɫɬɜ, ɦɨɠɧɨ ɡɚɩɢɫɚɬɶ ɜ ɜɢɞɟ:
H = å (a i - (a + b) / 2 + e ) + (a k - (a + b) / 2 - e ) . 2
i¹k
ɉɪɨɢɡɜɨɞɧɚɹ ɨɰɟɧɤɢ ɩɨ i-ɦɭ ɜɵɯɨɞɧɨɦɭ ɫɢɝɧɚɥɭ ɪɚɜɧɚ
2
¶H ì 2(a i - (a + b) / 2 + e ), i ¹ k =í . ¶a i î 2(a k - (a + b) / 2 - e ).
2. Ʉɨɞɢɪɨɜɚɧɢɟ ɧɨɦɟɪɨɦ ɤɚɧɚɥɚ. Ɇɚɤɫɢɦɚɥɶɧɵɣ ɢɧɬɟɪɩɪɟɬɚɬɨɪ. ɉɭɫɬɶ ɞɥɹ ɪɚɫɫɦɚɬɪɢɜɚɟɦɨɝɨ ɩɪɢɦɟɪɚ ɩɪɚɜɢɥɶɧɵɦ ɨɬɜɟɬɨɦ ɹɜɥɹɟɬɫɹ k-ɵɣ ɤɥɚɫɫ. Ɍɨɝɞɚ ɜɟɤɬɨɪ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɫɟɬɢ ɞɨɥɠɟɧ ɭɞɨɜɥɟɬɜɨɪɹɬɶ ɫɥɟɞɭɸɳɟɣ ɫɢɫɬɟɦɟ ɧɟɪɚɜɟɧɫɬɜ: a k - e ³ a i , ɩɪɢ i ¹ k . Ɉɰɟɧɤɨɣ ɪɟɲɟɧɢɹ ɫɟɬɶɸ ɞɚɧ-
ɧɨɝɨ ɩɪɢɦɟɪɚ ɹɜɥɹɟɬɫɹ ɪɚɫɫɬɨɹɧɢɟ ɨɬ ɬɨɱɤɢ a ɜ ɩɪɨɫɬɪɚɧɫɬɜɟ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɞɨ ɦɧɨɠɟɫɬɜɚ ɬɨɱɟɤ, ɭɞɨɜɥɟɬɜɨɪɹɸɳɢɯ ɷɬɨɣ ɫɢɫɬɟɦɟ ɧɟɪɚɜɟɧɫɬɜ. Ⱦɥɹ ɡɚɩɢɫɢ ɨɰɟɧɤɢ, ɢɫɤɥɸɱɢɦ ɢɡ ɜɟɤɬɨɪɚ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɫɢɝɧɚɥ ak, ɚ ɨɫɬɚɥɶɧɵɟ ɫɢɝɧɚɥɵ ɨɬɫɨɪɬɢɪɭɟɦ ɩɨ ɭɛɵɜɚɧɢɸ. Ɉɛɨɡɧɚɱɢɦ ɜɟɥɢɱɢɧɭ ak-e ɱɟɪɟɡ b0, ɚ ɜɟɤɬɨɪ ɨɬɫɨɪɬɢɪɨɜɚɧɧɵɯ ɫɢɝɧɚɥɨɜ ɱɟɪɟɡ b 1 ³ b 2 ³K ³ b N -1 . ɋɢɫɬɟɦɚ ɧɟɪɚɜɟɧɫɬɜ ɜ ɷɬɨɦ ɫɥɭɱɚɟ ɩɪɢ-
b 0 ³ b i , ɩɪɢ i>1. Ɇɧɨɠɟɫɬɜɨ ɬɨɱɟɤ ɭɞɨɜɥɟɬɜɨɪɹɸɳɢɯ ɷɬɨɣ ɫɢɫɬɟɦɟ ɧɟɪɚɜɟɧɫɬɜ ɨɛɨɡɧɚb 0 ³ b 1 , ɬɨ ɬɨɱɤɚ b ɩɪɢɧɚɞɥɟɠɢɬ ɦɧɨɠɟɫɬɜɭ D. ȿɫɥɢ b 0 < b 1 , ɬɨ ɧɚɣɞɟɦ ɩɪɨɟɤɰɢɸ ɬɨɱɤɢ b ɧɚ ɝɢɩɟɪɩɥɨɫɤɨɫɬɶ b0=b1. ɗɬɚ ɬɨɱɤɚ ɢɦɟɟɬ ɤɨɨɪɞɢɧɚɬɵ b + b1 æ b + b1 b 0 + b1 ö b1 = ç 0 > b 2 , ɬɨ ɬɨɱɤɚ b 1 ɩɪɢɧɚɞɥɟɠɢɬ ɦɧɨɠɟ, , b 2 , K , b N -1 ÷ . ȿɫɥɢ 0 è ø 2 2 2 ɫɬɜɭ D. ȿɫɥɢ ɧɟɬ, ɬɨ ɬɨɱɤɭ b ɧɭɠɧɨ ɩɪɨɟɤɬɢɪɨɜɚɬɶ ɧɚ ɝɢɩɟɪɩɥɨɫɤɨɫɬɶ b 0 = b 1 = b 2 . ɇɚɣɞɟɦ ɷɬɭ ɬɨɱɨɛɪɟɬɚɟɬ ɜɢɞ
ɱɢɦ ɱɟɪɟɡ D. Ɉɱɟɜɢɞɧɨ, ɱɬɨ ɟɫɥɢ
(
)
ɤɭ. ȿɟ ɤɨɨɪɞɢɧɚɬɵ ɦɨɠɧɨ ɡɚɩɢɫɚɬɶ ɜ ɫɥɟɞɭɸɳɟɦ ɜɢɞɟ b, b, b, b 3 , K , b N -1 . ɗɬɚ ɬɨɱɤɚ ɨɛɥɚɞɚɟɬ ɬɟɦ
ɫɜɨɣɫɬɜɨɦ, ɱɬɨ ɪɚɫɫɬɨɹɧɢɟ ɨɬ ɧɟɟ ɞɨ ɬɨɱɤɢ b ɦɢɧɢɦɚɥɶɧɨ. Ɍɚɤɢɦ ɨɛɪɚɡɨɦ, ɞɥɹ ɧɚɯɨɠɞɟɧɢɹ ɜɟɥɢɱɢɧɵ b ɞɨɫɬɚɬɨɱɧɨ ɜɡɹɬɶ ɩɪɨɢɡɜɨɞɧɭɸ ɨɬ ɪɚɫɫɬɨɹɧɢɹ ɩɨ b ɢ ɩɪɢɪɚɜɧɹɬɶ ɟɟ ɤ ɧɭɥɸ:
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(
)
d (b - b 0 ) 2 + (b - b 1 ) 2 + (b - b 2 ) 2 = 2((b - b 0 ) + (b - b 1 ) + (b - b 2 )) = db = 3b - b 0 - b 1 - b 2 = 0
ɂɡ ɷɬɨɝɨ ɭɪɚɜɧɟɧɢɹ ɧɚɯɨɞɢɦ b ɢ ɡɚɩɢɫɵɜɚɟɦ ɤɨɨɪɞɢɧɚɬɵ ɬɨɱɤɢ b : 2
ö æ b + b1 + b2 b 0 + b1 + b2 b 0 + b1 + b2 , , , b 3 , K , b N -1 ÷ . b2 = ç 0 ø è 3 3 3 ɗɬɚ ɩɪɨɰɟɞɭɪɚ ɩɪɨɞɨɥɠɚɟɬɫɹ ɞɚɥɶɲɟ, ɞɨ ɬɟɯ ɩɨɪ, ɩɨɤɚ ɩɪɢ ɧɟɤɨɬɨɪɨɦ l ɧɟ ɜɵɩɨɥɧɢɬɫɹ ɧɟɪɚɜɟɧ-
åb l
ɫɬɜɨ
ɤɢ
i=0
i
l +1
³ b l +1 ɢɥɢ ɩɨɤɚ l ɧɟ ɨɤɚɠɟɬɫɹ ɪɚɜɧɨɣ N-1. Ɉɰɟɧɤɨɣ ɹɜɥɹɟɬɫɹ ɪɚɫɫɬɨɹɧɢɟ ɨɬ ɬɨɱɤɢ b ɞɨ ɬɨɱ-
l æ l ö bi ç å bi ÷ å l ,K , i = 0 , b l +1 , K , b N + 1 ÷ . b = ç i=0 ç l +1 ÷ l +1 ç ÷ è ø
Ɉɧɚ
ɪɚɜɧɚ
ɫɥɟɞɭɸɳɟɣ
ɜɟɥɢɱɢɧɟ:
æ l ö bi ÷ l çå 0 = i - b j ÷ . ɉɪɨɢɡɜɨɞɧɚɹ ɨɰɟɧɤɢ ɩɨ ɜɵɯɨɞɧɨɦɭ ɫɢɝɧɚɥɭ bm ɪɚɜɧɚ: H = åç ç l +1 ÷ j= 0 ç ÷ è ø 2
ìæ l ö ÷ ïç å bi ¶H ïç i =0 - bm ÷ , m £ l , ÷ íç l + 1 ¶bm ïç ÷ è ø ï m > l. 0, î
Ⱦɥɹ ɩɟɪɟɯɨɞɚ ɤ ɩɪɨɢɡɜɨɞɧɵɦ ɩɨ ɢɫɯɨɞɧɵɦ ɜɵɯɨɞɧɵɦ ɫɢɝɧɚɥɚɦ a i ɧɟɨɛɯɨɞɢɦɨ ɨɛɪɚɬɢɬɶ ɫɞɟɥɚɧɧɵɟ ɧɚ ɩɟɪɜɨɦ ɷɬɚɩɟ ɜɵɱɢɫɥɟɧɢɹ ɨɰɟɧɤɢ ɩɪɟɨɛɪɚɡɨɜɚɧɢɹ. 3. Ⱦɜɨɢɱɧɵɣ ɢɧɬɟɪɩɪɟɬɚɬɨɪ. Ɉɰɟɧɤɚ ɞɥɹ ɞɜɨɢɱɧɨɝɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ ɫɬɪɨɢɬɫɹ ɬɨɱɧɨ ɬɚɤɠɟ ɤɚɤ ɢ ɞɥɹ ɡɧɚɤɨɜɨɝɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ ɩɪɢ ɤɨɞɢɪɨɜɚɧɢɢ ɧɨɦɟɪɨɦ ɤɚɧɚɥɚ. ɉɭɫɬɶ ɩɪɚɜɢɥɶɧɵɦ ɨɬɜɟɬɨɦ ɹɜɥɹɟɬɫɹ k-ɵɣ ɤɥɚɫɫ, ɬɨɝɞɚ ɨɛɨɡɧɚɱɢɦ ɱɟɪɟɡ K ɦɧɨɠɟɫɬɜɨ ɧɨɦɟɪɨɜ ɫɢɝɧɚɥɨɜ, ɤɨɬɨɪɵɦ ɜ ɞɜɨɢɱɧɨɦ ɩɪɟɞɫɬɚɜɥɟɧɢɢ k ɫɨɨɬɜɟɬɫɬɜɭɸɬ ɟɞɢɧɢɰɵ. ɉɪɢ ɭɪɨɜɧɟ ɧɚɞɟɠɧɨɫɬɢ ɨɰɟɧɤɚ ɡɚɞɚɟɬɫɹ ɮɨɪɦɭɥɨɣ: 2 2 H = ai - a+b /2+e + ai - a+b /2-e . i ÏK i ÎK ɉɪɨɢɡɜɨɞɧɚɹ ɨɰɟɧɤɢ ɩɨ i-ɦɭ ɜɵɯɨɞɧɨɦɭ ɫɢɝɧɚɥɭ ɪɚɜɧɚ:
å(
(
)
) å(
(
)
)
¶H ì2(a i - (a + b) / 2 + e ), i Ï K =í . ¶a i î2(a k - (a + b) / 2 - e ), i Î K .
4. ɉɨɪɹɞɤɨɜɵɣ ɢɧɬɟɪɩɪɟɬɚɬɨɪ. Ⱦɥɹ ɩɨɫɬɪɨɟɧɢɹ ɨɰɟɧɤɢ ɩɨ ɩɨɪɹɞɤɨɜɨɦɭ ɢɧɬɟɪɩɪɟɬɚɬɨɪɭ ɧɟɨɛɯɨɞɢɦɨ ɩɪɟɞɜɚɪɢɬɟɥɶɧɨ ɩɟɪɟɫɬɚɜɢɬɶ ɤɨɦɩɨɧɟɧɬɵ ɜɟɤɬɨɪɚ a ɜ ɫɨɨɬɜɟɬɫɬɜɢɢ ɫ ɩɨɞɫɬɚɧɨɜɤɨɣ, ɤɨɞɢɪɭɸ0 ɳɟɣ ɩɪɚɜɢɥɶɧɵɣ ɨɬɜɟɬ. Ɉɛɨɡɧɚɱɢɦ ɩɨɥɭɱɟɧɧɵɣ ɜ ɪɟɡɭɥɶɬɚɬɟ ɜɟɤɬɨɪ ɱɟɪɟɡ b . Ɇɧɨɠɟɫɬɜɨ ɬɨɱɟɤ, ɭɞɨɜɥɟ-
ɬɜɨɪɹɸɳɢɯ ɭɫɥɨɜɢɸ ɡɚɞɚɱɢ, ɨɩɢɫɵɜɚɟɬɫɹ ɫɢɫɬɟɦɨɣ ɭɪɚɜɧɟɧɢɣ
b i0 + e £ b i0+1 , ɝɞɟ e - ɭɪɨɜɟɧɶ ɧɚɞɟɠɧɨ-
ɫɬɢ. Ɉɛɨɡɧɚɱɢɦ ɷɬɨ ɦɧɨɠɟɫɬɜɨ ɱɟɪɟɡ D. Ɉɰɟɧɤɚ ɡɚɞɚɟɬɫɹ ɪɚɫɫɬɨɹɧɢɟɦ ɨɬ ɬɨɱɤɢ b ɞɨ ɩɪɨɟɤɰɢɢ ɷɬɨɣ ɬɨɱɤɢ ɧɚ ɦɧɨɠɟɫɬɜɨ D. Ɉɩɢɲɟɦ ɩɪɨɰɟɞɭɪɭ ɜɵɱɢɫɥɟɧɢɹ ɩɪɨɟɤɰɢɢ. 0 1. ɉɪɨɫɦɨɬɪɟɜ ɤɨɨɪɞɢɧɚɬɵ ɬɨɱɤɢ b , ɨɬɦɟɬɢɦ ɬɟ ɧɨɦɟɪɚ ɤɨɨɪɞɢɧɚɬ, ɞɥɹ ɤɨɬɨɪɵɯ ɧɚɪɭɲɚɟɬɫɹ ɧɟɪɚɜɟɧɫɬɜɨ
b i0 + e £ b i0+1 .
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2.
Ɇɧɨɠɟɫɬɜɨ ɨɬɦɟɱɟɧɧɵɯ ɤɨɨɪɞɢɧɚɬ ɥɢɛɨ ɫɨɫɬɨɢɬ ɢɡ ɨɞɧɨɣ ɩɨɫɥɟɞɨɜɚɬɟɥɶɧɨɫɬɢ ɩɨɫɥɟɞɨɜɚɬɟɥɶɧɵɯ ɧɨɦɟɪɨɜ i , i + 1,K , i + l , ɢɥɢ ɢɡ ɧɟɫɤɨɥɶɤɢɯ ɬɚɤɢɯ ɩɨɫɥɟɞɨɜɚɬɟɥɶɧɨɫɬɟɣ. ɇɚɣɞɟɦ ɬɨɱɤɭ
b 1 , ɤɨɬɨɪɚɹ ɹɜɥɹɥɚɫɶ ɛɵ ɩɪɨɟɤɰɢɟɣ ɬɨɱɤɢ b 0 ɧɚ ɝɢɩɟɪɩɥɨɫɤɨɫɬɶ, ɨɩɪɟɞɟɥɹɟɦɭɸ ɭɪɚɜɧɟɧɢɹɦɢ b 1i + e = b 1i +1 , ɝɞɟ i ɩɪɨɛɟɝɚɟɬ ɦɧɨɠɟɫɬɜɨ ɢɧɞɟɤɫɨɜ ɨɬɦɟɱɟɧɧɵɯ ɤɨɨɪɞɢɧɚɬ. ɉɭɫɬɶ ɦɧɨɠɟɫɬɜɨ ɨɬɦɟɱɟɧɧɵɯ ɤɨɨɪɞɢɧɚɬ ɪɚɫɩɚɞɚɟɬɫɹ ɧɚ n ɩɨɫɥɟɞɨɜɚɬɟɥɶɧɨɫɬɟɣ, ɤɚɠɞɚɹ ɢɡ ɤɨɬɨɪɵɯ ɢɦɟɟɬ ɜɢɞ
(
b 1 = b10 , K , bi10-1 , g 1 , g 1 + e , K , g 1 + l1e , bi10+l1 +1 , K , bi20-1 , g 2 , g 2 + e , K , g 2 + l2 e ,K , bN0
b im ,K , b im + lm , ɝɞɟ m - ɧɨɦɟɪ ɩɨɫɥɟɞɨɜɚɬɟɥɶɧɨɫɬɢ. Ɍɨɝɞɚ ɬɨɱɤɚ b ɢɦɟɟɬ ɜɢɞ: 1.
Ɍɨɱɤɚ
1
b 1 ɹɜɥɹɟɬɫɹ ɩɪɨɟɤɰɢɟɣ, ɢ ɫɥɟɞɨɜɚɬɟɥɶɧɨ, ɪɚɫɫɬɨɹɧɢɟ ɨɬ b 0 ɞɨ b 1 ɞɨɥɠɧɨ ɛɵɬɶ ɦɢɧɢ-
2ù - g m - je ú . Ⱦɥɹ ɧɚɯɨɠɞɟɧɢɹ ɦɢɧɢɦɭɦɚ ɷɬɨɣ û ë ɮɭɧɤɰɢɢ ɧɟɨɛɯɨɞɢɦɨ ɩɪɢɪɚɜɧɹɬɶ ɤ ɧɭɥɸ ɟɟ ɩɪɨɢɡɜɨɞɧɵɟ ɩɨ g m . ɉɨɥɭɱɚɟɦ ɫɢɫɬɟɦɭ ɭɪɚɜɧɟɧɢɣ
é
å êå ( b n
ɦɚɥɶɧɵɦ. ɗɬɨ ɪɚɫɫɬɨɹɧɢɟ ɪɚɜɧɨ
m=1
å (b lm
j=0
2.
)
0 im + j
ȿɫɥɢ ɬɨɱɤɚ
-g
m
lm
j =0
)
0 im + j
)
- je = 0 . Ɋɟɲɚɹ ɟɟ, ɧɚɯɨɞɢɦ g
= å b i0m + j lm
m
j=0
(l m + 1) - l m e
2.
b 1 ɭɞɨɜɥɟɬɜɨɪɹɟɬ ɧɟɪɚɜɟɧɫɬɜɚɦ, ɩɪɢɜɟɞɟɧɧɵɦ ɜ ɩɟɪɜɨɦ ɩɭɧɤɬɟ ɩɪɨɰɟɞɭɪɵ, ɬɨ ɪɚɫ-
ɫɬɨɹɧɢɟ ɨɬ ɧɟɟ ɞɨ ɬɨɱɤɢ
b 0 ɹɜɥɹɟɬɫɹ ɨɰɟɧɤɨɣ. ȼ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ, ɩɨɜɬɨɪɹɟɦ ɩɟɪɜɵɣ ɲɚɝ ɩɪɨ-
ɰɟɞɭɪɵ, ɢɫɩɨɥɶɡɭɹ ɬɨɱɤɭ
b 1 ɜɦɟɫɬɨ b 0 ; Ɉɛɴɟɞɢɧɹɟɦ ɩɨɥɭɱɟɧɧɵɣ ɫɩɢɫɨɤ ɨɬɦɟɱɟɧɧɵɯ ɤɨɦɩɨ-
ɧɟɧɬɨɜ ɫɨ ɫɩɢɫɤɨɦ, ɩɨɥɭɱɟɧɧɵɦ ɩɪɢ ɩɨɢɫɤɟ ɩɪɟɞɵɞɭɳɟɣ ɬɨɱɤɢ; ɧɚɯɨɞɢɦ ɬɨɱɤɭ b , ɩɨɜɬɨɪɹɹ ɜɫɟ ɲɚɝɢ ɩɪɨɰɟɞɭɪɵ, ɧɚɱɢɧɚɹ ɫɨ ɜɬɨɪɨɝɨ. Ɉɬɦɟɬɢɦ, ɱɬɨ ɜ ɯɨɞɟ ɩɪɨɰɟɞɭɪɵ ɱɢɫɥɨ ɨɬɦɟɱɟɧɧɵɯ ɩɨɫɥɟɞɨɜɚɬɟɥɶɧɨɫɬɟɣ ɫɨɫɟɞɧɢɯ ɢɧɞɟɤɫɨɜ ɧɟ ɜɨɡɪɚɫɬɚɟɬ. ɇɟɤɨɬɨɪɵɟ ɩɨɫɥɟɞɨɜɚɬɟɥɶɧɨɫɬɢ ɦɨɝɭɬ ɫɥɢɜɚɬɶɫɹ, ɧɨ ɧɨɜɵɟ ɜɨɡɧɢɤɚɬɶ ɧɟ ɦɨɝɭɬ. ɉɨɫɥɟ ɧɚɯɨɠɞɟɧɢɹ ɩɪɨɟɤɰɢɢ ɦɨɠɧɨ ɡɚɩɢɫɚɬɶ ɨɰɟɧɤɭ: 2
2 lm n é lm æ ö ù H = å êå ç b i0m + j - å b i0m + j ( l m + 1) - ( l m - 2 j )e 2÷ ú . ø ú m =1 ê j = 0 è j=0 û ë Ɉɛɨɡɧɚɱɢɦ ɱɟɪɟɡ I m m-ɭɸ ɩɨɫɥɟɞɨɜɚɬɟɥɶɧɨɫɬɶ ɫɨɫɟɞɧɢɯ ɤɨɨɪɞɢɧɚɬ, ɜɵɞɟɥɟɧɧɭɸ ɩɪɢ ɩɨɫɥɟɞ-
ɧɟɦ ɢɫɩɨɥɧɟɧɢɢ ɩɟɪɜɨɝɨ ɲɚɝɚ ɩɪɨɰɟɞɭɪɵ ɜɵɱɢɫɥɟɧɢɹ ɨɰɟɧɤɢ: ɩɪɨɢɡɜɨɞɧɭɸ ɨɰɟɧɤɢ ɩɨ ɜɵɯɨɞɧɨɦɭ ɫɢɝɧɚɥɭ
I m = {i m , i m + 1, K , i m + lm } . Ɍɨɝɞɚ
b ɦɨɠɧɨ ɡɚɩɢɫɚɬɶ ɜ ɫɥɟɞɭɸɳɟɦ ɜɢɞɟ:
lm ìæ ïç b 0i - å b 0 im + j ¶H ïïçè j =0 =í ¶b i ï n ï0, i Ï UI m . ïî m =1
0 i
(lm + 1) - (lm - 2(i - im ))e
ö 2÷ , $m: i Î I m ; ÷ ø
Ɍɚɤɢɦ ɨɛɪɚɡɨɦ, ɩɨɫɬɪɨɟɧɢɟ ɨɰɟɧɤɢ ɩɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɭ ɫɜɨɞɢɬɫɹ ɤ ɫɥɟɞɭɸɳɟɣ ɩɪɨɰɟɞɭɪɟ. Ɉɩɪɟɞɟɥɹɟɦ ɦɧɨɠɟɫɬɜɨ ɞɨɩɭɫɬɢɦɵɯ ɬɨɱɟɤ, ɬɨ ɟɫɬɶ ɬɚɤɢɯ ɬɨɱɟɤ ɜ ɩɪɨɫɬɪɚɧɫɬɜɟ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ, ɤɨɬɨɪɵɟ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɨɬɜɟɬɚ ɛɭɞɟɬ ɢɧɬɟɪɩɪɟɬɢɪɨɜɚɬɶ ɤɚɤ ɩɪɚɜɢɥɶɧɵɣ ɨɬɜɟɬ ɫɨ ɫɬɨɩɪɨɰɟɧɬɧɵɦ ɭɪɨɜɧɟɦ ɭɜɟɪɟɧɧɨɫɬɢ. 2. ɇɚɯɨɞɢɦ ɩɪɨɟɤɰɢɸ ɜɵɞɚɧɧɨɣ ɫɟɬɶɸ ɬɨɱɤɢ ɧɚ ɷɬɨ ɦɧɨɠɟɫɬɜɨ. ɉɪɨɟɤɰɢɟɣ ɹɜɥɹɟɬɫɹ ɛɥɢɠɚɣɲɚɹ ɬɨɱɤɚ ɢɡ ɦɧɨɠɟɫɬɜɚ. 3. Ɂɚɩɢɫɵɜɚɟɦ ɨɰɟɧɤɭ ɤɚɤ ɪɚɫɫɬɨɹɧɢɟ ɨɬ ɬɨɱɤɢ, ɜɵɞɚɧɧɨɣ ɫɟɬɶɸ, ɞɨ ɟɟ ɩɪɨɟɤɰɢɢ ɧɚ ɦɧɨɠɟɫɬɜɨ ɞɨɩɭɫɬɢɦɵɯ ɬɨɱɟɤ. 1.
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6.4 Ɉɰɟɧɤɚ ɨɛɭɱɚɸɳɟɝɨ ɦɧɨɠɟɫɬɜɚ. ȼɟɫ ɩɪɢɦɟɪɚ ȼ ɩɪɟɞɵɞɭɳɟɦ ɪɚɡɞɟɥɟ ɛɵɥ ɪɚɫɫɦɨɬɪɟɧ ɪɹɞ ɨɰɟɧɨɤ, ɩɨɡɜɨɥɹɸɳɢɯ ɨɰɟɧɢɬɶ ɪɟɲɟɧɢɟ ɫɟɬɶɸ ɤɨɧɤɪɟɬɧɨɝɨ ɩɪɢɦɟɪɚ. Ɉɞɧɚɤɨ, ɫɢɬɭɚɰɢɹ, ɤɨɝɞɚ ɫɟɬɶ ɯɨɬɹɬ ɨɛɭɱɢɬɶ ɪɟɲɟɧɢɸ ɬɨɥɶɤɨ ɨɞɧɨɝɨ ɩɪɢɦɟɪɚ, ɞɨɫɬɚɬɨɱɧɨ ɪɟɞɤɚ. Ɉɛɵɱɧɨ ɫɟɬɶ ɞɨɥɠɧɚ ɧɚɭɱɢɬɶɫɹ ɪɟɲɚɬɶ ɜɫɟ ɩɪɢɦɟɪɵ ɨɛɭɱɚɸɳɟɝɨ ɦɧɨɠɟɫɬɜɚ. Ɋɹɞ ɚɥɝɨɪɢɬɦɨɜ ɨɛɭɱɟɧɢɹ, ɤɨɬɨɪɵɟ ɛɭɞɭɬ ɪɚɫɫɦɚɬɪɢɜɚɬɶɫɹ ɜ ɝɥɚɜɟ "ɭɱɢɬɟɥɶ", ɬɪɟɛɭɸɬ ɜɨɡɦɨɠɧɨɫɬɢ ɨɛɭɱɚɬɶ ɫɟɬɶ ɪɟɲɟɧɢɸ ɜɫɟɯ ɩɪɢɦɟɪɨɜ ɨɞɧɨɜɪɟɦɟɧɧɨ ɢ, ɫɨɨɬɜɟɬɫɬɜɟɧɧɨ, ɨɰɟɧɢɜɚɬɶ ɪɟɲɟɧɢɟ ɫɟɬɶɸ ɜɫɟɯ ɩɪɢɦɟɪɨɜ ɨɛɭɱɚɸɳɟɝɨ ɦɧɨɠɟɫɬɜɚ. Ʉɚɤ ɭɠɟ ɨɬɦɟɱɚɥɨɫɶ, ɨɛɭɱɟɧɢɟ ɧɟɣɪɨɧɧɨɣ ɫɟɬɢ - ɷɬɨ ɩɪɨɰɟɫɫ ɦɢɧɢɦɢɡɚɰɢɢ ɜ ɩɪɨɫɬɪɚɧɫɬɜɟ ɨɛɭɱɚɟɦɵɯ ɩɚɪɚɦɟɬɪɨɜ ɮɭɧɤɰɢɢ ɨɰɟɧɤɢ. Ȼɨɥɶɲɢɧɫɬɜɨ ɚɥɝɨɪɢɬɦɨɜ ɨɛɭɱɟɧɢɹ ɢɫɩɨɥɶɡɭɸɬ ɫɩɨɫɨɛɧɨɫɬɶ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ɛɵɫɬɪɨ ɜɵɱɢɫɥɹɬɶ ɜɟɤɬɨɪ ɝɪɚɞɢɟɧɬɚ ɮɭɧɤɰɢɢ ɨɰɟɧɤɢ ɩɨ ɨɛɭɱɚɟɦɵɦ ɩɚɪɚɦɟɬɪɚɦ. Ɉɛɨɡɧɚɱɢɦ ɨɰɟɧɤɭ ɨɬɞɟɥɶɧɨɝɨ ɩɪɢɦɟɪɚ ɱɟɪɟɡ H i . ɚ ɨɰɟɧɤɭ ɜɫɟɝɨ ɨɛɭɱɚɸɳɟɝɨ ɦɧɨɠɟɫɬɜɚ ɱɟɪɟɡ
H OM . ɉɪɨɫɬɟɣɲɢɣ ɫɩɨɫɨɛ ɩɨɥɭɱɟɧɢɹ ɥɹɟɬɫɹ ɨɱɟɧɶ ɩɪɨɫɬɨ:
H OM ɢɡ H i - ɩɪɨɫɬɚɹ ɫɭɦɦɚ. ɉɪɢ ɷɬɨɦ ɜɟɤɬɨɪ ɝɪɚɞɢɟɧɬɚ ɜɵɱɢɫH OM = å H i ,
( å H ) = å ÑH .
ÑH OM = Ñ
i
.
i
Ɍɚɤɢɦ ɨɛɪɚɡɨɦ, ɢɫɩɨɥɶɡɭɹ ɫɩɨɫɨɛɧɨɫɬɶ ɫɟɬɢ ɜɵɱɢɫɥɹɬɶ ɝɪɚɞɢɟɧɬ ɮɭɧɤɰɢɢ ɨɰɟɧɤɢ ɪɟɲɟɧɢɹ ɨɞɧɨɝɨ ɩɪɢɦɟɪɚ, ɦɨɠɧɨ ɩɨɥɭɱɢɬɶ ɝɪɚɞɢɟɧɬ ɮɭɧɤɰɢɢ ɨɰɟɧɤɢ ɜɫɟɝɨ ɨɛɭɱɚɸɳɟɝɨ ɦɧɨɠɟɫɬɜɚ. Ɉɛɭɱɟɧɢɟ ɩɨ ɜɫɟɦɭ ɨɛɭɱɚɸɳɟɦɭ ɦɧɨɠɟɫɬɜɭ ɩɨɡɜɨɥɹɟɬ ɡɚɞɟɣɫɬɜɨɜɚɬɶ ɞɨɩɨɥɧɢɬɟɥɶɧɵɟ ɦɟɯɚɧɢɡɦɵ ɭɫɤɨɪɟɧɢɹ ɨɛɭɱɟɧɢɹ. Ȼɨɥɶɲɢɧɫɬɜɨ ɷɬɢɯ ɦɟɯɚɧɢɡɦɨɜ ɛɭɞɟɬ ɪɚɫɫɦɨɬɪɟɧɨ ɜ ɝɥɚɜɟ ???. ȼ ɷɬɨɦ ɪɚɡɞɟɥɟ ɛɭɞɟɬ ɪɚɫɫɦɨɬɪɟɧ ɬɨɥɶɤɨ ɨɞɢɧ ɢɡ ɧɢɯ - ɢɫɩɨɥɶɡɨɜɚɧɢɟ ɜɟɫɨɜ ɩɪɢɦɟɪɨɜ. ɂɫɩɨɥɶɡɨɜɚɧɢɟ ɜɟɫɨɜ ɩɪɢɦɟɪɨɜ ɦɨɠɟɬ ɛɵɬɶ ɜɵɡɜɚɧɨ ɨɞɧɨɣ ɢɡ ɫɥɟɞɭɸɳɢɯ ɩɪɢɱɢɧ. 1. Ɉɞɢɧ ɢɡ ɩɪɢɦɟɪɨɜ ɩɥɨɯɨ ɨɛɭɱɚɟɬɫɹ. 2. ɑɢɫɥɨ ɩɪɢɦɟɪɨɜ ɪɚɡɧɵɯ ɤɥɚɫɫɨɜ ɜ ɨɛɭɱɚɸɳɟɦ ɦɧɨɠɟɫɬɜɟ ɫɢɥɶɧɨ ɨɬɥɢɱɚɸɬɫɹ ɞɪɭɝ ɨɬ ɞɪɭɝɚ. 3. ɉɪɢɦɟɪɵ ɜ ɨɛɭɱɚɸɳɟɦ ɦɧɨɠɟɫɬɜɟ ɢɦɟɸɬ ɪɚɡɥɢɱɧɭɸ ɞɨɫɬɨɜɟɪɧɨɫɬɶ. Ɋɚɫɫɦɨɬɪɢɦ ɩɟɪɜɭɸ ɩɪɢɱɢɧɭ - ɩɪɢɦɟɪ ɩɥɨɯɨ ɨɛɭɱɚɟɬɫɹ. ɉɨɞ «ɩɥɨɯɨ ɨɛɭɱɚɟɬɫɹ» ɛɭɞɟɦ ɩɨɧɢɦɚɬɶ ɦɟɞɥɟɧɧɨɟ ɫɧɢɠɟɧɢɟ ɨɰɟɧɤɢ ɞɚɧɧɨɝɨ ɩɪɢɦɟɪɚ ɩɨ ɨɬɧɨɲɟɧɢɸ ɤ ɫɧɢɠɟɧɢɸ ɨɰɟɧɤɢ ɩɨ ɨɛɭɱɚɸɳɟɦɭ ɦɧɨɠɟɫɬɜɭ. Ⱦɥɹ ɬɨɝɨ ɱɬɨɛɵ ɭɫɤɨɪɢɬɶ ɨɛɭɱɟɧɢɟ ɞɚɧɧɨɝɨ ɩɪɢɦɟɪɚ, ɟɦɭ ɦɨɠɧɨ ɩɪɢɩɢɫɚɬɶ ɜɟɫ, ɛɨɥɶɲɢɣ, ɱɟɦ ɭ ɨɫɬɚɥɶɧɵɯ ɩɪɢɦɟɪɨɜ. ɉɪɢ ɷɬɨɦ ɨɰɟɧɤɚ ɩɨ ɨɛɭɱɚɸɳɟɦɭ ɦɧɨɠɟɫɬɜɭ ɢ ɟɟ ɝɪɚɞɢɟɧɬ ɦɨɠɧɨ ɡɚɩɢɫɚɬɶ ɜ ɫɥɟɞɭɸɳɟɦ ɜɢɞɟ:
H OM = å wi H i ; ÑH OM = å wi ÑH i . ɝɞɟ wi - ɜɟɫ i-ɝɨ ɩɪɢɦɟɪɚ. ɗɬɭ ɮɭɧɤɰɢɸ ɨɰɟɧɤɢ
ɛɭɞɟɦ ɧɚɡɵɜɚɬɶ ɨɰɟɧɤɨɣ ɜɡɜɟɲɟɧɧɵɯ ɩɪɢɦɟɪɨɜ. ɉɪɢ ɷɬɨɦ ɝɪɚɞɢɟɧɬ, ɜɵɱɢɫɥɟɧɧɵɣ ɩɨ ɨɰɟɧɤɟ ɪɟɲɟɧɢɹ ɫɟɬɶɸ ɷɬɨɝɨ ɩɪɢɦɟɪɚ, ɜɨɣɞɟɬ ɜ ɫɭɦɦɚɪɧɵɣ ɝɪɚɞɢɟɧɬ ɫ ɛɨɥɶɲɢɦ ɜɟɫɨɦ, ɢ, ɫɥɟɞɨɜɚɬɟɥɶɧɨ, ɫɢɥɶɧɟɟ ɩɨɜɥɢɹɟɬ ɧɚ ɜɵɛɨɪ ɧɚɩɪɚɜɥɟɧɢɹ ɨɛɭɱɟɧɢɹ. ɗɬɨɬ ɫɩɨɫɨɛ ɩɪɢɦɟɧɢɦ ɬɚɤɠɟ ɢ ɞɥɹ ɤɨɪɪɟɤɰɢɢ ɩɪɨɛɥɟɦ, ɫɜɹɡɚɧɧɵɯ ɫɨ ɜɬɨɪɨɣ ɩɪɢɱɢɧɨɣ - ɪɚɡɧɨɟ ɱɢɫɥɨ ɩɪɢɦɟɪɨɜ ɪɚɡɧɵɯ ɤɥɚɫɫɨɜ. Ɉɞɧɚɤɨ ɜ ɷɬɨɦ ɫɥɭɱɚɟ ɭɜɟɥɢɱɢɜɚɸɬɫɹ ɜɟɫɚ ɜɫɟɦ ɩɪɢɦɟɪɚɦ ɬɨɝɨ ɤɥɚɫɫɚ, ɜ ɤɨɬɨɪɨɦ ɦɟɧɶɲɟ ɩɪɢɦɟɪɨɜ. Ɉɩɵɬ ɩɨɤɚɡɵɜɚɟɬ, ɱɬɨ ɢɫɩɨɥɶɡɨɜɚɧɢɟ ɜɟɫɨɜ ɜ ɬɚɤɢɯ ɫɢɬɭɚɰɢɹɯ ɩɨɡɜɨɥɹɟɬ ɭɥɭɱɲɢɬɶ ɨɛɨɛɳɚɸɳɢɟ ɫɩɨɫɨɛɧɨɫɬɢ ɫɟɬɟɣ. ȼ ɫɥɭɱɚɟ ɪɚɡɥɢɱɧɨɣ ɞɨɫɬɨɜɟɪɧɨɫɬɢ ɩɪɢɦɟɪɨɜ ɜ ɨɛɭɱɚɸɳɟɦ ɦɧɨɠɟɫɬɜɟ ɮɭɧɤɰɢɹ ɜɡɜɟɲɟɧɧɵɯ ɩɪɢɦɟɪɨɜ ɧɟ ɩɪɢɦɟɧɢɦɚ. Ⱦɟɣɫɬɜɢɬɟɥɶɧɨ, ɟɫɥɢ ɢɡɜɟɫɬɧɨ, ɱɬɨ ɞɨɫɬɨɜɟɪɧɨɫɬɶ ɨɬɜɟɬɚ ɜ k-ɨɦ ɩɪɢɦɟɪɟ ɜ ɞɜɚ ɪɚɡɚ ɧɢɠɟ, ɱɟɦ ɜ l-ɨɦ, ɯɨɬɟɥɨɫɶ ɛɵ, ɱɬɨɛɵ ɨɛɭɱɟɧɧɚɹ ɫɟɬɶ ɜɵɞɚɜɚɥɚ ɞɥɹ k-ɨɝɨ ɩɪɢɦɟɪɚ ɜ ɞɜɚ ɪɚɡɚ ɦɟɧɶɲɢɣ ɭɪɨɜɟɧɶ ɭɜɟɪɟɧɧɨɫɬɢ. ɗɬɨɝɨ ɦɨɠɧɨ ɞɨɫɬɢɱɶ, ɟɫɥɢ ɩɪɢ ɜɵɱɢɫɥɟɧɢɢ ɨɰɟɧɤɢ k-ɨɝɨ ɩɪɢɦɟɪɚ ɛɭɞɟɬ ɢɫɩɨɥɶɡɨɜɚɬɶɫɹ ɜ ɞɜɚ ɪɚɡɚ ɦɟɧɶɲɢɣ ɭɪɨɜɟɧɶ ɧɚɞɟɠɧɨɫɬɢ. Ɉɰɟɧɤɚ ɨɛɭɱɚɸɳɟɝɨ ɦɧɨɠɟɫɬɜɚ ɜ ɷɬɨɦ ɫɥɭɱɚɟ ɜɵɱɢɫɥɹɟɬɫɹ ɩɨ ɮɨɪɦɭɥɟ ɛɟɡ ɜɟɫɨɜ, ɚ ɞɨɫɬɨɜɟɪɧɨɫɬɶ ɭɱɢɬɵɜɚɟɬɫɹ ɧɟɩɨɫɪɟɞɫɬɜɟɧɧɨ ɩɪɢ ɜɵɱɢɫɥɟɧɢɢ ɨɰɟɧɤɢ ɩɨ ɩɪɢɦɟɪɭ. Ɍɚɤɭɸ ɨɰɟɧɤɭ ɛɭɞɟɦ ɧɚɡɵɜɚɬɶ ɨɰɟɧɤɨɣ ɜɡɜɟɲɟɧɧɨɣ ɞɨɫɬɨɜɟɪɧɨɫɬɢ. Ɍɚɤɢɦ ɨɛɪɚɡɨɦ, ɤɚɠɞɵɣ ɩɪɢɦɟɪ ɦɨɠɟɬ ɢɦɟɬɶ ɞɜɚ ɜɟɫɚ: ɜɟɫ ɩɪɢɦɟɪɚ ɢ ɞɨɫɬɨɜɟɪɧɨɫɬɶ ɩɪɢɦɟɪɚ. Ʉɪɨɦɟ ɬɨɝɨ, ɩɪɢ ɪɟɲɟɧɢɢ ɡɚɞɚɱ ɤɥɚɫɫɢɮɢɤɚɰɢɢ ɤɚɠɞɵɣ ɤɥɚɫɫ ɦɨɠɟɬ ɨɛɥɚɞɚɬɶ ɫɨɛɫɬɜɟɧɧɵɦ ɜɟɫɨɦ. Ɉɤɨɧɱɚɬɟɥɶɧɨ ɮɭɧɤɰɢɸ ɨɰɟɧɤɢ ɩɨ ɨɛɭɱɚɸɳɟɦɭ ɦɧɨɠɟɫɬɜɭ ɢ ɟɟ ɝɪɚɞɢɟɧɬ ɦɨɠɧɨ ɡɚɩɢɫɚɬɶ ɜ ɫɥɟɞɭɸɳɟɦ ɜɢɞɟ:
H OM (e ) = å wi H i (d i e ) ,
ÑH OM (e ) = å wi ÑH i . (d i e ),
ɝɞɟ
wi - ɜɟɫ ɩɪɢɦɟɪɚ, d i - ɟɝɨ ɞɨɫɬɨɜɟɪɧɨɫɬɶ.
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6.5 Ƚɥɨɛɚɥɶɧɵɟ ɢ ɥɨɤɚɥɶɧɵɟ ɨɰɟɧɤɢ ȼ ɩɪɟɞɵɞɭɳɢɯ ɪɚɡɞɟɥɚɯ ɛɵɥ ɪɚɫɫɦɨɬɪɟɧ ɪɹɞ ɨɰɟɧɨɤ. ɗɬɢ ɨɰɟɧɤɢ ɨɛɥɚɞɚɸɬ ɨɞɧɢɦ ɨɛɳɢɦ ɫɜɨɣɫɬɜɨɦ - ɞɥɹ ɜɵɱɢɫɥɟɧɢɹ ɨɰɟɧɤɢ ɩɨ ɩɪɢɦɟɪɭ, ɩɪɟɞɴɹɜɥɟɧɧɨɦɭ ɫɟɬɢ, ɞɨɫɬɚɬɨɱɧɨ ɡɧɚɬɶ ɜɵɯɨɞɧɨɣ ɜɟɤɬɨɪ, ɜɵɞɚɧɧɵɣ ɫɟɬɶɸ ɩɪɢ ɪɟɲɟɧɢɢ ɷɬɨɝɨ ɩɪɢɦɟɪɚ, ɢ ɩɪɚɜɢɥɶɧɵɣ ɨɬɜɟɬ. Ɍɚɤɢɟ ɨɰɟɧɤɢ ɛɭɞɟɦ ɧɚɡɵɜɚɬɶ ɥɨɤɚɥɶɧɵɦɢ. ɉɪɢɜɟɞɟɦ ɬɨɱɧɨɟ ɨɩɪɟɞɟɥɟɧɢɟ. Ɉɩɪɟɞɟɥɟɧɢɟ. Ʌɨɤɚɥɶɧɨɣ ɧɚɡɵɜɚɟɬɫɹ ɥɸɛɚɹ ɨɰɟɧɤɚ, ɹɜɥɹɸɳɚɹɫɹ ɥɢɧɟɣɧɨɣ ɤɨɦɛɢɧɚɰɢɟɣ ɩɪɨɢɡɜɨɥɶɧɵɯ ɧɟɩɪɟɪɵɜɧɨ ɞɢɮɮɟɪɟɧɰɢɪɭɟɦɵɯ ɮɭɧɤɰɢɣ, ɤɚɠɞɚɹ ɢɡ ɤɨɬɨɪɵɯ ɡɚɜɢɫɢɬ ɨɬ ɨɰɟɧɤɢ ɬɨɥɶɤɨ ɨɞɧɨɝɨ ɩɪɢɦɟɪɚ. ɂɫɩɨɥɶɡɨɜɚɧɢɟ ɥɨɤɚɥɶɧɵɯ ɨɰɟɧɨɤ ɩɨɡɜɨɥɹɟɬ ɨɛɭɱɚɬɶ ɫɟɬɶ ɪɟɲɟɧɢɸ ɤɚɤ ɨɬɞɟɥɶɧɨ ɜɡɹɬɨɝɨ ɩɪɢɦɟɪɚ, ɬɚɤ ɢ ɜɫɟɝɨ ɨɛɭɱɚɸɳɟɝɨ ɦɧɨɠɟɫɬɜɚ ɜ ɰɟɥɨɦ. Ɉɞɧɚɤɨ ɫɭɳɟɫɬɜɭɸɬ ɡɚɞɚɱɢ, ɞɥɹ ɤɨɬɨɪɵɯ ɧɟɜɨɡɦɨɠɧɨ ɩɨɫɬɪɨɢɬɶ ɥɨɤɚɥɶɧɭɸ ɨɰɟɧɤɭ. Ȼɨɥɟɟ ɬɨɝɨ, ɞɥɹ ɧɟɤɨɬɨɪɵɯ ɡɚɞɚɱ ɧɟɥɶɡɹ ɩɨɫɬɪɨɢɬɶ ɞɚɠɟ ɨɛɭɱɚɸɳɟɟ ɦɧɨɠɟɫɬɜɨ. ɂɫɩɨɥɶɡɨɜɚɧɢɟ ɧɟɥɨɤɚɥɶɧɵɯ ɨɰɟɧɨɤ ɜɨɡɦɨɠɧɨ ɞɚɠɟ ɩɪɢ ɪɟɲɟɧɢɢ ɡɚɞɚɱ ɤɥɚɫɫɢɮɢɤɚɰɢɢ. ɉɪɢɜɟɞɟɦ ɞɜɚ ɩɪɢɦɟɪɚ ɧɟɥɨɤɚɥɶɧɵɯ ɨɰɟɧɤɢ. Ʉɢɧɟɬɢɱɟɫɤɚɹ ɨɰɟɧɤɚ ɞɥɹ ɡɚɞɚɱɢ ɤɥɚɫɫɢɮɢɤɚɰɢɢ. ɉɭɫɬɶ ɜ ɨɛɭɱɚɸɳɟɟ ɦɧɨɠɟɫɬɜɨ ɜɯɨɞɹɬ ɩɪɢɦɟɪɵ k ɤɥɚɫɫɨɜ. Ɍɪɟɛɭɟɬɫɹ ɨɛɭɱɢɬɶ ɫɟɬɶ ɬɚɤ, ɱɬɨɛɵ ɜ ɩɪɨɫɬɪɚɧɫɬɜɟ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɦɧɨɠɟɫɬɜɚ ɩɪɢɦɟɪɨɜ ɪɚɡɧɵɯ ɤɥɚɫɫɨɜ ɛɵɥɢ ɩɨɩɚɪɧɨ ɥɢɧɟɣɧɨ ɪɚɡɞɟɥɢɦɵ. ɉɭɫɬɶ ɫɟɬɶ ɜɵɞɚɟɬ N ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ. Ⱦɥɹ ɪɟɲɟɧɢɹ ɡɚɞɚɱɢ ɞɨɫɬɚɬɨɱɧɨ, ɱɬɨɛɵ ɜ ɯɨɞɟ ɨɛɭɱɟɧɢɹ ɜɫɟ ɬɨɱɤɢ ɜ ɩɪɨɫɬɪɚɧɫɬɜɟ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ, ɫɨɨɬɜɟɬɫɬɜɭɸɳɢɟ ɩɪɢɦɟɪɚɦ ɨɞɧɨɝɨ ɤɥɚɫɫɚ, ɫɨɛɢɪɚɥɢɫɶ ɜɨɤɪɭɝ ɨɞɧɨɣ ɬɨɱɤɢ - ɰɟɧɬɪɚ ɤɨɧɰɟɧɬɪɚɰɢɢ ɤɥɚɫɫɚ, ɢ ɱɬɨɛɵ ɰɟɧɬɪɵ ɤɨɧɰɟɧɬɪɚɰɢɢ ɪɚɡɧɵɯ ɤɥɚɫɫɨɜ ɛɵɥɢ ɤɚɤ ɦɨɠɧɨ ɞɚɥɶɲɟ ɞɪɭɝ ɨɬ ɞɪɭɝɚ. ȼ ɤɚɱɟɫɬɜɟ ɰɟɧɬɪɚ ɤɨɧɰɟɧɬɪɚɰɢɢ ɦɨɠɧɨ ɜɵɛɪɚɬɶ ɛɚɪɢɰɟɧɬɪ ɦɧɨɠɟɫɬɜɚ ɬɨɱɟɤ, ɫɨɨɬɜɟɬɫɬɜɭɸɳɢɯ ɩɪɢɦɟɪɚɦ ɞɚɧɧɨɝɨ ɤɥɚɫɫɚ. Ɍɚɤɢɦ ɨɛɪɚɡɨɦ, ɮɭɧɤɰɢɹ ɨɰɟɧɤɢ ɞɨɥɠɧɚ ɫɨɫɬɨɹɬɶ ɢɡ ɞɜɭɯ ɤɨɦɩɨɧɟɧɬɨɜ: ɩɟɪɜɚɹ ɪɟɚɥɢɡɭɟɬ ɩɪɢɬɹɠɟɧɢɟ ɦɟɠɞɭ ɩɪɢɦɟɪɚɦɢ ɨɞɧɨɝɨ ɤɥɚɫɫɚ ɢ ɛɚɪɢɰɟɧɬɪɨɦ ɷɬɨɝɨ ɤɥɚɫɫɚ, ɚ ɜɬɨɪɚɹ ɨɬɜɟɱɚɟɬ ɡɚ ɨɬɬɚɥɤɢɜɚɧɢɟ ɛɚɪɢɰɟɧɬɪɨɜ ɪɚɡɧɵɯ ɤɥɚɫɫɨɜ. Ɉɛɨɡɧɚɱɢɦ ɬɨɱɤɭ ɜ ɩɪɨɫɬɪɚɧɫɬɜɟ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ, ɫɨɨɬɜɟɬɫɬɜɭɸɳɭɸ
m-ɦɭ ɩɪɢɦɟɪɭ, ɱɟɪɟɡ a m , ɦɧɨɠɟɫɬɜɨ ɩɪɢɦɟɪɨɜ i-ɝɨ ɤɥɚɫɫɚ ɱɟɪɟɡ I i , ɛɚɪɢɰɟɧɬɪ ɬɨɱɟɤ, ɫɨɨɬɜɟɬɫɬɜɭɸɳɢɯ 1 i i ɩɪɢɦɟɪɚɦ ɷɬɨɝɨ ɤɥɚɫɫɚ, ɱɟɪɟɡ B ( B = å a m ), ɱɢɫɥɨ ɩɪɢɦɟɪɨɜ ɜ i-ɨɦ ɤɥɚɫɫɟ ɱɟɪɟɡ B i , ɚ ɪɚɫI i mÎIi ɫɬɨɹɧɢɟ ɦɟɠɞɭ ɬɨɱɤɚɦɢ
(
a ɢ b ɱɟɪɟɡ dist( a , b) = å a j - b j j
(
)
2
. ɂɫɩɨɥɶɡɭɹ ɷɬɢ ɨɛɨɡɧɚɱɟɧɢɹ, ɦɨɠɧɨ
)
ɡɚɩɢɫɚɬɶ ɩɪɢɬɹɝɢɜɚɸɳɢɣ ɤɨɦɩɨɧɟɧɬ ɮɭɧɤɰɢɢ ɨɰɟɧɤɢ ɞɥɹ ɜɫɟɯ ɩɪɢɦɟɪɨɜ i-ɝɨ ɤɥɚɫɫɚ ɜ ɜɢɞɟ:
H iP = å dist a j , B i j ÎI i
P i
Ɏɭɧɤɰɢɹ ɨɰɟɧɤɢ H ɨɛɟɫɩɟɱɢɜɚɟɬ ɫɢɥɶɧɨɟ ɩɪɢɬɹɠɟɧɢɟ ɞɥɹ ɩɪɢɦɟɪɨɜ, ɧɚɯɨɞɹɳɢɯɫɹ ɞɚɥɟɤɨ ɨɬ ɛɚɪɢɰɟɧɬɪɚ. ɉɪɢɬɹɠɟɧɢɟ ɨɫɥɚɛɟɜɚɟɬ ɫ ɩɪɢɛɥɢɠɟɧɢɟɦ ɤ ɛɚɪɢɰɟɧɬɪɭ. Ʉɨɦɩɨɧɟɧɬ ɮɭɧɤɰɢɢ ɨɰɟɧɤɢ, ɨɬɜɟɱɚɸɳɢɣ ɡɚ ɨɬɬɚɥɤɢɜɚɧɢɟ ɛɚɪɢɰɟɧɬɪɨɜ ɪɚɡɧɵɯ ɤɥɚɫɫɨɜ, ɞɨɥɠɟɧ ɨɛɟɫɩɟɱɢɜɚɬɶ ɫɢɥɶɧɨɟ ɨɬɬɚɥɤɢɜɚɧɢɟ ɛɥɢɡɤɢɯ ɛɚɪɢɰɟɧɬɪɨɜ ɢ ɨɫɥɚɛɟɜɚɬɶ ɫ ɭɞɚɥɟɧɢɟɦ ɛɚɪɢɰɟɧɬɪɨɜ ɞɪɭɝ ɨɬ ɞɪɭɝɚ. Ɍɚɤɢɦɢ ɫɜɨɣɫɬɜɚɦɢ ɨɛɥɚɞɚɟɬ ɝɪɚɜɢɬɚɰɢɨɧɧɨɟ ɨɬɬɚɥɤɢɜɚɧɢɟ. ɂɫɩɨɥɶɡɭɹ ɝɪɚɜɢɬɚɰɢɨɧɧɨɟ ɨɬɬɚɥɤɢɜɚɧɢɟ ɦɨɠɧɨ ɡɚɩɢɫɚɬɶ ɜɬɨɪɨɣ ɤɨɦɩɨɧɟɧɬ ɮɭɧɤɰɢɢ ɨɰɟɧɤɢ ɜ ɜɢɞɟ:
(
H O = å dist B i , B j i< j
)
-1
. Ɍɚɤɢɦ ɨɛɪɚɡɨɦ, ɨɰɟɧɤɭ, ɨɛɟɫɩɟɱɢɜɚɸɳɭɸ ɫɛɥɢɠɟ-
ɧɢɟ ɬɨɱɟɤ, ɫɨɨɬɜɟɬɫɬɜɭɸɳɢɯ ɩɪɢɦɟɪɚɦ ɨɞɧɨɝɨ ɤɥɚɫɫɚ, ɢ ɨɬɬɚɥɤɢɜɚɧɢɟ ɛɚɪɢɰɟɧɬɪɨɜ, ɦɨɠɧɨ ɡɚɩɢɫɚɬɶ ɜ ɜɢɞɟ:
(
)
(
)
H = å H iP + H O = å å dist a j , B i + å dist B i , B j . i
i
j ÎI i
i< j
ȼɵɱɢɫɥɢɦ ɩɪɨɢɡɜɨɞɧɭɸ ɨɰɟɧɤɢ ɩɨ j-ɦɭ ɜɵɯɨɞɧɨɦɭ ɫɢɝɧɚɥɭ, ɩɨɥɭɱɟɧɧɨɦɭ ɩɪɢ ɪɟɲɟɧɢɢ i-ɝɨ ɩɪɢɦɟɪɚ. ɉɭɫɬɶ i-ɵɣ ɩɪɢɦɟɪ ɩɪɢɧɚɞɥɟɠɢɬ l-ɦɭ ɤɥɚɫɫɭ. Ɍɨɝɞɚ ɩɪɨɢɡɜɨɞɧɚɹ ɢɦɟɟɬ ɜɢɞ:
(
)
2 dH = 2 a ij - B lj da ij Il
B jl - B kj
å dist k ¹l
2
(B , B ) l
k
.
ɗɬɭ ɨɰɟɧɤɭ ɛɭɞɟɦ ɧɚɡɵɜɚɬɶ ɤɢɧɟɬɢɱɟɫɤɨɣ. ɋɭɳɟɫɬɜɭɟɬ ɨɞɧɨ ɨɫɧɨɜɧɨɟ ɨɬɥɢɱɢɟ ɷɬɨɣ ɨɰɟɧɤɢ ɨɬ ɜɫɟɯ ɞɪɭɝɢɯ, ɪɚɧɟɟ ɪɚɫɫɦɨɬɪɟɧɧɵɯ, ɨɰɟɧɨɤ ɞɥɹ ɪɟɲɟɧɢɹ ɡɚɞɚɱ ɤɥɚɫɫɢɮɢɤɚɰɢɢ. ɉɪɢ ɢɫɩɨɥɶɡɨɜɚɧɢɢ ɬɪɚɞɢɰɢɨɧɧɵɯ ɩɨɞɯɨɞɨɜ, ɫɧɚɱɚɥɚ ɜɵɛɢɪɚɸɬ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɨɬɜɟɬɚ, ɡɚɬɟɦ ɫɬɪɨɹɬ ɩɨ ɜɵɛɪɚɧɧɨɦɭ ɢɧɬɟɪɩɪɟɬɚɬɨɪɭ ɮɭɧɤɰɢɸ ɨɰɟɧɤɢ, ɢ ɬɨɥɶɤɨ ɡɚɬɟɦ ɩɪɢɫɬɭɩɚɸɬ ɤ ɨɛɭɱɟɧɢɸ ɫɟɬɢ. Ⱦɥɹ ɤɢɧɟɬɢɱɟɫɤɨɣ ɨɰɟɧɤɢ ɬɚɤɨɣ
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ɩɨɞɯɨɞ ɧɟ ɩɪɢɦɟɧɢɦ. Ⱦɟɣɫɬɜɢɬɟɥɶɧɨ, ɞɨ ɬɨɝɨ ɤɚɤ ɛɭɞɟɬ ɡɚɤɨɧɱɟɧɨ ɨɛɭɱɟɧɢɟ ɫɟɬɢ ɧɟɜɨɡɦɨɠɧɨ ɩɨɫɬɪɨɢɬɶ ɢɧɬɟɪɩɪɟɬɚɬɨɪ. Ʉɪɨɦɟ ɬɨɝɨ, ɢɫɩɨɥɶɡɨɜɚɧɢɟ ɤɢɧɟɬɢɱɟɫɤɨɣ ɨɰɟɧɤɢ, ɞɟɥɚɟɬ ɧɟɨɛɯɨɞɢɦɵɦ ɨɛɭɱɟɧɢɟ ɫɟɬɢ ɪɟɲɟɧɢɸ ɜɫɟɯ ɩɪɢɦɟɪɨɜ ɨɛɭɱɚɸɳɟɝɨ ɦɧɨɠɟɫɬɜɚ ɨɞɧɨɜɪɟɦɟɧɧɨ. ɗɬɨ ɫɜɹɡɚɧɧɨ ɫ ɧɟɜɨɡɦɨɠɧɨɫɬɶɸ ɜɵɱɢɫɥɢɬɶ ɨɰɟɧɤɭ ɨɞɧɨɝɨ ɩɪɢɦɟɪɚ. Ʉɢɧɟɬɢɱɟɫɤɚɹ ɨɰɟɧɤɚ, ɨɱɟɜɢɞɧɨ, ɧɟ ɹɜɥɹɟɬɫɹ ɥɨɤɚɥɶɧɨɣ: ɞɥɹ ɜɵɱɢɫɥɟɧɢɹ ɩɪɨɢɡɜɨɞɧɵɯ ɨɰɟɧɤɢ ɩɨ ɜɵɯɨɞɧɵɦ ɫɢɝɧɚɥɚɦ ɩɪɢɦɟɪɚ ɧɟɨɛɯɨɞɢɦɨ ɡɧɚɬɶ ɛɚɪɢɰɟɧɬɪɵ ɜɫɟɯ ɤɥɚɫɫɨɜ, ɞɥɹ ɜɵɱɢɫɥɟɧɢɹ ɤɨɬɨɪɵɯ, ɜ ɫɜɨɸ ɨɱɟɪɟɞɶ, ɧɟɨɛɯɨɞɢɦɨ ɡɧɚɬɶ ɜɵɯɨɞɧɵɟ ɫɢɝɧɚɥɵ, ɩɨɥɭɱɚɟɦɵɟ ɩɪɢ ɪɟɲɟɧɢɢ ɜɫɟɯ ɩɪɢɦɟɪɨɜ ɨɛɭɱɚɸɳɟɝɨ ɦɧɨɠɟɫɬɜɚ. ɂɧɬɟɪɩɪɟɬɚɬɨɪ ɞɥɹ ɤɢɧɟɬɢɱɟɫɤɨɣ ɨɰɟɧɤɢ ɫɬɪɨɢɬɫɹ ɫɥɟɞɭɸɳɢɦ ɨɛɪɚɡɨɦ. Ⱦɥɹ ɩɨɫɬɪɨɟɧɢɹ ɪɚɡɞɟ-
(
)
ɥɢɬɟɥɹ i-ɝɨ ɢ j-ɝɨ ɤɥɚɫɫɨɜ ɫɬɪɨɢɦ ɩɥɨɫɤɨɫɬɶ, ɩɟɪɩɟɧɞɢɤɭɥɹɪɧɭɸ ɤ ɜɟɤɬɨɪɭ B - B . ɍɪɚɜɧɟɧɢɟ ɷɬɨɣ ɩɥɨɫɤɨɫɬɢ ɦɨɠɧɨ ɡɚɩɢɫɚɬɶ ɜ ɜɢɞɟ
(
)
i j å Bh - B p a p N
h =1 N
(
i j å Bh - B p
h =1
)
2
i
j
+ D = 0.
Ⱦɥɹ ɨɩɪɟɞɟɥɟɧɢɹ ɤɨɧɫɬɚɧɬɵ D ɧɚɯɨɞɢɦ ɫɪɟɞɢ ɬɨɱɟɤ i-ɝɨ ɤɥɚɫɫɚ ɛɥɢɠɚɣɲɭɸ ɤ ɛɚɪɢɰɟɧɬɪɭ j-ɝɨ ɤɥɚɫɫɚ. ɉɨɞɫɬɚɜɥɹɹ ɤɨɨɪɞɢɧɚɬɵ ɷɬɨɣ ɬɨɱɤɢ ɜ ɭɪɚɜɧɟɧɢɟ ɝɢɩɟɪɩɥɨɫɤɨɫɬɢ, ɩɨɥɭɱɚɟɦ ɭɪɚɜɧɟɧɢɟ ɧɚ D. Ɋɟɲɢɜ ɷɬɨ ɭɪɚɜɧɟɧɢɟ, ɧɚɯɨɞɢɦ ɜɟɥɢɱɢɧɭ D1 . ɂɫɩɨɥɶɡɭɹ ɛɥɢɠɚɣɲɭɸ ɤ ɛɚɪɢɰɟɧɬɪɭ i-ɝɨ ɤɥɚɫɫɚ ɬɨɱɤɭ j-ɝɨ ɤɥɚɫɫɚ, ɧɚɯɨɞɢɦ ɜɟɥɢɱɢɧɭ
D2 . ɂɫɤɨɦɚɹ ɤɨɧɫɬɚɧɬɚ D ɧɚɯɨɞɢɬɫɹ ɤɚɤ ɫɪɟɞɧɟɟ ɚɪɢɮɦɟɬɢɱɟɫɤɨɟ ɦɟɠɞɭ D1
ɢ D 2 . Ⱦɥɹ ɨɬɧɟɫɟɧɢɹ ɩɪɨɢɡɜɨɥɶɧɨɝɨ ɜɟɤɬɨɪɚ ɤ i-ɦɭ ɢɥɢ j-ɦɭ ɤɥɚɫɫɭ ɞɨɫɬɚɬɨɱɧɨ ɩɨɞɫɬɚɜɢɬɶ ɟɝɨ ɡɧɚɱɟɧɢɹ ɜ ɥɟɜɭɸ ɱɚɫɬɶ ɭɪɚɜɧɟɧɢɹ ɪɚɡɞɟɥɹɸɳɟɣ ɝɢɩɟɪɩɥɨɫɤɨɫɬɢ. ȿɫɥɢ ɡɧɚɱɟɧɢɟ ɥɟɜɨɣ ɱɚɫɬɢ ɭɪɚɜɧɟɧɢɹ ɩɨɥɭɱɚɟɬɫɹ ɛɨɥɶɲɟ ɧɭɥɹ, ɬɨ ɜɟɤɬɨɪ ɨɬɧɨɫɢɬɫɹ ɤ j-ɦɭ ɤɥɚɫɫɭ, ɜ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ - ɤ i-ɦɭ. ɂɧɬɟɪɩɪɟɬɚɬɨɪ ɪɚɛɨɬɚɟɬ ɫɥɟɞɭɸɳɢɦ ɨɛɪɚɡɨɦ: ɟɫɥɢ ɞɥɹ i-ɝɨ ɤɥɚɫɫɚ ɜɫɟ ɪɚɡɞɟɥɢɬɟɥɢ ɷɬɨɝɨ ɤɥɚɫɫɚ ɫ ɨɫɬɚɥɶɧɵɦɢ ɤɥɚɫɫɚɦɢ ɜɵɞɚɥɢ ɨɬɜɟɬ i-ɵɣ ɤɥɚɫɫ, ɬɨ ɨɤɨɧɱɚɬɟɥɶɧɵɦ ɨɬɜɟɬɨɦ ɹɜɥɹɟɬɫɹ i-ɵɣ ɤɥɚɫɫ. ȿɫɥɢ ɬɚɤɨɝɨ ɤɥɚɫɫɚ ɧɟ ɧɚɲɥɨɫɶ, ɬɨ ɨɬɜɟɬ «ɧɟ ɡɧɚɸ». ɋɢɬɭɚɰɢɹ, ɤɨɝɞɚ ɞɥɹ ɞɜɭɯ ɪɚɡɥɢɱɧɵɯ ɤɥɚɫɫɨɜ ɜɫɟ ɪɚɡɞɟɥɢɬɟɥɢ ɩɨɞɬɜɟɪɞɢɥɢ ɩɪɢɧɚɞɥɟɠɧɨɫɬɶ ɤ ɷɬɨɦɭ ɤɥɚɫɫɭ, ɧɟɜɨɡɦɨɠɧɚ, ɬɚɤ ɤɚɤ ɪɚɡɞɟɥɢɬɟɥɶ ɷɬɢɯ ɞɜɭɯ ɤɥɚɫɫɨɜ ɞɨɥɠɟɧ ɛɵɥ ɨɬɞɚɬɶ ɩɪɟɞɩɨɱɬɟɧɢɟ ɨɞɧɨɦɭ ɢɡ ɧɢɯ. Ɋɚɫɫɦɨɬɪɟɧɧɵɣ ɩɪɢɦɟɪ ɪɟɲɟɧɢɹ ɡɚɞɚɱɢ ɫ ɢɫɩɨɥɶɡɨɜɚɧɢɟɦ ɧɟɥɨɤɚɥɶɧɨɣ ɨɰɟɧɤɢ ɩɨɡɜɨɥɹɟɬ ɜɵɞɟɥɢɬɶ ɨɫɧɨɜɧɵɟ ɱɟɪɬɵ ɨɛɭɱɟɧɢɹ ɫ ɧɟɥɨɤɚɥɶɧɨɣ ɨɰɟɧɤɨɣ: 1. ɇɟɜɨɡɦɨɠɧɨɫɬɶ ɨɰɟɧɢɬɶ ɪɟɲɟɧɢɟ ɨɞɧɨɝɨ ɩɪɢɦɟɪɚ. 2. ɇɟɜɨɡɦɨɠɧɨɫɬɶ ɨɰɟɧɢɬɶ ɩɪɚɜɢɥɶɧɨɫɬɶ ɪɟɲɟɧɢɹ ɩɪɢɦɟɪɚ ɞɨ ɨɤɨɧɱɚɧɢɹ ɨɛɭɱɟɧɢɹ. 3. ɇɟɜɨɡɦɨɠɧɨɫɬɶ ɩɨɫɬɪɨɟɧɢɹ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ ɨɬɜɟɬɚ ɞɨ ɨɤɨɧɱɚɧɢɹ ɨɛɭɱɟɧɢɹ. ɗɬɨɬ ɩɪɢɦɟɪ ɹɜɥɹɟɬɫɹ ɨɬɱɚɫɬɢ ɧɚɞɭɦɚɧɧɵɦ, ɩɨɫɤɨɥɶɤɭ ɟɝɨ ɦɨɠɧɨ ɪɟɲɢɬɶ ɫ ɢɫɩɨɥɶɡɨɜɚɧɢɟɦ ɛɨɥɟɟ ɩɪɨɫɬɵɯ ɥɨɤɚɥɶɧɵɯ ɨɰɟɧɨɤ. ɇɢɠɟ ɩɪɢɜɟɞɟɧ ɩɪɢɦɟɪ ɡɚɞɚɱɢ, ɤɨɬɨɪɭɸ ɧɟɜɨɡɦɨɠɧɨ ɪɟɲɢɬɶ ɫ ɢɫɩɨɥɶɡɨɜɚɧɢɟɦ ɥɨɤɚɥɶɧɵɯ ɨɰɟɧɨɤ. Ƚɟɧɟɪɚɬɨɪ ɫɥɭɱɚɣɧɵɯ ɱɢɫɟɥ. ɇɟɨɛɯɨɞɢɦɨ ɨɛɭɱɢɬɶ ɫɟɬɶ ɝɟɧɟɪɢɪɨɜɚɬɶ ɩɨɫɥɟɞɨɜɚɬɟɥɶɧɨɫɬɶ ɫɥɭɱɚɣɧɵɯ ɱɢɫɟɥ ɢɡ ɞɢɚɩɚɡɨɧɚ
[0,1] ɫ ɡɚɞɚɧɧɵɦɢ k ɩɟɪɜɵɦɢ ɦɨɦɟɧɬɚɦɢ. ɇɚɩɨɦɧɢɦ, ɱɬɨ ɞɥɹ ɜɵɛɨɪɤɢ ɪɨɥɶ
ɩɟɪɜɨɝɨ ɦɨɦɟɧɬɚ ɢɝɪɚɟɬ ɫɪɟɞɧɟɟ ɡɧɚɱɟɧɢɟ, ɜɬɨɪɨɝɨ - ɫɪɟɞɧɢɣ ɤɜɚɞɪɚɬ, ɬɪɟɬɶɟɝɨ - ɫɪɟɞɧɢɣ ɤɭɛ ɢ ɬɚɤ ɞɚɥɟɟ. ȿɫɬɶ ɞɜɚ ɩɭɬɢ ɪɟɲɟɧɢɹ ɷɬɨɣ ɡɚɞɚɱɢ. ɉɟɪɜɵɣ - ɢɫɩɨɥɶɡɭɹ ɫɬɚɧɞɚɪɬɧɵɣ ɝɟɧɟɪɚɬɨɪ ɫɥɭɱɚɣɧɵɯ ɱɢɫɟɥ ɩɨɞɝɨɬɨɜɢɬɶ ɡɚɞɚɱɧɢɤ ɢ ɨɛɭɱɢɬɶ ɩɨ ɧɟɦɭ ɫɟɬɶ. ɗɬɨɬ ɩɭɬɶ ɩɥɨɯ ɬɟɦ, ɱɬɨ ɬɚɤɨɣ ɝɟɧɟɪɚɬɨɪ ɛɭɞɟɬ ɩɪɨɫɬɨ ɜɨɫɩɪɨɢɡɜɨɞɢɬɶ ɩɨɫɥɟɞɨɜɚɬɟɥɶɧɨɫɬɶ ɱɢɫɟɥ, ɡɚɩɢɫɚɧɧɭɸ ɜ ɡɚɞɚɱɧɢɤɟ. Ⱦɥɹ ɩɨɥɭɱɟɧɢɹ ɬɚɤɨɝɨ ɪɟɡɭɥɶɬɚɬɚ ɦɨɠɧɨ ɩɪɨɫɬɨ ɯɪɚɧɢɬɶ ɡɚɞɚɱɧɢɤ. ȼɬɨɪɨɣ ɜɚɪɢɚɧɬ - ɨɛɭɱɚɬɶ ɫɟɬɶ ɛɟɡ ɡɚɞɚɱɧɢɤɚ! ɉɭɫɬɶ ɧɟɣɪɨɫɟɬɶ ɩɪɢɧɢɦɚɟɬ ɨɞɢɧ ɜɯɨɞɧɨɣ ɫɢɝɧɚɥ ɢ ɜɵɞɚɟɬ ɨɞɢɧ ɜɵɯɨɞɧɨɣ. ɉɪɢ ɢɫɩɨɥɶɡɨɜɚɧɢɢ ɫɟɬɢ ɜɵɯɨɞɧɨɣ ɫɢɝɧɚɥ ɩɟɪɜɨɝɨ ɫɪɚɛɚɬɵɜɚɧɢɹ ɫɟɬɢ (ɩɟɪɜɨɟ ɫɥɭɱɚɣɧɨɟ ɱɢɫɥɨ) ɛɭɞɟɬ ɫɥɭɠɢɬɶ ɜɯɨɞɧɵɦ ɫɢɝɧɚɥɨɦ ɞɥɹ ɜɬɨɪɨɝɨ ɫɪɚɛɚɬɵɜɚɧɢɹ ɫɟɬɢ ɢ ɬɚɤ ɞɚɥɟɟ. Ⱦɥɹ ɩɨɫɬɪɨɟɧɢɹ ɨɰɟɧɤɢ ɡɚɞɚɞɢɦɫɹ ɬɪɟɦɹ ɧɚɛɨɪɚɦɢ ɱɢɫɟɥ: M i - ɧɟɨɛɯɨɞɢɦɨɟ ɡɧɚɱɟɧɢɟ i-ɝɨ ɦɨɦɟɧɬɚ,
Li - ɞɥɢɧɚ ɩɨɫɥɟɞɨɜɚɬɟɥɶɧɨɫɬɢ, ɧɚ ɤɨɬɨɪɨɣ i-ɵɣ ɦɨɦɟɧɬ ɫɝɟɧɟɪɢɪɨɜɚɧɧɨɣ ɩɨɫɥɟɞɨɜɚɬɟɥɶɧɨɫɬɢ e i ɨɬɥɢɱɚɬɶɫɹ ɨɬ M i . e i - ɬɨɱɧɨɫɬɶ ɜɵɱɢɫɥɟɧɢɹ i-ɝɨ ɦɨɦɟɧɬɚ. ȼɵɛɨɪɨɱɧɚɹ ɨɰɟɧɤɚ ɫɨɜɩɚɞɟɧɢɹ i-ɝɨ ɦɨɦɟɧɬɚ ɜ ɫɝɟɧɟɪɢɪɨɜɚɧɧɨɣ ɩɨɫɥɟɞɨɜɚɬɟɥɶɧɨɫɬɢ ɧɚ ɨɬɪɟɡɤɟ, 1 j + Li -1 i j ɧɚɱɢɧɚɸɳɟɦɫɹ ɫ j-ɝɨ ɫɥɭɱɚɣɧɨɝɨ ɱɢɫɥɚ, ɜɵɱɢɫɥɹɟɬɫɹ ɩɨ ɫɥɟɞɭɸɳɟɣ ɮɨɪɦɭɥɟ: M i = å a l , ɝɞɟ Li l = j
ɞɨɥɠɟɧ ɧɟ ɛɨɥɟɟ ɱɟɦ ɧɚ
a l - ɜɵɯɨɞɧɨɣ ɫɢɝɧɚɥ, ɩɨɥɭɱɟɧɧɵɣ ɧɚ l-ɨɦ ɫɪɚɛɚɬɵɜɚɧɢɢ ɫɟɬɢ. Ⱦɥɹ ɨɰɟɧɤɢ ɬɨɱɧɨɫɬɢ ɫɨɜɩɚɞɟɧɢɹ i-ɝɨ
CHAP6.DOC
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ɦɨɦɟɧɬɚ ɜ ɫɝɟɧɟɪɢɪɨɜɚɧɧɨɣ ɩɨɫɥɟɞɨɜɚɬɟɥɶɧɨɫɬɢ ɧɚ ɨɬɪɟɡɤɟ, ɧɚɱɢɧɚɸɳɟɦɫɹ ɫ j-ɝɨ ɫɥɭɱɚɣɧɨɝɨ ɱɢɫɥɚ, ɜɨɫɩɨɥɶɡɭɟɦɫɹ ɨɰɟɧɤɨɣ ɱɢɫɥɚ ɫ ɞɨɩɭɫɤɨɦ e i :
ì 0, ɩɪɢ M i j - M i £ e i , ï 2 ï H ij = í M i j - M i - e i , ɩɪɢ M i j > M i + , e i ï 2 ï M i j - M i + e i , ɩɪɢ M i j < M i - e i . î Ɍɚɤɢɦ ɨɛɪɚɡɨɦ, ɩɪɢ ɨɛɭɱɟɧɢɢ ɫɟɬɢ ɝɟɧɟɪɚɰɢɢ ɩɨɫɥɟɞɨɜɚɬɟɥɶɧɨɫɬɢ ɢɡ N ɫɥɭɱɚɣɧɵɯ ɱɢɫɟɥ ɨɰɟɧɤɭ
( (
) )
ɦɨɠɧɨ ɡɚɩɢɫɚɬɶ ɜ ɫɥɟɞɭɸɳɟɦ ɜɢɞɟ:
H=å
åH
k
N - Li
i =1
j =1
i j
.
ɉɪɨɢɡɜɨɞɧɚɹ ɨɰɟɧɤɢ ɩɨ ɜɵɯɨɞɧɨɦɭ ɫɢɝɧɚɥɭ l-ɝɨ ɫɪɚɛɚɬɵɜɚɧɢɹ ɫɟɬɢ ɦɨɠɧɨ ɡɚɩɢɫɚɬɶ ɜ ɫɥɟɞɭɸɳɟɦ ɜɢɞɟ:
ì 0, ɩɪɢ M i j - M i £ e i , ï i -1 l + Li -1 ï ia k dH ï M i j - M i - e i , ɩɪɢ M i j > M i + e i , =å å í L da l i =1 j = l - Li +1 ï i i -1 ï ia M i j - M i + e i , ɩɪɢ M i j < M i - e i . ïî Li
( (
) )
ɂɫɩɨɥɶɡɭɹ ɷɬɭ ɨɰɟɧɤɭ ɦɨɠɧɨ ɨɛɭɱɚɬɶ ɫɟɬɶ ɝɟɧɟɪɢɪɨɜɚɬɶ ɫɥɭɱɚɣɧɵɟ ɱɢɫɥɚ. ɍɞɨɛɫɬɜɨ ɷɬɨɝɨ ɩɨɞɯɨɞɚ ɤ ɪɟɲɟɧɢɸ ɡɚɞɚɱɢ ɨɛɭɱɟɧɢɹ ɝɟɧɟɪɚɬɨɪɚ ɫɥɭɱɚɣɧɵɯ ɱɢɫɟɥ ɜ ɬɨɦ, ɱɬɨ ɦɨɠɧɨ ɞɨɫɬɚɬɨɱɧɨ ɱɚɫɬɨ ɦɟɧɹɬɶ ɢɧɢɰɢɢɪɭɸɳɢɣ ɫɟɬɶ ɜɯɨɞɧɨɣ ɫɢɝɧɚɥ, ɱɬɨ ɩɨɡɜɨɥɢɬ ɫɟɬɢ ɝɟɧɟɪɢɪɨɜɚɬɶ ɧɟ ɨɞɧɭ, ɚ ɦɧɨɝɨ ɪɚɡɥɢɱɧɵɯ ɩɨɫɥɟɞɨɜɚɬɟɥɶɧɨɫɬɟɣ, ɨɛɥɚɞɚɸɳɢɯ ɜɫɟɦɢ ɧɟɨɛɯɨɞɢɦɵɦɢ ɫɜɨɣɫɬɜɚɦɢ. ɉɪɢ ɢɫɩɨɥɶɡɨɜɚɧɢɢ ɩɪɟɞɥɨɠɟɧɧɨɣ ɨɰɟɧɤɢ ɧɟɬ ɧɢɤɚɤɢɯ ɝɚɪɚɧɬɢɣ ɬɨɝɨ, ɱɬɨ ɜ ɝɟɧɟɪɢɪɭɟɦɨɣ ɫɟɬɶɸ ɩɨɫɥɟɞɨɜɚɬɟɥɶɧɨɫɬɢ ɧɟ ɩɨɹɜɹɬɫɹ ɫɢɥɶɧɨ ɫɤɨɪɪɟɥɢɪɨɜɚɧɧɵɟ ɩɨɞɩɨɫɥɟɞɨɜɚɬɟɥɶɧɨɫɬɢ. Ⱦɥɹ ɭɞɚɥɟɧɢɹ ɤɨɪɪɟɥɹɰɢɣ ɦɨɠɧɨ ɦɨɞɢɮɢɰɢɪɨɜɚɬɶ ɨɰɟɧɤɭ ɬɚɤ, ɱɬɨɛɵ ɨɧɚ ɜɨɡɪɚɫɬɚɥɚ ɩɪɢ ɩɨɹɜɥɟɧɢɢ ɤɨɪɪɟɥɹɰɢɣ. Ɋɚɫɫɦɨɬɪɢɦ ɞɜɟ ɩɨɞɩɨɫɥɟɞɨɜɚɬɟɥɶɧɨɫɬɢ ɞɥɢɧɧɵ L, ɩɟɪɜɚɹ ɢɡ ɤɨɬɨɪɵɯ ɧɚɱɢɧɚɟɬɫɹ ɫ a i , ɚ ɞɪɭɝɚɹ ɫ a i + h . Ʉɨɷɮɮɢɰɢɟɧɬ ɤɨɪɪɟɥɹɰɢɢ ɷɬɢɯ ɩɨɫɥɟɞɨɜɚɬɟɥɶɧɨɫɬɟɣ ɡɚɩɢɫɵɜɚɟɬɫɹ ɜ ɜɢɞɟ:
rih =
1 L-1 å a i+ ja i+ j+h - a i a i+h L j=0 a i2 - a i
2
a i2+ h - a i + h
ȼ ɷɬɨɣ ɮɨɪɦɭɥɟ ɩɪɢɧɹɬɵ ɫɥɟɞɭɸɳɢɟ ɨɛɨɡɧɚɱɟɧɢɹ:
2
.
a i - ɫɪɟɞɧɟɟ ɩɨ ɩɨɫɥɟɞɨɜɚɬɟɥɶɧɨɫɬɢ, ɧɚɱɢ-
ɧɚɸɳɟɣɫɹ ɫ a i ; a i - ɫɪɟɞɧɢɣ ɤɜɚɞɪɚɬ ɩɨɫɥɟɞɨɜɚɬɟɥɶɧɨɫɬɢ ɧɚɱɢɧɚɸɳɟɣɫɹ ɫ a i . ȼɵɱɢɫɥɟɧɢɟ ɬɚɤɨɝɨ ɤɨɷɮɮɢɰɢɟɧɬɚ ɤɨɪɪɟɥɹɰɢɢ ɞɨɜɨɥɶɧɨ ɞɨɥɝɢɣ ɩɪɨɰɟɫɫ. Ɉɞɧɚɤɨ ɜɦɟɫɬɨ ɜɵɛɨɪɨɱɧɵɯ ɦɨɦɟɧɬɨɜ ɜ ɮɨɪɦɭɥɭ ɦɨɠɧɨ ɩɨɞɫɬɚɜɢɬɶ ɡɧɚɱɟɧɢɹ ɦɨɦɟɧɬɨɜ, ɤɨɬɨɪɵɟ ɩɨɫɥɟɞɨɜɚɬɟɥɶɧɨɫɬɶ ɞɨɥɠɧɚ ɢɦɟɬɶ. ȼ ɷɬɨɦ ɫɥɭɱɚɟ ɮɨɪɦɭɥɚ ɫɢɥɶɧɨ ɭɩɪɨɳɚɟɬɫɹ: 2
rih =
1 L -1 å a i + j a i + j + h - M 12 L j=0 M 2 - M 12
.
Ⱦɨɛɚɜɤɭ ɞɥɹ ɭɞɚɥɟɧɢɹ ɤɨɪɪɟɥɹɰɢɣ ɩɨɫɥɟɞɨɜɚɬɟɥɶɧɨɫɬɟɣ ɞɥɢɧɨɣ ɨɬ ɨɬɧɨɫɢɬɟɥɶɧɨ ɞɪɭɝɚ ɧɚ ɫɦɟɳɟɧɢɹ ɨɬ
CHAP6.DOC
h1 ɞɨ h2 ɦɨɠɧɨ ɡɚɩɢɫɚɬɶ ɜ ɜɢɞɟ:
120
L1 ɞɨ L2 ɢ ɫɦɟɳɟɧɧɵɯ ɞɪɭɝ
æ 1 L -1 2ö ç å a i + ja i + j +h - M 1 ÷ L 0 j = ç ÷ . ç ÷ M 2 - M 12 ç ÷ è ø 2
Hr =
åå å L2
h2 N - h - L +1
L = L1 h = h1
i =1
ɉɪɢ ɧɟɨɛɯɨɞɢɦɨɫɬɢ ɦɨɠɧɨ ɜɜɟɫɬɢ ɢ ɞɪɭɝɢɟ ɩɨɩɪɚɜɤɢ, ɭɱɢɬɵɜɚɸɳɢɟ ɬɪɟɛɨɜɚɧɢɹ ɤ ɝɟɧɟɪɚɬɨɪɭ ɫɥɭɱɚɣɧɵɯ ɱɢɫɟɥ.
6.6 ɋɨɫɬɚɜɧɵɟ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɨɬɜɟɬɚ ɢ ɨɰɟɧɤɚ ɉɪɢ ɢɫɩɨɥɶɡɨɜɚɧɢɢ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ɞɥɹ ɪɟɲɟɧɢɹ ɪɚɡɥɢɱɧɵɯ ɡɚɞɚɱ ɜɨɡɧɢɤɚɟɬ ɧɟɨɛɯɨɞɢɦɨɫɬɶ ɩɨɥɭɱɚɬɶ ɨɬ ɫɟɬɢ ɧɟ ɨɞɢɧ ɨɬɜɟɬ, ɚ ɧɟɫɤɨɥɶɤɨ. ɇɚɩɪɢɦɟɪ, ɩɪɢ ɨɛɭɱɟɧɢɢ ɫɟɬɢ ɪɟɲɟɧɢɸ ɡɚɞɚɱɢ ɞɢɚɝɧɨɫɬɢɤɢ ɨɬɤɥɨɧɟɧɢɣ ɜ ɪɟɚɤɰɢɢ ɧɚ ɫɬɪɟɫɫ ɧɟɣɪɨɧɧɚɹ ɫɟɬɶ ɞɨɥɠɧɚ ɛɵɥɚ ɨɩɪɟɞɟɥɢɬɶ ɧɚɥɢɱɢɟ ɢɥɢ ɨɬɫɭɬɫɬɜɢɟ ɬɪɢɧɚɞɰɚɬɢ ɪɚɡɥɢɱɧɵɯ ɩɚɬɨɥɨɝɢɣ. ȿɫɥɢ ɨɞɧɚ ɫɟɬɶ ɦɨɠɟɬ ɜɵɞɚɜɚɬɶ ɬɨɥɶɤɨ ɨɞɢɧ ɨɬɜɟɬ, ɬɨ ɞɥɹ ɪɟɲɟɧɢɹ ɡɚɞɚɱɢ ɧɟɨɛɯɨɞɢɦɨ ɡɚɞɟɣɫɬɜɨɜɚɬɶ ɬɪɢɧɚɞɰɚɬɶ ɫɟɬɟɣ. Ɉɞɧɚɤɨ ɜ ɷɬɨɦ ɧɟɬ ɧɟɨɛɯɨɞɢɦɨɫɬɢ. ɉɨɫɤɨɥɶɤɭ ɤɚɠɞɵɣ ɨɬɜɟɬ, ɤɨɬɨɪɵɣ ɞɨɥɠɧɚ ɜɵɞɚɜɚɬɶ ɫɟɬɶ, ɢɦɟɟɬ ɬɨɥɶɤɨ ɞɜɚ ɜɚɪɢɚɧɬɚ, ɬɨ ɦɨɠɧɨ ɢɫɩɨɥɶɡɨɜɚɬɶ ɞɥɹ ɟɝɨ ɩɨɥɭɱɟɧɢɹ ɤɥɚɫɫɢɮɢɤɚɬɨɪ ɧɚ ɞɜɚ ɤɥɚɫɫɚ. Ⱦɥɹ ɬɚɤɨɝɨ ɤɥɚɫɫɢɮɢɤɚɬɨɪɚ ɧɟɨɛɯɨɞɢɦɨ ɞɜɚ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɚ. Ɍɨɝɞɚ ɞɥɹ ɪɟɲɟɧɢɹ ɡɚɞɚɱɢ ɞɨɫɬɚɬɨɱɧɨ ɩɨɥɭɱɚɬɶ 26 ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ: ɩɟɪɜɵɟ ɞɜɚ ɫɢɝɧɚɥɚ - ɞɥɹ ɨɩɪɟɞɟɥɟɧɢɹ ɩɟɪɜɨɣ ɩɚɬɨɥɨɝɢɢ, ɬɪɟɬɢɣ ɢ ɱɟɬɜɟɪɬɵɣ - ɞɥɹ ɜɬɨɪɨɣ ɢ ɬɚɤ ɞɚɥɟɟ. Ɍɚɤɢɦ ɨɛɪɚɡɨɦ, ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɨɬɜɟɬɚ ɞɥɹ ɷɬɨɣ ɡɚɞɚɱɢ ɫɨɫɬɨɢɬ ɢɡ ɬɪɢɧɚɞɰɚɬɢ ɢɧɬɟɪɩɪɟɬɚɬɨɪɨɜ, ɚ ɨɰɟɧɤɚ ɢɡ ɬɪɢɧɚɞɰɚɬɢ ɨɰɟɧɨɤ. Ȼɨɥɟɟ ɬɨɝɨ, ɧɟɬ ɧɢɤɚɤɢɯ ɨɝɪɚɧɢɱɟɧɢɣ ɧɚ ɬɢɩɵ ɢɫɩɨɥɶɡɭɟɦɵɯ ɢɧɬɟɪɩɪɟɬɚɬɨɪɨɜ ɢɥɢ ɨɰɟɧɨɤ. ȼɨɡɦɨɠɧɚ ɤɨɦɛɢɧɚɰɢɹ, ɧɚɩɪɢɦɟɪ, ɫɥɟɞɭɸɳɢɯ ɨɬɜɟɬɨɜ. 1. ɑɢɫɥɨ ɫ ɞɨɩɭɫɤɨɦ. 2. Ʉɥɚɫɫɢɮɢɤɚɬɨɪ ɧɚ ɜɨɫɟɦɶ ɤɥɚɫɫɨɜ. 3. ɋɥɭɱɚɣɧɨɟ ɱɢɫɥɨ. ɉɪɢ ɢɫɩɨɥɶɡɨɜɚɧɢɢ ɬɚɤɢɯ ɫɨɫɬɚɜɧɵɯ ɨɰɟɧɨɤ ɢ ɢɧɬɟɪɩɪɟɬɚɬɨɪɨɜ ɤɚɠɞɵɣ ɢɡ ɷɬɢɯ ɤɨɦɩɨɧɟɧɬɨɜ ɞɨɥɠɟɧ ɫɥɟɞɢɬɶ ɡɚ ɬɟɦ, ɱɬɨɛɵ ɤɚɠɞɚɹ ɱɚɫɬɧɚɹ ɨɰɟɧɤɚ ɢɥɢ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɩɨɥɭɱɚɥɢ ɧɚ ɜɯɨɞ ɬɟ ɞɚɧɧɵɟ, ɤɨɬɨɪɵɟ ɢɦ ɧɟɨɛɯɨɞɢɦɵ.
6.7 ɋɬɚɧɞɚɪɬ ɩɟɪɜɨɝɨ ɭɪɨɜɧɹ ɤɨɦɩɨɧɟɧɬɚ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɨɬɜɟɬɚ Ⱦɚɧɧɵɣ ɪɚɡɞɟɥ ɩɨɫɜɹɳɟɧ ɨɩɢɫɚɧɢɸ ɫɬɚɧɞɚɪɬɚ ɡɚɩɢɫɢ ɧɚ ɞɢɫɤ ɤɨɦɩɨɧɟɧɬɚ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɨɬɜɟɬɨɜ. ɉɨɫɬɪɨɟɧɢɟ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ ɩɪɨɢɫɯɨɞɢɬ ɜ ɪɟɞɚɤɬɨɪɟ ɢɧɬɟɪɩɪɟɬɚɬɨɪɨɜ ɨɬɜɟɬɚ. ɂɧɬɟɪɩɪɟɬɚɬɨɪ ɨɬɜɟɬɚ ɜɫɟɝɞɚ ɹɜɥɹɟɬɫɹ ɫɨɫɬɚɜɧɵɦ, ɞɚɠɟ ɟɫɥɢ ɜɵɯɨɞɨɦ ɹɜɥɹɟɬɫɹ ɨɞɢɧ ɨɬɜɟɬ. ȼ ɫɨɫɬɚɜ ɷɬɨɝɨ ɨɛɴɟɤɬɚ ɜɯɨɞɹɬ ɱɚɫɬɧɵɟ ɢɧɬɟɪɩɪɟɬɚɬɨɪɵ. Ʉɪɨɦɟ ɬɨɝɨ, ɨɩɢɫɚɧɢɟ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ ɞɨɥɠɧɨ ɜɤɥɸɱɚɬɶ ɜ ɫɟɛɹ ɩɪɚɜɢɥɚ ɪɚɫɩɪɟɞɟɥɟɧɢɹ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɫɟɬɢ ɦɟɠɞɭ ɱɚɫɬɧɵɦɢ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚɦɢ ɢ ɪɚɫɩɨɥɨɠɟɧɢɹ ɨɬɜɟɬɨɜ ɱɚɫɬɧɵɯ ɢɧɬɟɪɩɪɟɬɚɬɨɪɨɜ ɜ ɟɞɢɧɨɦ ɦɚɫɫɢɜɟ ɨɬɜɟɬɨɜ. Ɍɚɤɢɦ ɨɛɪɚɡɨɦ, ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɨɬɜɟɬɚ ɩɪɢ ɜɵɩɨɥɧɟɧɢɢ ɡɚɩɪɨɫɚ ɧɚ ɢɧɬɟɪɩɪɟɬɚɰɢɸ ɦɚɫɫɢɜɚ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɫɟɬɢ ɩɨɥɭɱɚɟɬ ɧɚ ɜɯɨɞɟ ɦɚɫɫɢɜ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɫɟɬɢ, ɚ ɜɨɡɜɪɚɳɚɟɬ ɞɜɚ ɦɚɫɫɢɜɚ – ɨɬɜɟɬɨɜ ɢ ɤɨɷɮɮɢɰɢɟɧɬɨɜ ɭɜɟɪɟɧɧɨɫɬɢ. Ʉɚɠɞɵɣ ɱɚɫɬɧɵɣ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɨɬɜɟɬɚ ɩɨɥɭɱɚɟɬ ɧɚ ɜɯɨɞɟ ɦɚɫɫɢɜ ɫɢɝɧɚɥɨɜ (ɜɨɡɦɨɠɧɨ ɢɡ ɨɞɧɨɝɨ ɷɥɟɦɟɧɬɚ), ɤɨɬɨɪɵɟ ɨɧ ɢɧɬɟɪɩɪɟɬɢɪɭɟɬ, ɚ ɧɚ ɜɵɯɨɞɟ ɜɨɡɜɪɚɳɚɟɬ ɞɜɚ ɱɢɫɥɚ – ɨɬɜɟɬ ɢ ɤɨɷɮɮɢɰɢɟɧɬ ɭɜɟɪɟɧɧɨɫɬɢ ɜ ɷɬɨɦ ɨɬɜɟɬɟ. Ɍɚɛɥɢɰɚ 1. Ʉɥɸɱɟɜɵɟ ɫɥɨɜɚ ɹɡɵɤɚ ɨɩɢɫɚɧɢɹ ɢɧɬɟɪɩɪɟɬɚɬɨɪɨɜ ɨɬɜɟɬɚ. Ʉɥɸɱɟɜɨɟ ɫɥɨɜɨɄɪɚɬɤɨɟ ɨɩɢɫɚɧɢɟ 1. Answer Ɉɬɜɟɬ. 2. Connections ɇɚɱɚɥɨ ɛɥɨɤɚ ɨɩɢɫɚɧɢɹ ɪɚɫɩɪɟɞɟɥɟɧɢɹ ɫɢɝɧɚɥɨɜ ɢ ɨɬɜɟɬɨɜ. 3. Contents ɇɚɱɚɥɨ ɛɥɨɤɚ ɨɩɢɫɚɧɢɹ ɫɨɫɬɚɜɚ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ. 4. Include ɉɪɟɞɲɟɫɬɜɭɟɬ ɢɦɟɧɢ ɮɚɣɥɚ, ɰɟɥɢɤɨɦ ɜɫɬɚɜɥɹɟɦɨɝɨ ɜ ɷɬɨ ɦɟɫɬɨ ɨɩɢɫɚɧɢɹ. 5. Interpretator Ɂɚɝɨɥɨɜɨɤ ɪɚɡɞɟɥɚ ɮɚɣɥɚ, ɫɨɞɟɪɠɚɳɢɣ ɨɩɢɫɚɧɢɟ ɢɧɬɟɪɩɪɟɬɚɬɨɪ. 6. NumberOf Ɏɭɧɤɰɢɹ. ȼɨɡɜɪɚɳɚɟɬ ɱɢɫɥɨ ɢɧɬɟɪɩɪɟɬɢɪɭɟɦɵɯ ɱɚɫɬɧɵɦ ɢɧɬɟɪɩɪɟɬɚɬɨɪɨɦ ɫɢɝɧɚɥɨɜ. 7. Reliability Ʉɨɷɮɮɢɰɢɟɧɬ ɭɜɟɪɟɧɧɨɫɬɢ. 8. Signals ɂɦɹ, ɩɨ ɤɨɬɨɪɨɦɭ ɚɞɪɟɫɭɸɬɫɹ ɢɧɬɟɪɩɪɟɬɢɪɭɟɦɵɟ ɫɢɝɧɚɥɵ; ɧɚɱɚɥɨ ɛɥɨɤɚ ɨɩɢɫɚɧɢɹ ɫɢɝɧɚɥɨɜ. 9. SetParameters ɉɪɨɰɟɞɭɪɚ ɭɫɬɚɧɨɜɥɟɧɢɹ ɡɧɚɱɟɧɢɣ ɩɚɪɚɦɟɬɪɨɜ.
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Ɍɚɛɥɢɰɚ 2. ɋɬɚɧɞɚɪɬɧɵɟ ɱɚɫɬɧɵɟ ɢɧɬɟɪɩɪɟɬɚɬɨɪɵ. ɇɚɡɜɚɧɢɟɉɚɪɚɦɟɬɪɵȺɪɝɭɦɟɧɬɵɈɩɢɫɚɧɢɟ Empty B – ɦɧɨɠɢɬɟɥɶ ɂɧɬɟɪɩɪɟɬɢɪɭɟɬ ɨɞɢɧ ɫɢɝɧɚɥ Ⱥ. Ɉɬɜɟɬɨɦ C – ɫɦɟɳɟɧɢɟ ɹɜɥɹɟɬɫɹ ɜɟɥɢɱɢɧɚ Ɉ=Ⱥ*ȼ+ɋ Binary E – ɭɪɨɜɟɧɶ ɧɚɞɟɠɧɨɫɬɢ N – ɱɢɫɥɨ ɫɢɝɧɚɥɨɜ Ʉɨɞɢɪɨɜɚɧɢɟ ɧɨɦɟɪɨɦ ɤɚɧɚɥɚ. Ɂɧɚɤɨɜɵɣ (ɤɥɚɫɫɨɜ) ɢɧɬɟɪɩɪɟɬɚɬɨɪ Major E – ɭɪɨɜɟɧɶ ɧɚɞɟɠɧɨɫɬɢ N – ɱɢɫɥɨ ɫɢɝɧɚɥɨɜ Ʉɨɞɢɪɨɜɚɧɢɟ ɧɨɦɟɪɨɦ ɤɚɧɚɥɚ. Ɇɚɤɫɢ(ɤɥɚɫɫɨɜ) ɦɚɥɶɧɵɣ ɢɧɬɟɪɩɪɟɬɚɬɨɪ. BynaryCoded E – ɭɪɨɜɟɧɶ ɧɚɞɟɠɧɨɫɬɢ N – ɱɢɫɥɨ ɫɢɝɧɚɥɨɜ Ⱦɜɨɢɱɧɵɣ ɢɧɬɟɪɩɪɟɬɚɬɨɪ. (ɤɥɚɫɫɨɜ) ȼ ɬɚɛɥ. 1 ɩɪɢɜɟɞɟɧ ɫɩɢɫɨɤ ɤɥɸɱɟɜɵɯ ɫɥɨɜ, ɫɩɟɰɢɮɢɱɟɫɤɢɯ ɞɥɹ ɹɡɵɤɚ ɨɩɢɫɚɧɢɹ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ ɨɬɜɟɬɨɜ. ɇɚɢɛɨɥɟɟ ɱɚɫɬɨ ɜɫɬɪɟɱɚɸɳɢɟɫɹ ɢɧɬɟɪɩɪɟɬɚɬɨɪɵ ɨɛɴɹɜɥɟɧɵ ɫɬɚɧɞɚɪɬɧɵɦɢ. Ⱦɥɹ ɫɬɚɧɞɚɪɬɧɵɯ ɢɧɬɟɪɩɪɟɬɚɬɨɪɨɜ ɨɩɢɫɚɧɢɟ ɱɚɫɬɧɵɯ ɢɧɬɟɪɩɪɟɬɚɬɨɪɨɜ ɨɬɫɭɬɫɬɜɭɟɬ. ɋɩɢɫɨɤ ɫɬɚɧɞɚɪɬɧɵɯ ɢɧɬɟɪɩɪɟɬɚɬɨɪɨɜ ɩɪɢɜɟɞɟɧ ɜ ɬɚɛɥ. 2.
6.7.1 ȻɇɎ ɹɡɵɤɚ ɨɩɢɫɚɧɢɹ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ Ɉɛɨɡɧɚɱɟɧɢɹ, ɩɪɢɧɹɬɵɟ ɜ ɞɚɧɧɨɦ ɪɚɫɲɢɪɟɧɢɢ ȻɇɎ ɢ ɨɩɢɫɚɧɢɟ ɪɹɞɚ ɤɨɧɫɬɪɭɤɰɢɣ ɩɪɢɜɟɞɟɧɵ ɜ ɝɥɚɜɟ «Ɉɛɳɢɣ ɫɬɚɧɞɚɪɬ» ɜ ɪɚɡɞɟɥɟ «Ɉɩɢɫɚɧɢɟ ɹɡɵɤɚ ɨɩɢɫɚɧɢɹ ɤɨɦɩɨɧɟɧɬɨɜ». <Ɉɩɢɫɚɧɢɟ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ> ::= <Ɂɚɝɨɥɨɜɨɤ> [<Ɉɩɢɫɚɧɢɟ ɮɭɧɤɰɢɣ>] <Ɉɩɢɫɚɧɢɟ ɱɚɫɬɧɵɯ ɢɧɬɟɪɩɪɟɬɚɬɨɪɨɜ> <Ɉɩɢɫɚɧɢɟ ɫɨɫɬɚɜɚ> [<ɍɫɬɚɧɨɜɥɟɧɢɟ ɩɚɪɚɦɟɬɪɨɜ>] [<Ɉɩɢɫɚɧɢɟ ɫɢɝɧɚɥɨɜ>] [<Ɉɩɢɫɚɧɢɟ ɪɚɫɩɪɟɞɟɥɟɧɢɹ ɫɢɝɧɚɥɨɜ>] [<Ɉɩɢɫɚɧɢɟ ɪɚɫɩɪɟɞɟɥɟɧɢɹ ɨɬɜɟɬɨɜ>] <Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ> <Ɂɚɝɨɥɨɜɨɤ> ::= Interpretator <ɂɦɹ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ> <ɂɦɹ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ>::= <ɂɞɟɧɬɢɮɢɤɚɬɨɪ> <Ɉɩɢɫɚɧɢɟ ɱɚɫɬɧɵɯ ɢɧɬɟɪɩɪɟɬɚɬɨɪɨɜ> ::= <Ɉɩɢɫɚɧɢɟ ɱɚɫɬɧɨɝɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ> [<Ɉɩɢɫɚɧɢɟ ɱɚɫɬɧɵɯ ɢɧɬɟɪɩɪɟɬɚɬɨɪɨɜ>] <Ɉɩɢɫɚɧɢɟ ɱɚɫɬɧɨɝɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ> ::= <Ɂɚɝɨɥɨɜɨɤ ɨɩɢɫɚɧɢɹ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ> [<Ɉɩɢɫɚɧɢɟ ɫɬɚɬɢɱɟɫɤɢɯ ɩɟɪɟɦɟɧɧɵɯ >] [<Ɉɩɢɫɚɧɢɟ ɩɟɪɟɦɟɧɧɵɯ>] <Ɍɟɥɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ> <Ɂɚɝɨɥɨɜɨɤ ɨɩɢɫɚɧɢɹ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ> ::= Inter <ɂɦɹ ɱɚɫɬɧɨɝɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ> : (<ɋɩɢɫɨɤ ɮɨɪɦɚɥɶɧɵɯ ɚɪɝɭɦɟɧɬɨɜ>) <ɂɦɹ ɱɚɫɬɧɨɝɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ> ::= <ɂɞɟɧɬɢɮɢɤɚɬɨɪ> <Ɍɟɥɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ> ::= Begin <ɋɨɫɬɚɜɧɨɣ ɨɩɟɪɚɬɨɪ> End <Ɉɩɢɫɚɧɢɟ ɫɨɫɬɚɜɚ> ::= Contents <ɋɩɢɫɨɤ ɢɦɟɧ ɢɧɬɟɪɩɪɟɬɚɬɨɪɨɜ> ; <ɋɩɢɫɨɤ ɢɦɟɧ ɢɧɬɟɪɩɪɟɬɚɬɨɪɨɜ> ::= <ɂɦɹ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ> [,<ɋɩɢɫɨɤ ɢɦɟɧ ɢɧɬɟɪɩɪɟɬɚɬɨɪɨɜ >] <ɂɦɹ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ> ::= <ɉɫɟɜɞɨɧɢɦ>: {<ɂɦɹ ɪɚɧɟɟ ɨɩɢɫɚɧɧɨɝɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ> ½ <ɂɦɹ ɫɬɚɧɞɚɪɬɧɨɝɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ>} [[<ɑɢɫɥɨ ɷɤɡɟɦɩɥɹɪɨɜ >]][(<ɋɩɢɫɨɤ ɮɚɤɬɢɱɟɫɤɢɯ ɚɪɝɭɦɟɧɬɨɜ>)] <ɉɫɟɜɞɨɧɢɦ> ::= <ɂɞɟɧɬɢɮɢɤɚɬɨɪ> <ɑɢɫɥɨ ɷɤɡɟɦɩɥɹɪɨɜ > ::= <ɐɟɥɨɟ ɱɢɫɥɨ> <ɂɦɹ ɪɚɧɟɟ ɨɩɢɫɚɧɧɨɝɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ> ::= <ɂɞɟɧɬɢɮɢɤɚɬɨɪ> <ɂɦɹ ɫɬɚɧɞɚɪɬɧɨɝɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ> ::= <ɂɞɟɧɬɢɮɢɤɚɬɨɪ> <ɍɫɬɚɧɨɜɥɟɧɢɟ ɩɚɪɚɦɟɬɪɨɜ> ::= <ɍɫɬɚɧɨɜɥɟɧɢɟ ɩɚɪɚɦɟɬɪɨɜ ɑɚɫɬɧɨɝɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ> [;<ɍɫɬɚɧɨɜɥɟɧɢɟ ɩɚɪɚɦɟɬɪɨɜ>] <Ɉɩɢɫɚɧɢɟ ɫɢɝɧɚɥɨɜ> ::= Signals <Ʉɨɧɫɬɚɧɬɧɨɟ ɜɵɪɚɠɟɧɢɟ ɬɢɩɚ Long> <Ɉɩɢɫɚɧɢɟ ɪɚɫɩɪɟɞɟɥɟɧɢɹ ɫɢɝɧɚɥɨɜ> ::= <Ɉɩɢɫɚɧɢɟ ɪɚɫɩɪɟɞɟɥɟɧɢɹ ɋɢɝɧɚɥɨɜ, ɂɧɬɟɪɩɪɟɬɚɬɨɪɚ, ɑɚɫɬɧɨɝɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ, Signals> <Ɉɩɢɫɚɧɢɟ ɪɚɫɩɪɟɞɟɥɟɧɢɹ ɨɬɜɟɬɨɜ> ::= <Ɉɩɢɫɚɧɢɟ ɪɚɫɩɪɟɞɟɥɟɧɢɹ Ɉɬɜɟɬɨɜ, ɂɧɬɟɪɩɪɟɬɚɬɨɪɚ, ɑɚɫɬɧɨɝɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ, Answer> <Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ> ::= End Interpretator
6.7.2 Ɉɩɢɫɚɧɢɟ ɹɡɵɤɚ ɨɩɢɫɚɧɢɹ ɢɧɬɟɪɩɪɟɬɚɬɨɪɨɜ ɋɬɪɭɤɬɭɪɚ ɨɩɢɫɚɧɢɹ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ ɢɦɟɟɬ ɜɢɞ: ɡɚɝɨɥɨɜɨɤ, ɨɩɢɫɚɧɢɟ ɱɚɫɬɧɵɯ ɢɧɬɟɪɩɪɟɬɚɬɨɪɨɜ, ɨɩɢɫɚɧɢɟ ɫɨɫɬɚɜɚ, ɨɩɢɫɚɧɢɟ ɫɢɝɧɚɥɨɜ, ɨɩɢɫɚɧɢɟ ɪɚɫɩɪɟɞɟɥɟɧɢɹ ɫɢɝɧɚɥɨɜ, ɨɩɢɫɚɧɢɟ ɪɚɫɩɪɟɞɟɥɟɧɢɹ ɨɬɜɟɬɨɜ, ɤɨɧɟɰ ɨɩɢɫɚɧɢɹ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ. Ɂɚɝɨɥɨɜɨɤ ɫɨɫɬɨɢɬ ɢɡ ɤɥɸɱɟɜɨɝɨ ɫɥɨɜɚ Interpretator ɢ ɢɦɟɧɢ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ ɢ ɫɥɭɠɢɬ ɞɥɹ ɨɛɨɡɧɚɱɟɧɢɹ ɧɚɱɚɥɚ ɨɩɢɫɚɧɢɹ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ ɜ ɮɚɣɥɟ, ɫɨɞɟɪɠɚɳɟɦ ɧɟɫɤɨɥɶɤɨ ɤɨɦɩɨɧɟɧɬɨɜ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚ.
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Ɉɩɢɫɚɧɢɟ ɱɚɫɬɧɨɝɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ – ɷɬɨ ɨɩɢɫɚɧɢɟ ɩɪɨɰɟɞɭɪɵ, ɜɵɱɢɫɥɹɸɳɟɣ ɞɜɟ ɜɟɥɢɱɢɧɵ: ɨɬɜɟɬ ɢ ɭɜɟɪɟɧɧɨɫɬɶ ɜ ɨɬɜɟɬɟ. Ɉɬɦɟɬɢɦ, ɱɬɨ ɭɜɟɪɟɧɧɨɫɬɶ ɜ ɨɬɜɟɬɟ ɢɦɟɟɬ ɫɦɵɫɥ ɬɨɥɶɤɨ ɞɥɹ ɨɰɟɧɨɤ ɫ ɭɪɨɜɧɟɦ ɧɚɞɟɠɧɨɫɬɢ. ȼ ɨɫɬɚɥɶɧɵɯ ɫɥɭɱɚɹɯ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɨɬɜɟɬɚ ɦɨɠɟɬ ɜɵɱɢɫɥɹɬɶ ɚɧɚɥɨɝɢɱɧɭɸ ɜɟɥɢɱɢɧɭ, ɧɨ ɷɬɚ ɜɟɥɢɱɢɧɚ ɧɟ ɹɜɥɹɟɬɫɹ ɤɨɷɮɮɢɰɢɟɧɬɨɦ ɭɜɟɪɟɧɧɨɫɬɢ ɜ ɨɬɜɟɬɟ ɜ ɬɨɱɧɨɦ ɫɦɵɫɥɟ. Ɉɬɦɟɬɢɦ, ɱɬɨ ɩɪɢ ɨɩɢɫɚɧɢɢ ɱɚɫɬɧɨɝɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ ɟɝɨ ɚɪɝɭɦɟɧɬɨɦ, ɤɚɤ ɩɪɚɜɢɥɨ, ɹɜɥɹɟɬɫɹ ɱɢɫɥɨ ɢɧɬɟɪɩɪɟɬɢɪɭɟɦɵɯ ɫɢɝɧɚɥɨɜ. ɉɪɢ ɜɵɩɨɥɧɟɧɢɢ ɱɚɫɬɧɵɣ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɩɨɥɭɱɚɟɬ ɜ ɤɚɱɟɫɬɜɟ ɚɪɝɭɦɟɧɬɨɜ ɦɚɫɫɢɜ ɢɧɬɟɪɩɪɟɬɢɪɭɟɦɵɯ ɫɢɝɧɚɥɨɜ ɢ ɞɜɟ ɞɟɣɫɬɜɢɬɟɥɶɧɵɟ ɩɟɪɟɦɟɧɧɵɟ ɞɥɹ ɜɨɡɜɪɚɳɟɧɢɹ ɜɵɱɢɫɥɟɧɧɵɯ ɨɬɜɟɬɚ ɢ ɭɜɟɪɟɧɧɨɫɬɢ ɜ ɨɬɜɟɬɟ. Ɏɨɪɦɚɥɶɧɨ, ɩɪɢ ɢɫɩɨɥɧɟɧɢɢ, ɱɚɫɬɧɵɣ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɢɦɟɟɬ ɨɩɢɫɚɧɢɟ ɫɥɟɞɭɸɳɟɝɨ ɜɢɞɚ: Pascal: Procedure Interpretator(Signals : PRealArray; Var Answer, Reliability : Real); C: void Interpretator(PRealArray Signals, Real* Answer, Real* Reliability); ȼ ɪɚɡɞɟɥɟ ɨɩɢɫɚɧɢɹ ɫɨɫɬɚɜɚ ɩɟɪɟɱɢɫɥɹɸɬɫɹ ɱɚɫɬɧɵɟ ɢɧɬɟɪɩɪɟɬɚɬɨɪɵ, ɜɯɨɞɹɳɢɟ ɜ ɫɨɫɬɚɜ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ. ɉɪɢɡɧɚɤɨɦ ɤɨɧɰɚ ɪɚɡɞɟɥɚ ɫɥɭɠɢɬ ɫɢɦɜɨɥ «;». ȼ ɧɟɨɛɹɡɚɬɟɥɶɧɨɦ ɪɚɡɞɟɥɟ ɭɫɬɚɧɨɜɥɟɧɢɹ ɩɚɪɚɦɟɬɪɨɜ ɩɪɨɢɡɜɨɞɢɬɫɹ ɡɚɞɚɧɢɟ ɡɧɚɱɟɧɢɣ ɩɚɪɚɦɟɬɪɨɜ (ɫɬɚɬɢɱɟɫɤɢɯ ɩɟɪɟɦɟɧɧɵɯ) ɱɚɫɬɧɵɯ ɢɧɬɟɪɩɪɟɬɚɬɨɪɨɜ. ɉɨɫɥɟ ɤɥɸɱɟɜɨɝɨ ɫɥɨɜɚ SetParameters ɫɥɟɞɭɟɬ ɫɩɢɫɨɤ ɡɧɚɱɟɧɢɣ ɩɚɪɚɦɟɬɪɨɜ ɜ ɬɨɦ ɩɨɪɹɞɤɟ, ɜ ɤɚɤɨɦ ɩɚɪɚɦɟɬɪɵ ɛɵɥɢ ɨɛɴɹɜɥɟɧɵ ɩɪɢ ɨɩɢɫɚɧɢɢ ɱɚɫɬɧɨɝɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ (ɞɥɹ ɫɬɚɧɞɚɪɬɧɵɯ ɢɧɬɟɪɩɪɟɬɚɬɨɪɨɜ ɩɨɪɹɞɨɤ ɩɚɪɚɦɟɬɪɨɜ ɭɤɚɡɚɧ ɜ ɬɚɛɥ. 2). ɉɪɢ ɢɫɩɨɥɶɡɨɜɚɧɢɢ ɨɞɧɨɝɨ ɨɩɟɪɚɬɨɪɚ ɡɚɞɚɧɢɹ ɩɚɪɚɦɟɬɪɨɜ ɞɥɹ ɡɚɞɚɧɢɹ ɩɚɪɚɦɟɬɪɨɜ ɧɟɫɤɨɥɶɤɢɦ ɷɤɡɟɦɩɥɹɪɚɦ ɨɞɧɨɝɨ ɱɚɫɬɧɨɝɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ ɩɨɫɥɟ ɤɥɸɱɟɜɨɝɨ ɫɥɨɜɚ SetParameters ɭɤɚɡɵɜɚɟɬɫɹ ɫɬɨɥɶɤɨ ɜɵɪɚɠɟɧɢɣ, ɡɚɞɚɸɳɢɯ ɡɧɚɱɟɧɢɹ ɩɚɪɚɦɟɬɪɨɜ, ɫɤɨɥɶɤɨ ɧɟɨɛɯɨɞɢɦɨ ɞɥɹ ɨɞɧɨɝɨ ɷɤɡɟɦɩɥɹɪɚ. ɇɚɩɪɢɦɟɪ, ɟɫɥɢ ɜ ɛɥɨɤɟ ɨɩɢɫɚɧɢɹ ɫɨɫɬɚɜɚ ɫɨɞɟɪɠɢɬɫɹ 10 ɷɤɡɟɦɩɥɹɪɨɜ ɞɜɨɢɱɧɨɝɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ ɧɚ 15 ɢɧɬɟɪɩɪɟɬɢɪɭɟɦɵɯ ɫɢɝɧɚɥɨɜ – MyInt : BinaryCoded(15)[10], ɬɨ ɩɨɫɥɟ ɤɥɸɱɟɜɨɝɨ ɫɥɨɜɚ SetParameters ɞɨɥɠɧɨ ɛɵɬɶ ɬɨɥɶɤɨ ɨɞɧɨ ɜɵɪɚɠɟɧɢɟ: MyInt[I:1..10] SetParameters 0.01*I ȼ ɞɚɧɧɨɦ ɩɪɢɦɟɪɟ ɩɟɪɜɵɣ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɛɭɞɟɬ ɢɦɟɬɶ ɭɪɨɜɟɧɶ ɧɚɞɟɠɧɨɫɬɢ ɪɚɜɧɵɣ 0.01, ɜɬɨɪɨɣ – 0.02 ɢ ɬ.ɞ. ȼ ɧɟɨɛɹɡɚɬɟɥɶɧɨɦ ɪɚɡɞɟɥɟ ɨɩɢɫɚɧɢɟ ɫɢɝɧɚɥɨɜ ɭɤɚɡɵɜɚɟɬɫɹ ɱɢɫɥɨ ɫɢɝɧɚɥɨɜ, ɢɧɬɟɪɩɪɟɬɢɪɭɟɦɵɯ ɢɧɬɟɪɩɪɟɬɚɬɨɪɨɦ. ȿɫɥɢ ɷɬɨɬ ɪɚɡɞɟɥ ɨɩɭɳɟɧ, ɬɨ ɩɨɥɚɝɚɟɬɫɹ, ɱɬɨ ɱɢɫɥɨ ɢɧɬɟɪɩɪɟɬɢɪɭɟɦɵɯ ɢɧɬɟɪɩɪɟɬɚɬɨɪɨɦ ɫɢɝɧɚɥɨɜ ɪɚɜɧɨ ɫɭɦɦɟ ɫɢɝɧɚɥɨɜ, ɢɧɬɟɪɩɪɟɬɢɪɭɟɦɵɯ ɜɫɟɦɢ ɱɚɫɬɧɵɦɢ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚɦɢ. ȼ ɤɨɧɫɬɚɧɬɧɨɦ ɜɵɪɚɠɟɧɢɢ ɦɨɠɟɬ ɜɵɡɵɜɚɬɶɫɹ ɮɭɧɤɰɢɹ NumberOf, ɚɪɝɭɦɟɧɬɨɦ ɤɨɬɨɪɨɣ ɹɜɥɹɟɬɫɹ ɢɦɹ ɱɚɫɬɧɨɝɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ (ɢɥɢ ɟɝɨ ɩɫɟɜɞɨɧɢɦ) ɫ ɭɤɚɡɚɧɢɟɦ ɮɚɤɬɢɱɟɫɤɢɯ ɚɪɝɭɦɟɧɬɨɜ. ȼ ɧɟɨɛɹɡɚɬɟɥɶɧɨɦ ɪɚɡɞɟɥɟ ɨɩɢɫɚɧɢɹ ɪɚɫɩɪɟɞɟɥɟɧɢɹ ɫɢɝɧɚɥɨɜ ɞɥɹ ɤɚɠɞɨɝɨ ɱɚɫɬɧɨɝɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ ɭɤɚɡɵɜɚɟɬɫɹ, ɤɚɤɢɟ ɫɢɝɧɚɥɵ ɢɡ ɨɛɳɟɝɨ ɢɧɬɟɪɩɪɟɬɢɪɭɟɦɨɝɨ ɦɚɫɫɢɜɚ ɩɟɪɟɞɚɸɬɫɹ ɟɦɭ ɞɥɹ ɢɧɬɟɪɩɪɟɬɚɰɢɢ. ȿɫɥɢ ɷɬɨɬ ɪɚɡɞɟɥ ɨɬɫɭɬɫɬɜɭɟɬ, ɬɨ ɫɱɢɬɚɟɬɫɹ, ɱɬɨ ɤɚɠɞɵɣ ɫɥɟɞɭɸɳɢɣ ɱɚɫɬɧɵɣ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɩɨɥɭɱɚɟɬ ɫɥɟɞɭɸɳɢɣ ɮɪɚɝɦɟɧɬ ɨɛɳɟɝɨ ɜɟɤɬɨɪɚ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ. ȼ ɩɪɢɦɟɪɟ 1 ɞɚɧɧɵɣ ɪɚɡɞɟɥ ɨɩɢɫɵɜɚɟɬ ɪɚɫɩɪɟɞɟɥɟɧɢɟ ɫɢɝɧɚɥɨɜ ɩɨ ɭɦɨɥɱɚɧɢɸ. ȼ ɧɟɨɛɹɡɚɬɟɥɶɧɨɦ ɪɚɡɞɟɥɟ ɨɩɢɫɚɧɢɹ ɪɚɫɩɪɟɞɟɥɟɧɢɹ ɨɬɜɟɬɨɜ ɞɥɹ ɤɚɠɞɨɝɨ ɱɚɫɬɧɨɝɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ ɭɤɚɡɵɜɚɟɬɫɹ, ɤɚɤɨɣ ɷɥɟɦɟɧɬ ɦɚɫɫɢɜɚ ɨɬɜɟɬɨɜ ɨɧ ɜɵɱɢɫɥɹɟɬ. ȿɫɥɢ ɷɬɨɬ ɪɚɡɞɟɥ ɨɩɭɳɟɧ, ɬɨ ɫɱɢɬɚɟɬɫɹ, ɱɬɨ ɩɟɪɜɵɣ ɱɚɫɬɧɵɣ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɜɵɱɢɫɥɹɟɬ ɩɟɪɜɵɣ ɷɥɟɦɟɧɬ ɦɚɫɫɢɜɚ ɨɬɜɟɬɨɜ, ɜɬɨɪɨɣ – ɜɬɨɪɨɣ ɷɥɟɦɟɧɬ ɢ ɬ.ɞ. Ɇɚɫɫɢɜ ɭɪɨɜɧɟɣ ɧɚɞɟɠɧɨɫɬɟɣ ɜɫɟɝɞɚ ɩɚɪɚɥɥɟɥɟɧ ɦɚɫɫɢɜɭ ɨɬɜɟɬɨɜ. ȼ ɩɪɢɦɟɪɟ 1 ɞɚɧɧɵɣ ɪɚɡɞɟɥ ɨɩɢɫɵɜɚɟɬ ɪɚɫɩɪɟɞɟɥɟɧɢɟ ɨɬɜɟɬɨɜ ɩɨ ɭɦɨɥɱɚɧɢɸ. Ʉɪɨɦɟ ɬɨɝɨ, ɜ ɥɸɛɨɦ ɦɟɫɬɟ ɨɩɢɫɚɧɢɹ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ ɦɨɝɭɬ ɜɫɬɪɟɱɚɬɶɫɹ ɤɨɦɦɟɧɬɚɪɢɢ, ɡɚɤɥɸɱɟɧɧɵɟ ɜ ɮɢɝɭɪɧɵɟ ɫɤɨɛɤɢ.
6.7.3 ɉɪɢɦɟɪ ɨɩɢɫɚɧɢɹ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ ȼ ɷɬɨɦ ɪɚɡɞɟɥɟ ɩɪɢɜɟɞɟɧɵ ɞɜɚ ɩɪɢɦɟɪɚ ɨɩɢɫɚɧɢɹ ɨɞɧɨɝɨ ɢ ɬɨɝɨ ɠɟ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ ɫɥɟɞɭɸɳɟɝɨ ɫɨɫɬɚɜɚ: ɩɟɪɜɵɣ ɫɢɝɧɚɥ ɢɧɬɟɪɩɪɟɬɢɪɭɟɬɫɹ ɤɚɤ ɬɟɦɩɟɪɚɬɭɪɚ ɩɭɬɟɦ ɭɦɧɨɠɟɧɢɹ ɧɚ 10 ɢ ɞɨɛɚɜɥɟɧɢɹ 273; ɫɥɟɞɭɸɳɢɟ ɞɜɚ ɫɢɝɧɚɥɚ ɢɧɬɟɪɩɪɟɬɢɪɭɸɬɫɹ ɤɚɤ ɧɚɥɢɱɢɟ ɨɛɥɚɱɧɨɫɬɢ, ɢɫɩɨɥɶɡɭɹ ɡɧɚɤɨɜɵɣ ɢɧɬɟɪɩɪɟɬɚɬɨɪ; ɫɥɟɞɭɸɳɢɟ ɬɪɢ ɫɢɝɧɚɥɚ ɢɧɬɟɪɩɪɟɬɢɪɭɸɬɫɹ ɤɚɤ ɧɚɩɪɚɜɥɟɧɢɟ ɜɟɬɪɚ, ɢɫɩɨɥɶɡɭɹ ɞɜɨɢɱɧɵɣ ɢɧɬɟɪɩɪɟɬɚɬɨɪ (ɜɨɫɟɦɶ ɪɭɦɛɨɜ); ɩɨɫɥɟɞɧɢɟ ɬɪɢ ɫɢɝɧɚɥɚ ɢɧɬɟɪɩɪɟɬɢɪɭɸɬɫɹ ɦɚɤɫɢɦɚɥɶɧɵɦ ɢɧɬɟɪɩɪɟɬɚɬɨɪɨɦ ɤɚɤ ɫɢɥɚ ɨɫɚɞɤɨɜ (ɛɟɡ ɨɫɚɞɤɨɜ, ɫɥɚɛɵɟ ɨɫɚɞɤɢ, ɫɢɥɶɧɵɟ ɨɫɚɞɤɢ). ȼ ɩɟɪɜɨɦ ɩɪɢɦɟɪɟ ɩɪɢɜɟɞɟɧɨ ɨɩɢɫɚɧɢɟ ɞɭɛɥɢɤɚɬɨɜ ɜɫɟɯ ɫɬɚɧɞɚɪɬɧɵɯ ɢɧɬɟɪɩɪɟɬɚɬɨɪɨɜ. ȼɨ ɜɬɨɪɨɦ – ɢɫɩɨɥɶɡɨɜɚɧɵ ɫɬɚɧɞɚɪɬɧɵɟ ɢɧɬɟɪɩɪɟɬɚɬɨɪɵ. ɉɪɢɦɟɪ 1. Interpretator Meteorology Inter Empty1 () {ɂɧɬɟɪɩɪɟɬɚɬɨɪ ɨɫɭɳɟɫɬɜɥɹɸɳɢɣ ɦɚɫɲɬɚɛɢɪɨɜɚɧɢɟ ɢ ɫɞɜɢɝ ɫɢɝɧɚɥɚ} Static Real B Name "Ɇɚɫɲɬɚɛɧɵɣ ɦɧɨɠɢɬɟɥɶ";
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Real C Name "ɋɞɜɢɝ ɧɚɱɚɥɚ ɨɬɫɱɟɬɚ"; Begin Answer = Signals[1] * B + C; Reliability = 0 End Inter Binary1 : ( N : Long ) { Ʉɨɞɢɪɨɜɚɧɢɟ ɧɨɦɟɪɨɦ ɤɚɧɚɥɚ. Ɂɧɚɤɨɜɵɣ ɢɧɬɟɪɩɪɟɬɚɬɨɪ} Static Real E Name "ɍɪɨɜɟɧɶ ɧɚɞɟɠɧɨɫɬɢ"; Var Long A, B, I; Real Dist; Begin Dist = E; B = 0; {ɑɢɫɥɨ ɟɞɢɧɢɰ} A = 0; {ɇɨɦɟɪ ɟɞɢɧɢɰɵ} For I = 1 To N Do Begin If Abs(Signals[I]) < Dist Then Dist = Abs(Signals[I]); If Signals[I] > 0 Then Begin A = I; B = B + 1; End; End; If B <> 1 Then Answer = 0 Else Answer = A Reliability = Abs(Dist / E) End Inter Major1 : ( N : Long) { Ʉɨɞɢɪɨɜɚɧɢɟ ɧɨɦɟɪɨɦ ɤɚɧɚɥɚ. Ɇɚɤɫɢɦɚɥɶɧɵɣ ɢɧɬɟɪɩɪɟɬɚɬɨɪ.} Static Real E Name "ɍɪɨɜɟɧɶ ɧɚɞɟɠɧɨɫɬɢ"; Var Real A, B; Long I, J; Begin A = -1.E+30; {Ɇɚɤɫɢɦɚɥɶɧɵɣ ɫɢɝɧɚɥ} B = -1.E+30; {ȼɬɨɪɨɣ ɩɨ ɜɟɥɢɱɢɧɟ ɫɢɝɧɚɥ} J = 0; {ɇɨɦɟɪ ɦɚɤɫɢɦɚɥɶɧɨɝɨ ɫɢɝɧɚɥɚ} For I = 1 To N Do Begin If Signals[I] > A Then Begin B = A; A = Signals[I]; J=I; End Else If Signals[I] > B Then B = Signals[I]; End; Answer = J; If A - B > E Then Reliability = 1 Else Reliability = (A - B) / E; End Inter BynaryCoded1 : ( N : Long ) Static Real E Name "ɍɪɨɜɟɧɶ ɧɚɞɟɠɧɨɫɬɢ"; Var Long A, I; Real Dist; Begin Dist = E; A = 0; {Ɉɬɜɟɬ} For I = 1 To N Do Begin If Abs(Signals[I]) < Dist Then Dist = Abs(Signals[I]); A = A * 2; If Signals[I] > 0 Then A = A + 1; End; Answer = A; Reliability = Abs(Dist / E) End Contents Temp : Empty1, Cloud : Binary1(2), Wind : BynaryCoded1(3), Rain : Major1(3);
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Temp SetParameters 10, 273; Cloud SetParameters 0.1; Wind SetParameters 0.2; Rain SetParameters 0.15 Signals NumberOf(Signals,Temp) + NumberOf(Signals, Cloud) + NumberOf(Signals, Wind) + NumberOf(Signals, Rain) Connections Temp.Signals <=> Signals[1]; Cloud.Signals[1..2] <=> Signals[2; 3]; Wind.Signals[1..3] <=> Signals[4..6]; Rain.Signals[1..3] <=> Signals[7..9] Temp.Answer <=> Answer[1]; Cloud.Answer[1..2] <=> Answer[2]; Wind.Answer[1..3] <=> Answer[3]; Rain.Answer[1..3] <=> Answer[4] End Interpretator ɉɪɢɦɟɪ 2. Interpretator Meteorology Contents Temp : Empty, Cloud : Binary(2), Wind : BynaryCoded(3), Rain : Major(3); Temp SetParameters 10, 273; Cloud SetParameters 0.1; Wind SetParameters 0.2; Rain SetParameters 0.15 End Interpretator
6.8 ɋɬɚɧɞɚɪɬ ɜɬɨɪɨɝɨ ɭɪɨɜɧɹ ɤɨɦɩɨɧɟɧɬɚ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɨɬɜɟɬɚ Ɂɚɩɪɨɫɵ ɤ ɤɨɦɩɨɧɟɧɬɭ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɨɬɜɟɬɚ ɦɨɠɧɨ ɪɚɡɛɢɬɶ ɧɚ ɩɹɬɶ ɝɪɭɩɩ: ɂɧɬɟɪɩɪɟɬɚɰɢɹ. ɂɡɦɟɧɟɧɢɟ ɩɚɪɚɦɟɬɪɨɜ. Ɋɚɛɨɬɚ ɫɨ ɫɬɪɭɤɬɭɪɨɣ. ɂɧɢɰɢɚɰɢɹ ɪɟɞɚɤɬɨɪɚ ɢ ɤɨɧɫɬɪɭɤɬɨɪɚ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ ɨɬɜɟɬɚ. Ɉɛɪɚɛɨɬɤɚ ɨɲɢɛɨɤ. ɉɨɫɤɨɥɶɤɭ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪ ɦɨɠɟɬ ɪɚɛɨɬɚɬɶ ɨɞɧɨɜɪɟɦɟɧɧɨ ɫ ɧɟɫɤɨɥɶɤɢɦɢ ɫɟɬɹɦɢ, ɬɨ ɢ ɤɨɦɩɨɧɟɧɬ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɨɬɜɟɬɚ ɞɨɥɠɟɧ ɢɦɟɬɶ ɜɨɡɦɨɠɧɨɫɬɶ ɨɞɧɨɜɪɟɦɟɧɧɨɣ ɪɚɛɨɬɵ ɫ ɧɟɫɤɨɥɶɤɢɦɢ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚɦɢ. ɉɨɷɬɨɦɭ ɛɨɥɶɲɢɧɫɬɜɨ ɡɚɩɪɨɫɨɜ ɤ ɢɧɬɟɪɩɪɟɬɚɬɨɪɭ ɫɨɞɟɪɠɚɬ ɹɜɧɨɟ ɭɤɚɡɚɧɢɟ ɢɦɟɧɢ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ ɨɬɜɟɬɚ. ɇɢɠɟ ɩɪɢɜɟɞɟɧɨ ɨɩɢɫɚɧɢɟ ɜɫɟɯ ɡɚɩɪɨɫɨɜ ɤ ɤɨɦɩɨɧɟɧɬɭ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɨɬɜɟɬɚ. Ʉɚɠɞɵɣ ɡɚɩɪɨɫ ɹɜɥɹɟɬɫɹ ɥɨɝɢɱɟɫɤɨɣ ɮɭɧɤɰɢɟɣ, ɜɨɡɜɪɚɳɚɸɳɟɣ ɡɧɚɱɟɧɢɟ ɢɫɬɢɧɚ, ɟɫɥɢ ɡɚɩɪɨɫ ɜɵɩɨɥɧɟɧ ɭɫɩɟɲɧɨ, ɢ ɥɨɠɶ – ɩɪɢ ɨɲɢɛɨɱɧɨɦ ɡɚɜɟɪɲɟɧɢɢ ɢɫɩɨɥɧɟɧɢɹ ɡɚɩɪɨɫɚ. ȼ ɡɚɩɪɨɫɚɯ ɜɬɨɪɨɣ ɢ ɬɪɟɬɶɟɣ ɝɪɭɩɩɵ ɩɪɢ ɨɛɪɚɳɟɧɢɢ ɤ ɱɚɫɬɧɵɦ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚɦ ɢɫɩɨɥɶɡɭɟɬɫɹ ɫɥɟɞɭɸɳɢɣ ɫɢɧɬɚɤɫɢɫ: <ɉɨɥɧɨɟ ɢɦɹ ɱɚɫɬɧɨɝɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ> ::= <ɂɦɹ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ>.<ɉɫɟɜɞɨɧɢɦ ɱɚɫɬɧɨɝɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ> [[<ɇɨɦɟɪ ɷɤɡɟɦɩɥɹɪɚ>]] 1. 2. 3. 4. 5.
Ɍɚɛɥɢɰɚ 3. Ɂɧɚɱɟɧɢɹ ɩɪɟɞɨɩɪɟɞɟɥɟɧɧɵɯ ɤɨɧɫɬɚɧɬ ɤɨɦɩɨɧɟɧɬɚ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɨɬɜɟɬɚ ɢ ɨɰɟɧɤɚ ɇɚɡɜɚɧɢɟȼɟɥɢɱɢɧɚɁɧɚɱɟɧɢɟ Empty 0 ɂɧɬɟɪɩɪɟɬɢɪɭɟɬ ɨɞɢɧ ɫɢɝɧɚɥ ɤɚɤ ɞɟɣɫɬɜɢɬɟɥɶɧɨɟ ɱɢɫɥɨ. Binary 1 Ʉɨɞɢɪɨɜɚɧɢɟ ɧɨɦɟɪɨɦ ɤɚɧɚɥɚ. Ɂɧɚɤɨɜɵɣ ɢɧɬɟɪɩɪɟɬɚɬɨɪ Major 2 Ʉɨɞɢɪɨɜɚɧɢɟ ɧɨɦɟɪɨɦ ɤɚɧɚɥɚ. Ɇɚɤɫɢɦɚɥɶɧɵɣ ɢɧɬɟɪɩɪɟɬɚɬɨɪ. BynaryCoded 3 Ⱦɜɨɢɱɧɵɣ ɢɧɬɟɪɩɪɟɬɚɬɨɪ. UserType -1 ɂɧɬɟɪɩɪɟɬɚɬɨɪ, ɨɩɪɟɞɟɥɟɧɧɵɣ ɩɨɥɶɡɨɜɚɬɟɥɟɦ.
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ɉɪɢ ɜɵɡɨɜɟ ɪɹɞɚ ɡɚɩɪɨɫɨɜ ɢɫɩɨɥɶɡɭɸɬɫɹ ɩɪɟɞɨɩɪɟɞɟɥɟɧɧɵɟ ɤɨɧɫɬɚɧɬɵ. ɂɯ ɡɧɚɱɟɧɢɹ ɩɪɢɜɟɞɟɧɵ ɜ ɬɚɛɥ. 3.
6.8.1 Ɂɚɩɪɨɫ ɧɚ ɢɧɬɟɪɩɪɟɬɚɰɢɸ ȿɞɢɧɫɬɜɟɧɧɵɣ ɡɚɩɪɨɫ ɩɟɪɜɨɣ ɝɪɭɩɩɵ ɜɵɩɨɥɧɹɟɬ ɨɫɧɨɜɧɭɸ ɮɭɧɤɰɢɸ ɤɨɦɩɨɧɟɧɬɚ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɨɬɜɟɬɚ – ɢɧɬɟɪɩɪɟɬɢɪɭɟɬ ɦɚɫɫɢɜ ɫɢɝɧɚɥɨɜ.
6.8.1.1 ɂɧɬɟɪɩɪɟɬɢɪɨɜɚɬɶ ɦɚɫɫɢɜ ɫɢɝɧɚɥɨɜ (Interpretate) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function Interpretate( IntName : PString; Signals : PRealArray; Var Reliability, Answers : PRealArray ) : Logic; C: Logic Interpretate(PString IntName, PRealArray Signals, PRealArray* Reliability, PRealArray* Answers) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɚ: IntName – ɭɤɚɡɚɬɟɥɶ ɧɚ ɫɬɪɨɤɭ ɫɢɦɜɨɥɨɜ, ɫɨɞɟɪɠɚɳɭɸ ɢɦɹ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ ɨɬɜɟɬɚ. Signals – ɦɚɫɫɢɜ ɢɧɬɟɪɩɪɟɬɢɪɭɟɦɵɯ ɫɢɝɧɚɥɨɜ. Answers – ɦɚɫɫɢɜ ɨɬɜɟɬɨɜ. Reliability – ɦɚɫɫɢɜ ɤɨɷɮɮɢɰɢɟɧɬɨɜ ɭɜɟɪɟɧɧɨɫɬɢ ɜ ɨɬɜɟɬɟ. ɇɚɡɧɚɱɟɧɢɟ – ɢɧɬɟɪɩɪɟɬɢɪɭɟɬ ɦɚɫɫɢɜ ɫɢɝɧɚɥɨɜ Signals, ɢɫɩɨɥɶɡɭɹ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɨɬɜɟɬɚ, ɭɤɚɡɚɧɧɵɣ ɜ ɩɚɪɚɦɟɬɪɟ IntName. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. 1. ȿɫɥɢ Error <> 0, ɬɨ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 2. ȿɫɥɢ ɜ ɤɚɱɟɫɬɜɟ ɚɪɝɭɦɟɧɬɚ IntName ɞɚɧ ɩɭɫɬɨɣ ɭɤɚɡɚɬɟɥɶ, ɢɥɢ ɭɤɚɡɚɬɟɥɶ ɧɚ ɩɭɫɬɭɸ ɫɬɪɨɤɭ, ɬɨ ɢɫɩɨɥɧɹɸɳɢɦ ɡɚɩɪɨɫ ɨɛɴɟɤɬɨɦ ɹɜɥɹɟɬɫɹ ɩɟɪɜɵɣ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɨɬɜɟɬɚ ɜ ɫɩɢɫɤɟ ɢɧɬɟɪɩɪɟɬɚɬɨɪɨɜ ɤɨɦɩɨɧɟɧɬɚ ɢɧɬɟɪɩɪɟɬɚɬɨɪ. 3. ȿɫɥɢ ɫɩɢɫɨɤ ɢɧɬɟɪɩɪɟɬɚɬɨɪɨɜ ɤɨɦɩɨɧɟɧɬɚ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɩɭɫɬ ɢɥɢ ɢɦɹ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ ɨɬɜɟɬɚ, ɩɟɪɟɞɚɧɧɨɟ ɜ ɚɪɝɭɦɟɧɬɟ IntName ɜ ɷɬɨɦ ɫɩɢɫɤɟ ɧɟ ɧɚɣɞɟɧɨ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 501 – ɧɟɜɟɪɧɨɟ ɢɦɹ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ ɨɬɜɟɬɚ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 4. ɉɪɨɢɡɜɨɞɢɬɫɹ ɢɧɬɟɪɩɪɟɬɚɰɢɹ ɨɬɜɟɬɚ ɢɧɬɟɪɩɪɟɬɚɬɨɪɨɦ ɨɬɜɟɬɚ, ɢɦɹ ɤɨɬɨɪɨɝɨ ɛɵɥɨ ɭɤɚɡɚɧɨ ɜ ɚɪɝɭɦɟɧɬɟ IntName. 5. ȿɫɥɢ ɜɨ ɜɪɟɦɹ ɜɵɩɨɥɧɟɧɢɹ ɡɚɩɪɨɫɚ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ, ɬɨ ɝɟɧɟɪɢɪɭɟɬɫɹ ɜɧɭɬɪɟɧɧɹɹ ɨɲɢɛɤɚ 504 ɨɲɢɛɤɚ ɢɧɬɟɪɩɪɟɬɚɰɢɢ. ɍɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ. ȼɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. ȼ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɭɫɩɟɲɧɨ ɡɚɜɟɪɲɚɟɬɫɹ.
6.8.2 Ɉɫɬɚɥɶɧɵɟ ɡɚɩɪɨɫɵ ɇɢɠɟ ɩɪɢɜɟɞɟɧ ɫɩɢɫɨɤ ɡɚɩɪɨɫɨɜ, ɢɫɩɨɥɧɟɧɢɟ ɤɨɬɨɪɵɯ ɨɩɢɫɚɧɨ ɜ ɝɥɚɜɟ "Ɉɛɳɢɣ ɫɬɚɧɞɚɪɬ": aiSetCurrent – ɋɞɟɥɚɬɶ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɨɬɜɟɬɚ ɬɟɤɭɳɢɦ aiAdd – Ⱦɨɛɚɜɥɟɧɢɟ ɧɨɜɨɝɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ ɨɬɜɟɬɚ aiDelete – ɍɞɚɥɟɧɢɟ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ ɨɬɜɟɬɚ aiWrite – Ɂɚɩɢɫɶ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ ɨɬɜɟɬɚ aiGetStructNames – ȼɟɪɧɭɬɶ ɢɦɟɧɚ ɱɚɫɬɧɵɯ ɢɧɬɟɪɩɪɟɬɚɬɨɪɨɜ aiGetType – ȼɟɪɧɭɬɶ ɬɢɩ ɱɚɫɬɧɨɝɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ aiGetData – ɉɨɥɭɱɢɬɶ ɩɚɪɚɦɟɬɪɵ ɱɚɫɬɧɨɝɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ aiGetName – ɉɨɥɭɱɢɬɶ ɢɦɟɧɚ ɩɚɪɚɦɟɬɪɨɜ ɱɚɫɬɧɨɝɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ aiSetData – ɍɫɬɚɧɨɜɢɬɶ ɩɚɪɚɦɟɬɪɵ ɱɚɫɬɧɨɝɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ aiEdit – Ɋɟɞɚɤɬɢɪɨɜɚɬɶ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɨɬɜɟɬɚ OnError – ɍɫɬɚɧɨɜɢɬɶ ɨɛɪɚɛɨɬɱɢɤ ɨɲɢɛɨɤ GetError – Ⱦɚɬɶ ɧɨɦɟɪ ɨɲɢɛɤɢ FreeMemory – Ɉɫɜɨɛɨɞɢɬɶ ɩɚɦɹɬɶ ȼ ɡɚɩɪɨɫɟ aiGetType ɜ ɩɟɪɟɦɟɧɧɨɣ TypeId ɜɨɡɜɪɚɳɚɟɬɫɹ ɡɧɚɱɟɧɢɟ ɨɞɧɨɣ ɢɡ ɩɪɟɞɨɩɪɟɞɟɥɟɧɧɵɯ ɤɨɧɫɬɚɧɬ, ɩɟɪɟɱɢɫɥɟɧɧɵɯ ɜ ɬɚɛɥ. 3. ɉɪɢ ɢɫɩɨɥɧɟɧɢɢ ɡɚɩɪɨɫɚ aiSetData ɝɟɧɟɪɢɪɭɟɬɫɹ ɡɚɩɪɨɫ SetEstIntParameters ɤ ɤɨɦɩɨɧɟɧɬɟ ɨɰɟɧɤɚ. Ⱥɪɝɭɦɟɧɬɵ ɝɟɧɟɪɢɪɭɟɦɨɝɨ ɡɚɩɪɨɫɚ ɫɨɜɩɚɞɚɸɬ ɫ ɚɪɝɭɦɟɧɬɚɦɢ ɢɫɩɨɥɧɹɟɦɨɝɨ ɡɚɩɪɨɫɚ
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6.8.3 Ɉɲɢɛɤɢ ɤɨɦɩɨɧɟɧɬɚ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɨɬɜɟɬɚ ȼ ɬɚɛɥ. 4 ɩɪɢɜɟɞɟɧ ɩɨɥɧɵɣ ɫɩɢɫɨɤ ɨɲɢɛɨɤ, ɤɨɬɨɪɵɟ ɦɨɝɭɬ ɜɨɡɧɢɤɚɬɶ ɩɪɢ ɜɵɩɨɥɧɟɧɢɢ ɡɚɩɪɨɫɨɜ ɤɨɦɩɨɧɟɧɬɨɦ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɨɬɜɟɬɚ, ɢ ɞɟɣɫɬɜɢɹ ɫɬɚɧɞɚɪɬɧɨɝɨ ɨɛɪɚɛɨɬɱɢɤɚ ɨɲɢɛɨɤ. Ɍɚɛɥɢɰɚ 4. Ɉɲɢɛɤɢ ɤɨɦɩɨɧɟɧɬɚ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɨɬɜɟɬɚ ɢ ɞɟɣɫɬɜɢɹ ɫɬɚɧɞɚɪɬɧɨɝɨ ɨɛɪɚɛɨɬɱɢɤɚ ɨɲɢɛɨɤ. ʋɇɚɡɜɚɧɢɟ ɨɲɢɛɤɢɋɬɚɧɞɚɪɬɧɚɹ ɨɛɪɚɛɨɬɤɚ 501 ɇɟɜɟɪɧɨɟ ɢɦɹ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ ɨɬɜɟɬɚɁɚɧɟɫɟɧɢɟ ɧɨɦɟɪɚ ɜ Error 502 Ɉɲɢɛɤɚ ɫɱɢɬɵɜɚɧɢɹ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ ɨɬɜɟɬɚɁɚɧɟɫɟɧɢɟ ɧɨɦɟɪɚ ɜ Error 503 Ɉɲɢɛɤɚ ɫɨɯɪɚɧɟɧɢɹ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ ɨɬɜɟɬɚɁɚɧɟɫɟɧɢɟ ɧɨɦɟɪɚ ɜ Error 504 Ɉɲɢɛɤɚ ɢɧɬɟɪɩɪɟɬɚɰɢɢɁɚɧɟɫɟɧɢɟ ɧɨɦɟɪɚ ɜ Error
6.9 ɋɬɚɧɞɚɪɬ ɩɟɪɜɨɝɨ ɭɪɨɜɧɹ ɤɨɦɩɨɧɟɧɬɚ ɨɰɟɧɤɚ Ⱦɚɧɧɵɣ ɪɚɡɞɟɥ ɩɨɫɜɹɳɟɧ ɨɩɢɫɚɧɢɸ ɫɬɚɧɞɚɪɬɚ ɯɪɚɧɟɧɢɹ ɧɚ ɞɢɫɤɟ ɨɩɢɫɚɧɢɹ ɤɨɦɩɨɧɟɧɬɚ ɨɰɟɧɤɚ. ɉɨɫɬɪɨɟɧɢɟ ɨɰɟɧɤɢ ɩɪɨɢɫɯɨɞɢɬ ɜ ɪɟɞɚɤɬɨɪɟ ɨɰɟɧɨɤ. ȼ ɞɚɧɧɨɦ ɫɬɚɧɞɚɪɬɟ ɩɪɟɞɥɚɝɚɟɬɫɹ ɨɝɪɚɧɢɱɢɬɶɫɹ ɪɚɫɫɦɨɬɪɟɧɢɟɦ ɬɨɥɶɤɨ ɥɨɤɚɥɶɧɵɯ ɨɰɟɧɨɤ, ɩɨɫɤɨɥɶɤɭ ɢɫɩɨɥɶɡɨɜɚɧɢɟ ɧɟɥɨɤɚɥɶɧɵɯ (ɝɥɨɛɚɥɶɧɵɯ) ɨɰɟɧɨɤ ɫɢɥɶɧɨ ɭɫɥɨɠɧɹɟɬ ɤɨɦɩɨɧɟɧɬ ɨɰɟɧɤɚ, ɚ ɨɛɥɚɫɬɶ ɩɪɢɦɟɧɟɧɢɹ ɧɟɥɨɤɚɥɶɧɵɯ ɨɰɟɧɨɤ ɭɡɤɚ ɩɨ ɫɪɚɜɧɟɧɢɸ ɫ ɥɨɤɚɥɶɧɵɦɢ ɨɰɟɧɤɚɦɢ. Ɉɰɟɧɤɚ ɜɫɟɝɞɚ ɹɜɥɹɟɬɫɹ ɫɨɫɬɚɜɧɨɣ, ɞɚɠɟ ɟɫɥɢ ɨɬɜɟɬɨɦ ɫɟɬɢ ɹɜɥɹɟɬɫɹ ɨɞɧɚ ɜɟɥɢɱɢɧɚ. ȼ ɫɨɫɬɚɜ ɷɬɨɝɨ ɨɛɴɟɤɬɚ ɜɯɨɞɹɬ ɱɚɫɬɧɵɟ ɨɰɟɧɤɢ. Ʉɪɨɦɟ ɬɨɝɨ, ɜ ɨɩɢɫɚɧɢɟ ɨɰɟɧɤɢ ɜɤɥɸɱɚɸɬɫɹ ɩɪɚɜɢɥɚ ɪɚɫɩɪɟɞɟɥɟɧɢɹ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɫɟɬɢ ɦɟɠɞɭ ɱɚɫɬɧɵɦɢ ɨɰɟɧɤɚɦɢ ɢ ɪɚɫɩɨɥɨɠɟɧɢɹ ɨɰɟɧɨɤ, ɜɵɱɢɫɥɹɟɦɵɯ ɱɚɫɬɧɵɦɢ ɨɰɟɧɤɚɦɢ, ɜ ɟɞɢɧɨɦ ɦɚɫɫɢɜɟ ɨɰɟɧɨɤ. Ʉɪɨɦɟ ɬɨɝɨ, ɪɚɡɥɢɱɧɵɟ ɱɚɫɬɧɵɟ ɨɰɟɧɤɢ ɦɨɝɭɬ ɢɦɟɬɶ ɪɚɡɧɭɸ ɡɧɚɱɢɦɨɫɬɶ. ȼ ɷɬɨɦ ɫɥɭɱɚɟ ɨɛɳɚɹ ɨɰɟɧɤɚ ɨɩɪɟɞɟɥɹɟɬɫɹ ɤɚɤ ɫɭɦɦɚ ɱɚɫɬɧɵɯ ɨɰɟɧɨɤ ɫ ɜɟɫɚɦɢ, ɡɚɞɚɸɳɢɦɢ ɡɧɚɱɢɦɨɫɬɶ. Ɍɚɤɢɦ ɨɛɪɚɡɨɦ, ɨɰɟɧɤɚ ɩɪɢ ɜɵɩɨɥɧɟɧɢɢ ɡɚɩɪɨɫɚ ɧɚ ɨɰɟɧɢɜɚɧɢɟ ɦɚɫɫɢɜɚ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɫɟɬɢ ɩɨɥɭɱɚɟɬ ɧɚ ɜɯɨɞɟ ɦɚɫɫɢɜ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɫɟɬɢ, ɦɚɫɫɢɜ ɩɪɚɜɢɥɶɧɵɯ ɨɬɜɟɬɨɜ ɢ ɦɚɫɫɢɜ ɢɯ ɞɨɫɬɨɜɟɪɧɨɫɬɟɣ, ɚ ɜɨɡɜɪɚɳɚɟɬ ɞɜɚ ɦɚɫɫɢɜɚ – ɦɚɫɫɢɜ ɨɰɟɧɨɤ ɢ ɦɚɫɫɢɜ ɩɪɨɢɡɜɨɞɧɵɯ ɨɰɟɧɤɢ ɩɨ ɜɵɯɨɞɧɵɦ ɫɢɝɧɚɥɚɦ ɫɟɬɢ – ɢ ɜɟɥɢɱɢɧɭ ɫɭɦɦɚɪɧɨɣ ɨɰɟɧɤɢ. ȼɨɡɦɨɠɧɵ ɞɜɚ ɪɟɠɢɦɚ ɨɰɟɧɢɜɚɧɢɹ: ɨɰɟɧɢɜɚɧɢɟ ɛɟɡ ɜɵɱɢɫɥɟɧɢɹ ɦɚɫɫɢɜɚ ɩɪɨɢɡɜɨɞɧɵɯ ɨɰɟɧɤɢ ɩɨ ɜɵɯɨɞɧɵɦ ɫɢɝɧɚɥɚɦ ɫɟɬɢ, ɢ ɨɰɟɧɢɜɚɧɢɟ ɫ ɜɵɱɢɫɥɟɧɢɟɦ ɦɚɫɫɢɜɚ ɩɪɨɢɡɜɨɞɧɵɯ. Ɍɚɛɥɢɰɚ 5 Ʉɥɸɱɟɜɵɟ ɫɥɨɜɚ ɹɡɵɤɚ ɨɩɢɫɚɧɢɹ ɨɰɟɧɨɤ. Ʉɥɸɱɟɜɨɟ ɫɥɨɜɨɄɪɚɬɤɨɟ ɨɩɢɫɚɧɢɟ 1. Answer ɉɪɚɜɢɥɶɧɵɣ ɨɬɜɟɬ. 2. Back Ɇɚɫɫɢɜ ɩɪɨɢɡɜɨɞɧɵɯ ɨɰɟɧɤɢ ɩɨ ɨɰɟɧɢɜɚɟɦɵɦ ɫɢɝɧɚɥɚɦ. 3. Contents ɇɚɱɚɥɨ ɛɥɨɤɚ ɨɩɢɫɚɧɢɹ ɫɨɫɬɚɜɚ ɨɰɟɧɤɢ. 4. Direv ɉɪɢɡɧɚɤ ɧɟɨɛɯɨɞɢɦɨɫɬɢ ɜɵɱɢɫɥɟɧɢɹ ɩɪɨɢɡɜɨɞɧɵɯ. 5. Est Ɂɚɝɨɥɨɜɨɤ ɨɩɢɫɚɧɢɹ ɱɚɫɬɧɨɣ ɨɰɟɧɤɢ. 6. Estim ɉɟɪɟɦɟɧɧɚɹ ɞɟɣɫɬɜɢɬɟɥɶɧɨɝɨ ɬɢɩɚ, ɞɥɹ ɜɨɡɜɪɚɳɟɧɢɹ ɜɵɱɢɫɥɟɧɧɨɣ ɨɰɟɧɤɢ. 7. Estimation Ɂɚɝɨɥɨɜɨɤ ɪɚɡɞɟɥɚ ɮɚɣɥɚ, ɫɨɞɟɪɠɚɳɢɣ ɨɩɢɫɚɧɢɟ ɨɰɟɧɤɢ. 8. Include ɉɪɟɞɲɟɫɬɜɭɟɬ ɢɦɟɧɢ ɮɚɣɥɚ, ɰɟɥɢɤɨɦ ɜɫɬɚɜɥɹɟɦɨɝɨ ɜ ɷɬɨ ɦɟɫɬɨ ɨɩɢɫɚɧɢɹ. 9. Link ɍɤɚɡɵɜɚɟɬ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɨɬɜɟɬɚ, ɫɜɹɡɚɧɧɵɣ ɫ ɨɰɟɧɤɨɣ. 10. NumberOf Ɏɭɧɤɰɢɹ. ȼɨɡɜɪɚɳɚɟɬ ɱɢɫɥɨ ɢɧɬɟɪɩɪɟɬɢɪɭɟɦɵɯ ɱɚɫɬɧɵɦ ɢɧɬɟɪɩɪɟɬɚɬɨɪɨɦ ɫɢɝɧɚɥɨɜ. 11. Reliability Ⱦɨɫɬɨɜɟɪɧɨɫɬɶ ɩɪɚɜɢɥɶɧɨɝɨ ɨɬɜɟɬɚ. 12. Signals ɂɦɹ, ɩɨ ɤɨɬɨɪɨɦɭ ɚɞɪɟɫɭɸɬɫɹ ɢɧɬɟɪɩɪɟɬɢɪɭɟɦɵɟ ɫɢɝɧɚɥɵ; ɧɚɱɚɥɨ ɛɥɨɤɚ ɨɩɢɫɚɧɢɹ ɫɢɝɧɚɥɨɜ. 13. Weight ȼɟɫ ɱɚɫɬɧɨɣ ɨɰɟɧɤɢ. 14. Weights ɇɚɱɚɥɨ ɛɥɨɤɚ ɨɩɢɫɚɧɢɹ ɜɟɫɨɜ ɱɚɫɬɧɵɯ ɨɰɟɧɨɤ. Ʉɚɠɞɚɹ ɱɚɫɬɧɚɹ ɨɰɟɧɤɚ ɩɨɥɭɱɚɟɬ ɧɚ ɜɯɨɞɟ ɫɜɨɣ ɦɚɫɫɢɜ ɫɢɝɧɚɥɨɜ (ɜɨɡɦɨɠɧɨ ɢɡ ɨɞɧɨɝɨ ɷɥɟɦɟɧɬɚ), ɩɪɚɜɢɥɶɧɵɣ ɨɬɜɟɬ ɢ ɟɝɨ ɞɨɫɬɨɜɟɪɧɨɫɬɶ, ɚ ɧɚ ɜɵɯɨɞɟ ɜɵɱɢɫɥɹɟɬ ɨɰɟɧɤɭ ɢ, ɩɪɢ ɧɟɨɛɯɨɞɢɦɨɫɬɢ, ɦɚɫɫɢɜ ɩɪɨɢɡɜɨɞɧɵɯ ɨɰɟɧɤɢ ɩɨ ɜɵɯɨɞɧɵɦ ɫɢɝɧɚɥɚɦ ɫɟɬɢ. ȼ ɬɚɛɥ. 5 ɩɪɢɜɟɞɟɧ ɫɩɢɫɨɤ ɤɥɸɱɟɜɵɯ ɫɥɨɜ ɫɩɟɰɢɮɢɱɟɫɤɢɯ ɞɥɹ ɹɡɵɤɚ ɨɩɢɫɚɧɢɹ ɨɰɟɧɨɤ. ɇɚɢɛɨɥɟɟ ɱɚɫɬɨ ɜɫɬɪɟɱɚɸɳɢɟɫɹ ɱɚɫɬɧɵɟ ɨɰɟɧɤɢ ɨɛɴɹɜɥɟɧɵ ɫɬɚɧɞɚɪɬɧɵɦɢ. Ⱦɥɹ ɫɬɚɧɞɚɪɬɧɵɯ ɨɰɟɧɨɤ ɨɩɢɫɚɧɢɟ ɱɚɫɬɧɵɯ ɨɰɟɧɨɤ ɨɬɫɭɬɫɬɜɭɟɬ. ɋɩɢɫɨɤ ɫɬɚɧɞɚɪɬɧɵɯ ɨɰɟɧɨɤ ɩɪɢɜɟɞɟɧ ɜ ɬɚɛɥ. 6.
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Ɍɚɛɥɢɰɚ 6 ɋɬɚɧɞɚɪɬɧɵɟ ɱɚɫɬɧɵɟ ɨɰɟɧɤɢ. ɇɚɡɜɚɧɢɟɉɚɪɚɦɟɬɪɵȺɪɝɭɦɟɧɬɵɈɩɢɫɚɧɢɟ Empty B – ɦɧɨɠɢɬɟɥɶ Ɉɰɟɧɢɜɚɟɬ ɨɞɢɧ ɫɢɝɧɚɥ Ⱥ, ɜɵɱɢɫɥɹɹ ɪɚɫC – ɫɦɟɳɟɧɢɟ ɫɬɨɹɧɢɟ ɞɨ ɩɪɚɜɢɥɶɧɨɝɨ ɨɬɜɟɬɚ ɫ ɭɱɟɬɨɦ ɧɨɪɦɢɪɨɜɤɢ. Binary E – ɭɪɨɜɟɧɶ ɧɚɞɟɠɧɨɫɬɢ N – ɱɢɫɥɨ ɫɢɝɧɚɥɨɜ. Ʉɨɞɢɪɨɜɚɧɢɟ ɧɨɦɟɪɨɦ ɤɚɧɚɥɚ. ɋɨɨɬɜɟɬɫɬɜɭɟɬ ɡɧɚɤɨɜɨɦɭ ɢɧɬɟɪɩɪɟɬɚɬɨɪɭ. Major E – ɭɪɨɜɟɧɶ ɧɚɞɟɠɧɨɫɬɢ N – ɱɢɫɥɨ ɫɢɝɧɚɥɨɜ. Ʉɨɞɢɪɨɜɚɧɢɟ ɧɨɦɟɪɨɦ ɤɚɧɚɥɚ. ɋɨɨɬɜɟɬɫɬɜɭɟɬ ɦɚɤɫɢɦɚɥɶɧɨɦɭ ɢɧɬɟɪɩɪɟɬɚɬɨɪɭ. BynaryCoded E – ɭɪɨɜɟɧɶ ɧɚɞɟɠɧɨɫɬɢ N – ɱɢɫɥɨ ɫɢɝɧɚɥɨɜ. ɋɨɨɬɜɟɬɫɬɜɭɟɬ ɞɜɨɢɱɧɨɦɭ ɢɧɬɟɪɩɪɟɬɚɬɨɪɭ.
6.9.1 ȻɇɎ ɹɡɵɤɚ ɨɩɢɫɚɧɢɹ ɨɰɟɧɨɤ Ɉɛɨɡɧɚɱɟɧɢɹ, ɩɪɢɧɹɬɵɟ ɜ ɞɚɧɧɨɦ ɪɚɫɲɢɪɟɧɢɢ ȻɇɎ ɢ ɨɩɢɫɚɧɢɟ ɪɹɞɚ ɤɨɧɫɬɪɭɤɰɢɣ ɩɪɢɜɟɞɟɧɵ ɜ ɝɥɚɜɟ «Ɉɛɳɢɣ ɫɬɚɧɞɚɪɬ» ɜ ɪɚɡɞɟɥɟ «Ɉɩɢɫɚɧɢɟ ɹɡɵɤɚ ɨɩɢɫɚɧɢɹ ɤɨɦɩɨɧɟɧɬ». <Ɉɩɢɫɚɧɢɟ ɨɰɟɧɤɢ> ::= <Ɂɚɝɨɥɨɜɨɤ> [<Ɉɩɢɫɚɧɢɟ ɮɭɧɤɰɢɣ>] <Ɉɩɢɫɚɧɢɟ ɱɚɫɬɧɵɯ ɨɰɟɧɨɤ> <Ɉɩɢɫɚɧɢɟ ɫɨɫɬɚɜɚ> [<ɋɜɹɡɵɜɚɧɢɟ ɫ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚɦɢ>] [<ɍɫɬɚɧɨɜɥɟɧɢɟ ɩɚɪɚɦɟɬɪɨɜ>] [<Ɉɩɢɫɚɧɢɟ ɜɟɫɨɜ>] [<Ɉɩɢɫɚɧɢɟ ɫɢɝɧɚɥɨɜ>] [<Ɉɩɢɫɚɧɢɟ ɪɚɫɩɪɟɞɟɥɟɧɢɹ ɫɢɝɧɚɥɨɜ>] [<Ɉɩɢɫɚɧɢɟ ɪɚɫɩɪɟɞɟɥɟɧɢɹ ɨɰɟɧɨɤ>] <Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɨɰɟɧɤɢ> <Ɂɚɝɨɥɨɜɨɤ> ::= Estimation <ɂɦɹ ɨɰɟɧɤɢ> <ɂɦɹ ɨɰɟɧɤɢ>::= <ɂɞɟɧɬɢɮɢɤɚɬɨɪ> <Ɉɩɢɫɚɧɢɟ ɱɚɫɬɧɵɯ ɨɰɟɧɨɤ> ::= <Ɉɩɢɫɚɧɢɟ ɱɚɫɬɧɨɣ ɨɰɟɧɤɢ> [<Ɉɩɢɫɚɧɢɟ ɱɚɫɬɧɵɯ ɨɰɟɧɨɤ>] <Ɉɩɢɫɚɧɢɟ ɱɚɫɬɧɨɣ ɨɰɟɧɤɢ> ::= <Ɂɚɝɨɥɨɜɨɤ ɨɩɢɫɚɧɢɹ ɨɰɟɧɤɢ> [<Ɉɩɢɫɚɧɢɟ ɫɬɚɬɢɱɟɫɤɢɯ ɩɟɪɟɦɟɧɧɵɯ>] [<Ɉɩɢɫɚɧɢɟ ɩɟɪɟɦɟɧɧɵɯ>] <Ɍɟɥɨ ɨɰɟɧɤɢ> <Ɂɚɝɨɥɨɜɨɤ ɨɩɢɫɚɧɢɹ ɨɰɟɧɤɢ> ::= Est <ɂɦɹ ɱɚɫɬɧɨɣ ɨɰɟɧɤɢ> (<ɋɩɢɫɨɤ ɮɨɪɦɚɥɶɧɵɯ ɚɪɝɭɦɟɧɬɨɜ>) <ɂɦɹ ɱɚɫɬɧɨɣ ɨɰɟɧɤɢ> ::= <ɂɞɟɧɬɢɮɢɤɚɬɨɪ> <Ɍɟɥɨ ɨɰɟɧɤɢ> ::= Begin <ɋɨɫɬɚɜɧɨɣ ɨɩɟɪɚɬɨɪ> End <Ɉɩɢɫɚɧɢɟ ɫɨɫɬɚɜɚ> ::= Contents <ɋɩɢɫɨɤ ɢɦɟɧ ɨɰɟɧɨɤ> ; <ɋɩɢɫɨɤ ɢɦɟɧ ɨɰɟɧɨɤ> ::= <ɂɦɹ ɨɰɟɧɤɢ> [,<ɋɩɢɫɨɤ ɢɦɟɧ ɨɰɟɧɨɤ >] <ɂɦɹ ɨɰɟɧɤɢ> ::= <ɉɫɟɜɞɨɧɢɦ>: {<ɂɦɹ ɪɚɧɟɟ ɨɩɢɫɚɧɧɨɣ ɨɰɟɧɤɢ> ½ <ɂɦɹ ɫɬɚɧɞɚɪɬɧɨɣ ɨɰɟɧɤɢ>} [(<ɋɩɢɫɨɤ ɮɚɤɬɢɱɟɫɤɢɯ ɚɪɝɭɦɟɧɬɨɜ>)] [[<ɑɢɫɥɨ ɷɤɡɟɦɩɥɹɪɨɜ>]] <ɉɫɟɜɞɨɧɢɦ> ::= <ɂɞɟɧɬɢɮɢɤɚɬɨɪ> <ɑɢɫɥɨ ɷɤɡɟɦɩɥɹɪɨɜ > ::= <ɐɟɥɨɟ ɱɢɫɥɨ> <ɂɦɹ ɪɚɧɟɟ ɨɩɢɫɚɧɧɨɣ ɨɰɟɧɤɢ> ::= <ɂɞɟɧɬɢɮɢɤɚɬɨɪ> <ɂɦɹ ɫɬɚɧɞɚɪɬɧɨɣ ɨɰɟɧɤɢ> ::= <ɂɞɟɧɬɢɮɢɤɚɬɨɪ> <ɍɫɬɚɧɨɜɥɟɧɢɟ ɩɚɪɚɦɟɬɪɨɜ> ::= <ɍɫɬɚɧɨɜɥɟɧɢɟ ɩɚɪɚɦɟɬɪɨɜ ɑɚɫɬɧɨɣ ɨɰɟɧɤɢ> [;<ɍɫɬɚɧɨɜɥɟɧɢɟ ɩɚɪɚɦɟɬɪɨɜ>] <ɋɜɹɡɵɜɚɧɢɟ ɫ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚɦɢ> ::= <ɉɫɟɜɞɨɧɢɦ> [[<ɇɚɱɚɥɶɧɵɣ ɧɨɦɟɪ> [..<Ʉɨɧɟɱɧɵɣ ɧɨɦɟɪ> [:<ɒɚɝ>] ]]] Link <ɉɫɟɜɞɨɧɢɦ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ> [[<ɇɚɱɚɥɶɧɵɣ ɧɨɦɟɪ> [..<Ʉɨɧɟɱɧɵɣ ɧɨɦɟɪ> [:<ɒɚɝ>] ]]] <ɉɫɟɜɞɨɧɢɦ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ> ::= <ɂɞɟɧɬɢɮɢɤɚɬɨɪ> <Ɉɩɢɫɚɧɢɟ ɜɟɫɨɜ> ::= Weights <ɋɩɢɫɨɤ ɜɟɫɨɜ>; <ɋɩɢɫɨɤ ɜɟɫɨɜ> ::= <ȼɟɫ> [,<ɋɩɢɫɨɤ ɜɟɫɨɜ>] <ȼɟɫ> ::= <Ⱦɟɣɫɬɜɢɬɟɥɶɧɨɟ ɱɢɫɥɨ> <Ɉɩɢɫɚɧɢɟ ɫɢɝɧɚɥɨɜ> ::= Signals <Ʉɨɧɫɬɚɧɬɧɨɟ ɜɵɪɚɠɟɧɢɟ ɬɢɩɚ Long> <Ɉɩɢɫɚɧɢɟ ɪɚɫɩɪɟɞɟɥɟɧɢɹ ɫɢɝɧɚɥɨɜ> ::= <Ɉɩɢɫɚɧɢɟ ɪɚɫɩɪɟɞɟɥɟɧɢɹ ɋɢɝɧɚɥɨɜ, Ɉɰɟɧɤɢ, ɑɚɫɬɧɨɣ ɨɰɟɧɤɢ, Signals> <Ɉɩɢɫɚɧɢɟ ɪɚɫɩɪɟɞɟɥɟɧɢɹ ɨɬɜɟɬɨɜ> ::= <Ɉɩɢɫɚɧɢɟ ɪɚɫɩɪɟɞɟɥɟɧɢɹ Ɉɬɜɟɬɨɜ, Ɉɰɟɧɤɢ, ɑɚɫɬɧɨɣ ɨɰɟɧɤɢ, Answer> <Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɨɰɟɧɤɢ> ::= End Estimation
6.9.2 Ɉɩɢɫɚɧɢɟ ɹɡɵɤɚ ɨɩɢɫɚɧɢɹ ɨɰɟɧɨɤ ɋɬɪɭɤɬɭɪɚ ɨɩɢɫɚɧɢɹ ɨɰɟɧɤɢ ɢɦɟɟɬ ɜɢɞ: ɡɚɝɨɥɨɜɨɤ, ɨɩɢɫɚɧɢɟ ɮɭɧɤɰɢɣ, ɨɩɢɫɚɧɢɟ ɱɚɫɬɧɵɯ ɨɰɟɧɨɤ, ɨɩɢɫɚɧɢɟ ɫɨɫɬɚɜɚ, ɨɩɢɫɚɧɢɟ ɫɜɹɡɟɣ ɫ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚɦɢ, ɨɩɢɫɚɧɢɟ ɫɢɝɧɚɥɨɜ, ɨɩɢɫɚɧɢɟ ɪɚɫɩɪɟɞɟɥɟɧɢɹ ɫɢɝɧɚɥɨɜ, ɨɩɢɫɚɧɢɟ ɪɚɫɩɪɟɞɟɥɟɧɢɹ ɨɬɜɟɬɨɜ, ɤɨɧɟɰ ɨɩɢɫɚɧɢɹ ɨɰɟɧɤɢ. Ɂɚɝɨɥɨɜɨɤ ɫɨɫɬɨɢɬ ɢɡ ɤɥɸɱɟɜɨɝɨ ɫɥɨɜɚ Estimation ɢ ɢɦɟɧɢ ɨɰɟɧɤɢ ɢ ɫɥɭɠɢɬ ɞɥɹ ɨɛɨɡɧɚɱɟɧɢɹ ɧɚɱɚɥɚ ɨɩɢɫɚɧɢɹ ɨɰɟɧɤɢ ɜ ɮɚɣɥɟ, ɫɨɞɟɪɠɚɳɟɦ ɧɟɫɤɨɥɶɤɨ ɤɨɦɩɨɧɟɧɬ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚ.
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128
Ɉɩɢɫɚɧɢɟ ɱɚɫɬɧɨɣ ɨɰɟɧɤɢ – ɷɬɨ ɨɩɢɫɚɧɢɟ ɩɪɨɰɟɞɭɪɵ, ɜɵɱɢɫɥɹɸɳɟɣ ɨɰɟɧɤɭ ɢ, ɩɪɢ ɧɟɨɛɯɨɞɢɦɨɫɬɢ, ɦɚɫɫɢɜ ɩɪɨɢɡɜɨɞɧɵɯ ɨɰɟɧɤɢ ɩɨ ɜɵɯɨɞɧɵɦ ɫɢɝɧɚɥɚɦ ɫɟɬɢ. Ɉɬɦɟɬɢɦ, ɱɬɨ ɩɪɢ ɨɩɢɫɚɧɢɢ ɱɚɫɬɧɨɣ ɨɰɟɧɤɢ ɟɝɨ ɚɪɝɭɦɟɧɬɨɦ, ɤɚɤ ɩɪɚɜɢɥɨ, ɹɜɥɹɟɬɫɹ ɱɢɫɥɨ ɨɰɟɧɢɜɚɟɦɵɯ ɫɢɝɧɚɥɨɜ. ɉɪɢ ɜɵɩɨɥɧɟɧɢɢ ɱɚɫɬɧɚɹ ɨɰɟɧɤɚ ɩɨɥɭɱɚɟɬ ɜ ɤɚɱɟɫɬɜɟ ɚɪɝɭɦɟɧɬɨɜ ɦɚɫɫɢɜ ɨɰɟɧɢɜɚɟɦɵɯ ɫɢɝɧɚɥɨɜ, ɩɪɢɡɧɚɤ ɧɟɨɛɯɨɞɢɦɨɫɬɢ ɜɵɱɢɫɥɟɧɢɹ ɩɪɨɢɡɜɨɞɧɵɯ, ɩɪɚɜɢɥɶɧɵɣ ɨɬɜɟɬ, ɞɨɫɬɨɜɟɪɧɨɫɬɶ ɩɪɚɜɢɥɶɧɨɝɨ ɨɬɜɟɬɚ, ɞɟɣɫɬɜɢɬɟɥɶɧɭɸ ɩɟɪɟɦɟɧɧɭɸ ɞɥɹ ɜɨɡɜɪɚɳɟɧɢɹ ɜɵɱɢɫɥɟɧɧɨɣ ɨɰɟɧɤɢ ɢ ɦɚɫɫɢɜ ɞɥɹ ɜɨɡɜɪɚɳɟɧɢɹ ɩɪɨɢɡɜɨɞɧɵɯ. Ɏɨɪɦɚɥɶɧɨ, ɩɪɢ ɢɫɩɨɥɧɟɧɢɢ ɱɚɫɬɧɚɹ ɨɰɟɧɤɚ ɢɦɟɟɬ ɨɩɢɫɚɧɢɟ ɫɥɟɞɭɸɳɟɝɨ ɜɢɞɚ: Pascal: Procedure Estimation(Signals, Back : PRealArray; Direv : Logic; Answer, Reliability : Real; Var Estim : Real); C: void Estimation(PRealArray Signals, PRealArray Back, Logic Direv, Real Answer, Real Reliability, Real* Estim); Ɉɬɦɟɬɢɦ ɨɞɧɭ ɜɚɠɧɭɸ ɨɫɨɛɟɧɧɨɫɬɶ ɜɵɩɨɥɧɟɧɢɹ ɬɟɥɚ ɱɚɫɬɧɨɣ ɨɰɟɧɤɢ. Ɉɩɟɪɚɬɨɪ ɩɪɢɫɜɚɢɜɚɧɢɹ ɡɧɚɱɟɧɢɹ ɷɥɟɦɟɧɬɭ ɦɚɫɫɢɜɚ ɩɪɨɢɡɜɨɞɧɵɯ, ɨɡɧɚɱɚɟɬ ɞɨɛɚɜɥɟɧɢɟ ɷɬɨɝɨ ɡɧɚɱɟɧɢɹ ɤ ɜɟɥɢɱɢɧɟ, ɪɚɧɟɟ ɧɚɯɨɞɢɜɲɟɣɫɹ ɜ ɷɬɨɦ ɦɚɫɫɢɜɟ. ɇɚɩɪɢɦɟɪ, ɡɚɩɢɫɶ Back[I] = A, ɨɡɧɚɱɚɟɬ ɜɵɩɨɥɧɟɧɢɟ ɫɥɟɞɭɸɳɟɝɨ ɨɩɟɪɚɬɨɪɚ Back[I] = Back[I] + A. ɗɬɨ ɫɜɹɡɚɧɨ ɫ ɬɟɦ, ɱɬɨ ɨɞɢɧ ɢ ɬɨɬ ɠɟ ɫɢɝɧɚɥ ɦɨɠɟɬ ɛɵɬɶ ɡɚɞɟɣɫɬɜɨɜɚɧ ɜ ɧɟɫɤɨɥɶɤɢɯ ɱɚɫɬɧɵɯ ɨɰɟɧɤɚɯ ɢ ɩɪɨɢɡɜɨɞɧɚɹ ɨɛɳɟɣ ɮɭɧɤɰɢɢ ɨɰɟɧɤɢ ɪɚɜɧɚ ɫɭɦɦɟ ɩɪɨɢɡɜɨɞɧɵɯ ɱɚɫɬɧɵɯ ɨɰɟɧɨɤ ɩɨ ɷɬɨɦɭ ɫɢɝɧɚɥɭ. ȼ ɪɚɡɞɟɥɟ ɨɩɢɫɚɧɢɹ ɫɨɫɬɚɜɚ ɩɟɪɟɱɢɫɥɹɸɬɫɹ ɱɚɫɬɧɵɟ ɨɰɟɧɤɢ, ɜɯɨɞɹɳɢɟ ɜ ɫɨɫɬɚɜ ɨɰɟɧɤɢ. ɉɪɢɡɧɚɤɨɦ ɤɨɧɰɚ ɪɚɡɞɟɥɚ ɫɥɭɠɢɬ ɫɢɦɜɨɥ «;». ȼ ɧɟɨɛɹɡɚɬɟɥɶɧɨɦ ɪɚɡɞɟɥɟ ɭɫɬɚɧɨɜɥɟɧɢɹ ɩɚɪɚɦɟɬɪɨɜ ɩɪɨɢɡɜɨɞɢɬɫɹ ɡɚɞɚɧɢɟ ɡɧɚɱɟɧɢɣ ɩɚɪɚɦɟɬɪɨɜ ɱɚɫɬɧɵɯ ɨɰɟɧɨɤ. ɉɨɫɥɟ ɤɥɸɱɟɜɨɝɨ ɫɥɨɜɚ SetParameters ɫɥɟɞɭɟɬ ɫɩɢɫɨɤ ɡɧɚɱɟɧɢɣ ɩɚɪɚɦɟɬɪɨɜ ɜ ɬɨɦ ɩɨɪɹɞɤɟ, ɜ ɤɚɤɨɦ ɩɚɪɚɦɟɬɪɵ (ɫɬɚɬɢɱɟɫɤɢɟ ɩɟɪɟɦɟɧɧɵɟ) ɛɵɥɢ ɨɛɴɹɜɥɟɧɵ ɩɪɢ ɨɩɢɫɚɧɢɢ ɱɚɫɬɧɨɣ ɨɰɟɧɤɢ (ɞɥɹ ɫɬɚɧɞɚɪɬɧɵɯ ɨɰɟɧɨɤ ɩɨɪɹɞɨɤ ɩɚɪɚɦɟɬɪɨɜ ɭɤɚɡɚɧ ɜ ɬɚɛɥ. 6). ɉɪɢ ɢɫɩɨɥɶɡɨɜɚɧɢɢ ɨɞɧɨɝɨ ɨɩɟɪɚɬɨɪɚ ɡɚɞɚɧɢɹ ɩɚɪɚɦɟɬɪɨɜ ɞɥɹ ɡɚɞɚɧɢɹ ɩɚɪɚɦɟɬɪɨɜ ɧɟɫɤɨɥɶɤɢɦ ɷɤɡɟɦɩɥɹɪɚɦ ɨɞɧɨɣ ɱɚɫɬɧɨɣ ɨɰɟɧɤɢ ɩɨɫɥɟ ɤɥɸɱɟɜɨɝɨ ɫɥɨɜɚ SetParameters ɭɤɚɡɵɜɚɟɬɫɹ ɫɬɨɥɶɤɨ ɜɵɪɚɠɟɧɢɣ, ɡɚɞɚɸɳɢɯ ɡɧɚɱɟɧɢɹ ɩɚɪɚɦɟɬɪɨɜ, ɫɤɨɥɶɤɨ ɧɟɨɛɯɨɞɢɦɨ ɞɥɹ ɨɞɧɨɝɨ ɷɤɡɟɦɩɥɹɪɚ. ɇɚɩɪɢɦɟɪ, ɟɫɥɢ ɜ ɛɥɨɤɟ ɨɩɢɫɚɧɢɹ ɫɨɫɬɚɜɚ ɫɨɞɟɪɠɢɬɫɹ 10 ɷɤɡɟɦɩɥɹɪɨɜ ɞɜɨɢɱɧɨɣ ɨɰɟɧɤɢ ɧɚ 15 ɨɰɟɧɢɜɚɟɦɵɯ ɫɢɝɧɚɥɨɜ – MyEst : BinaryCoded(15)[10], ɬɨ ɩɨɫɥɟ ɤɥɸɱɟɜɨɝɨ ɫɥɨɜɚ SetParameters ɞɨɥɠɧɨ ɛɵɬɶ ɬɨɥɶɤɨ ɨɞɧɨ ɜɵɪɚɠɟɧɢɟ: MyEst[I:1..10] SetParameters 0.01*I ȼ ɞɚɧɧɨɦ ɩɪɢɦɟɪɟ ɩɟɪɜɚɹ ɨɰɟɧɤɚ ɛɭɞɟɬ ɢɦɟɬɶ ɭɪɨɜɟɧɶ ɧɚɞɟɠɧɨɫɬɢ ɪɚɜɧɵɣ 0.01, ɜɬɨɪɚɹ – 0.02 ɢ ɬ.ɞ. ȼ ɧɟɨɛɹɡɚɬɟɥɶɧɨɦ ɪɚɡɞɟɥɟ ɨɩɢɫɚɧɢɹ ɫɜɹɡɟɣ ɫ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚɦɢ ɦɨɠɧɨ ɭɤɚɡɚɬɶ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɨɬɜɟɬɚ, ɫɜɹɡɚɧɧɵɣ ɫ ɞɚɧɧɨɣ ɨɰɟɧɤɨɣ. Ⱦɥɹ ɫɜɹɡɢ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɢ ɨɰɟɧɤɚ ɞɨɥɠɧɵ ɢɦɟɬɶ ɨɞɢɧɚɤɨɜɨɟ ɱɢɫɥɨ ɩɚɪɚɦɟɬɪɨɜ ɢ ɨɞɢɧɚɤɨɜɵɣ ɩɨɪɹɞɨɤ ɢɯ ɨɩɢɫɚɧɢɹ. Ɍɚɤ, ɜ ɩɪɢɜɟɞɟɧɧɨɦ ɧɢɠɟ ɩɪɢɦɟɪɟ, ɧɟɜɨɡɦɨɠɧɨ ɫɜɹɡɵɜɚɧɢɟ ɨɰɟɧɤɢ Temp ɫ ɨɞɧɨɢɦɟɧɧɵɦ ɢɧɬɟɪɩɪɟɬɚɬɨɪɨɦ ɢɡ-ɡɚ ɪɚɡɥɢɱɢɹ ɜ ɱɢɫɥɟ ɩɚɪɚɦɟɬɪɨɜ. ȿɫɥɢ ɜ ɥɟɜɨɣ ɱɚɫɬɢ ɜɵɪɚɠɟɧɢɹ Link ɭɤɚɡɚɧ ɞɢɚɩɚɡɨɧ ɨɰɟɧɨɤ, ɬɨ ɜ ɩɪɚɜɨɣ ɱɚɫɬɢ ɞɨɥɠɟɧ ɛɵɬɶ ɭɤɚɡɚɧ ɞɢɚɩɚɡɨɧ, ɫɨɞɟɪɠɚɳɢɣ ɫɬɨɥɶɤɨ ɠɟ ɢɧɬɟɪɩɪɟɬɚɬɨɪɨɜ. ɍɤɚɡɚɧɢɟ ɫɜɹɡɢ ɜɥɟɱɟɬ ɢɞɟɧɬɢɱɧɨɫɬɶ ɩɚɪɚɦɟɬɪɨɜ ɨɰɟɧɤɢ ɢ ɫɜɹɡɚɧɧɨɝɨ ɫ ɧɟɣ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ ɨɬɜɟɬɨɜ. ɂɞɟɧɬɢɱɧɨɫɬɶ ɨɛɟɫɩɟɱɢɜɚɟɬɫɹ ɩɪɢ ɢɫɩɨɥɧɟɧɢɢ ɡɚɩɪɨɫɨɜ aiSetData ɢ esSetData. ȼ ɧɟɨɛɹɡɚɬɟɥɶɧɨɦ ɪɚɡɞɟɥɟ ɨɩɢɫɚɧɢɹ ɜɟɫɨɜ ɭɤɚɡɵɜɚɸɬɫɹ ɜɟɫɚ ɱɚɫɬɧɵɯ ɨɰɟɧɨɤ. ȿɫɥɢ ɷɬɨɬ ɪɚɡɞɟɥ ɨɩɭɳɟɧ, ɬɨ ɜɫɟ ɱɚɫɬɧɵɟ ɨɰɟɧɤɢ ɪɚɜɧɵ ɟɞɢɧɢɰɟ, ɬɨ ɟɫɬɶ ɜɫɟ ɱɚɫɬɧɵɟ ɨɰɟɧɤɢ ɢɦɟɸɬ ɪɚɜɧɭɸ ɡɧɚɱɢɦɨɫɬɶ. ȼ ɧɟɨɛɹɡɚɬɟɥɶɧɨɦ ɪɚɡɞɟɥɟ ɨɩɢɫɚɧɢɹ ɫɢɝɧɚɥɨɜ ɭɤɚɡɵɜɚɟɬɫɹ ɱɢɫɥɨ ɫɢɝɧɚɥɨɜ, ɨɰɟɧɢɜɚɟɦɵɯ ɜɫɟɦɢ ɱɚɫɬɧɵɦɢ ɨɰɟɧɤɚɦɢ. ȿɫɥɢ ɷɬɨɬ ɪɚɡɞɟɥ ɨɩɭɳɟɧ, ɬɨ ɩɨɥɚɝɚɟɬɫɹ, ɱɬɨ ɱɢɫɥɨ ɨɰɟɧɢɜɚɟɦɵɯ ɨɰɟɧɤɨɣ ɫɢɝɧɚɥɨɜ ɪɚɜɧɨ ɫɭɦɦɟ ɫɢɝɧɚɥɨɜ, ɨɰɟɧɢɜɚɟɦɵɯ ɜɫɟɦɢ ɱɚɫɬɧɵɦɢ ɨɰɟɧɤɚɦɢ. ȼ ɤɨɧɫɬɚɧɬɧɨɦ ɜɵɪɚɠɟɧɢɢ ɦɨɠɟɬ ɜɵɡɵɜɚɬɶɫɹ ɮɭɧɤɰɢɹ NumberOf, ɚɪɝɭɦɟɧɬɨɦ ɤɨɬɨɪɨɣ ɹɜɥɹɟɬɫɹ ɢɦɹ ɱɚɫɬɧɨɣ ɨɰɟɧɤɢ (ɢɥɢ ɟɟ ɩɫɟɜɞɨɧɢɦ) ɫ ɭɤɚɡɚɧɢɟɦ ɮɚɤɬɢɱɟɫɤɢɯ ɚɪɝɭɦɟɧɬɨɜ. ȼ ɧɟɨɛɹɡɚɬɟɥɶɧɨɦ ɪɚɡɞɟɥɟ ɨɩɢɫɚɧɢɹ ɪɚɫɩɪɟɞɟɥɟɧɢɹ ɫɢɝɧɚɥɨɜ ɞɥɹ ɤɚɠɞɨɣ ɱɚɫɬɧɨɣ ɨɰɟɧɤɢ ɭɤɚɡɵɜɚɟɬɫɹ, ɤɚɤɢɟ ɫɢɝɧɚɥɵ ɢɡ ɨɛɳɟɝɨ ɨɰɟɧɢɜɚɟɦɨɝɨ ɦɚɫɫɢɜɚ ɩɟɪɟɞɚɸɬɫɹ ɟɣ ɞɥɹ ɨɰɟɧɢɜɚɧɢɹ. ȿɫɥɢ ɷɬɨɬ ɪɚɡɞɟɥ ɨɩɭɳɟɧ, ɬɨ ɫɱɢɬɚɟɬɫɹ, ɱɬɨ ɤɚɠɞɚɹ ɫɥɟɞɭɸɳɚɹ ɱɚɫɬɧɚɹ ɨɰɟɧɤɚ ɩɨɥɭɱɚɟɬ ɫɥɟɞɭɸɳɢɣ ɮɪɚɝɦɟɧɬ ɦɚɫɫɢɜɚ ɫɢɝɧɚɥɨɜ. ɉɨɪɹɞɨɤ ɫɥɟɞɨɜɚɧɢɹ ɱɚɫɬɧɵɯ ɨɰɟɧɨɤ ɫɨɨɬɜɟɬɫɬɜɭɟɬ ɩɨɪɹɞɤɭ ɢɯ ɩɟɪɟɱɢɫɥɟɧɢɹ ɜ ɪɚɡɞɟɥɟ ɨɩɢɫɚɧɢɹ ɫɨɫɬɚɜɚ. ȼ ɩɪɢɦɟɪɟ 1 ɪɚɡɞɟɥ ɨɩɢɫɚɧɢɹ ɪɚɫɩɪɟɞɟɥɟɧɢɹ ɫɢɝɧɚɥɨɜ ɡɚɞɚɟɬ ɪɚɫɩɪɟɞɟɥɟɧɢɟ ɫɢɝɧɚɥɨɜ ɩɨ ɭɦɨɥɱɚɧɢɸ. Ɇɚɫɫɢɜ ɩɪɨɢɡɜɨɞɧɵɯ ɨɰɟɧɤɢ ɩɨ ɜɵɯɨɞɧɵɦ ɫɢɝɧɚɥɚɦ ɫɟɬɢ ɩɚɪɚɥɥɟɥɟɧ ɦɚɫɫɢɜɭ ɫɢɝɧɚɥɨɜ. ȼ ɧɟɨɛɹɡɚɬɟɥɶɧɨɦ ɪɚɡɞɟɥɟ ɨɩɢɫɚɧɢɹ ɪɚɫɩɪɟɞɟɥɟɧɢɹ ɨɬɜɟɬɨɜ ɞɥɹ ɤɚɠɞɨɣ ɱɚɫɬɧɨɣ ɨɰɟɧɤɢ ɭɤɚɡɵɜɚɟɬɫɹ ɤɚɤɨɣ ɷɥɟɦɟɧɬ ɦɚɫɫɢɜɚ ɨɬɜɟɬɨɜ ɛɭɞɟɬ ɟɣ ɩɟɪɟɞɚɧ. ȿɫɥɢ ɷɬɨɬ ɪɚɡɞɟɥ ɨɩɭɳɟɧ, ɬɨ ɫɱɢɬɚɟɬɫɹ, ɱɬɨ ɤɚɠɞɚɹ ɫɥɟɞɭɸɳɚɹ ɱɚɫɬɧɚɹ ɨɰɟɧɤɚ ɩɨɥɭɱɚɟɬ ɫɥɟɞɭɸɳɢɣ ɷɥɟɦɟɧɬ ɦɚɫɫɢɜɚ ɨɬɜɟɬɨɜ. ɉɨɪɹɞɨɤ ɫɥɟɞɨɜɚɧɢɹ ɱɚɫɬɧɵɯ ɨɰɟɧɨɤ ɫɨɨɬɜɟɬɫɬɜɭɟɬ ɩɨɪɹɞɤɭ ɢɯ ɩɟɪɟɱɢɫɥɟɧɢɹ ɜ ɪɚɡɞɟɥɟ ɨɩɢɫɚɧɢɹ ɫɨɫɬɚɜɚ. ȼ ɩɪɢɦɟɪɟ 1 ɪɚɡɞɟɥ ɨɩɢɫɚɧɢɹ ɪɚɫɩɪɟɞɟɥɟɧɢɹ ɨɬɜɟɬɨɜ ɡɚɞɚɟɬ ɪɚɫɩɪɟɞɟɥɟɧɢɟ ɫɢɝɧɚɥɨɜ ɩɨ ɭɦɨɥɱɚɧɢɸ. Ɇɚɫɫɢɜɵ ɞɨɫɬɨɜɟɪɧɨɫɬɟɣ ɨɬɜɟɬɨɜ ɢ ɜɵɱɢɫɥɟɧɧɵɯ ɨɰɟɧɨɤ ɩɚɪɚɥɥɟɥɶɧɵ ɦɚɫɫɢɜɭ ɨɬɜɟɬɨɜ.
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Ʉɪɨɦɟ ɬɨɝɨ, ɜ ɥɸɛɨɦ ɦɟɫɬɟ ɨɩɢɫɚɧɢɹ ɨɰɟɧɤɢ ɦɨɝɭɬ ɜɫɬɪɟɱɚɬɶɫɹ ɤɨɦɦɟɧɬɚɪɢɢ, ɡɚɤɥɸɱɟɧɧɵɟ ɜ ɮɢɝɭɪɧɵɟ ɫɤɨɛɤɢ.
6.9.3 ɉɪɢɦɟɪ ɨɩɢɫɚɧɢɹ ɨɰɟɧɤɢ ȼ ɷɬɨɦ ɪɚɡɞɟɥɟ ɩɪɢɜɟɞɟɧɵ ɞɜɚ ɩɪɢɦɟɪɚ ɨɩɢɫɚɧɢɹ ɨɞɧɨɣ ɢ ɬɨɣ ɠɟ ɨɰɟɧɤɢ ɫɥɟɞɭɸɳɟɝɨ ɫɨɫɬɚɜɚ: ɩɟɪɜɵɣ ɫɢɝɧɚɥ ɢɧɬɟɪɩɪɟɬɢɪɭɟɬɫɹ ɤɚɤ ɬɟɦɩɟɪɚɬɭɪɚ ɩɭɬɟɦ ɭɦɧɨɠɟɧɢɹ ɧɚ 10 ɢ ɞɨɛɚɜɥɟɧɢɹ 273; ɫɥɟɞɭɸɳɢɟ ɞɜɚ ɫɢɝɧɚɥɚ ɢɧɬɟɪɩɪɟɬɢɪɭɸɬɫɹ ɤɚɤ ɧɚɥɢɱɢɟ ɨɛɥɚɱɧɨɫɬɢ, ɢɫɩɨɥɶɡɭɹ ɡɧɚɤɨɜɵɣ ɢɧɬɟɪɩɪɟɬɚɬɨɪ; ɫɥɟɞɭɸɳɢɟ ɬɪɢ ɫɢɝɧɚɥɚ ɢɧɬɟɪɩɪɟɬɢɪɭɸɬɫɹ ɤɚɤ ɧɚɩɪɚɜɥɟɧɢɟ ɜɟɬɪɚ, ɢɫɩɨɥɶɡɭɹ ɞɜɨɢɱɧɵɣ ɢɧɬɟɪɩɪɟɬɚɬɨɪ (ɜɨɫɟɦɶ ɪɭɦɛɨɜ); ɩɨɫɥɟɞɧɢɟ ɬɪɢ ɫɢɝɧɚɥɚ ɢɧɬɟɪɩɪɟɬɢɪɭɸɬɫɹ ɦɚɤɫɢɦɚɥɶɧɵɦ ɢɧɬɟɪɩɪɟɬɚɬɨɪɨɦ ɤɚɤ ɫɢɥɚ ɨɫɚɞɤɨɜ (ɛɟɡ ɨɫɚɞɤɨɜ, ɫɥɚɛɵɟ ɨɫɚɞɤɢ, ɫɢɥɶɧɵɟ ɨɫɚɞɤɢ). Ⱦɥɹ ɬɪɟɯ ɩɨɫɥɟɞɧɢɯ ɢɧɬɟɪɩɪɟɬɚɬɨɪɨɜ ɢɫɩɨɥɶɡɭɸɬɫɹ ɫɨɨɬɜɟɬɫɬɜɭɸɳɢɟ ɢɦ ɨɰɟɧɤɢ ɬɢɩɚ ɪɚɫɫɬɨɹɧɢɟ ɞɨ ɦɧɨɠɟɫɬɜɚ. ȼ ɩɟɪɜɨɦ ɩɪɢɦɟɪɟ ɩɪɢɜɟɞɟɧɨ ɨɩɢɫɚɧɢɟ ɞɭɛɥɢɤɚɬɨɜ ɜɫɟɯ ɫɬɚɧɞɚɪɬɧɵɯ ɨɰɟɧɨɤ. ȼɨ ɜɬɨɪɨɦ – ɢɫɩɨɥɶɡɨɜɚɧɵ ɫɬɚɧɞɚɪɬɧɵɟ ɨɰɟɧɤɢ. ɉɪɢɦɟɪ 1. Estimation Meteorology Est Empty1 () {Ɉɰɟɧɤɚ ɞɥɹ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ, ɨɫɭɳɟɫɬɜɥɹɸɳɟɝɨ ɦɚɫɲɬɚɛɢɪɨɜɚɧɢɟ ɢ ɫɞɜɢɝ ɫɢɝɧɚɥɚ} Static Real B Name "Ɇɚɫɲɬɚɛɧɵɣ ɦɧɨɠɢɬɟɥɶ"; Real C Name "ɋɞɜɢɝ ɧɚɱɚɥɚ ɨɬɫɱɟɬɚ"; Real E Name "Ɍɪɟɛɭɟɦɚɹ ɬɨɱɧɨɫɬɶ ɫɨɜɩɚɞɟɧɢɹ"; Var Real A; Begin A = Signals[1] - (Answer - C) / B; D = E * Reliability; {ɍɪɨɜɟɧɶ ɧɚɞɟɠɧɨɫɬɢ ɫ ɩɨɩɪɚɜɤɨɣ ɧɚ ɞɨɫɬɨɜɟɪɧɨɫɬɶ} If Abs(A) 0 Then Begin Estim = Weight * Sqr(A - D) / 2; If Direv Then Back[1] = Weight * (A - D); End Else Begin Estim = Weight * Sqr(A + D) / 2; If Direv Then Back[1] = Weight * (A + D); End End Est Binary1 ( N : Long) { Ʉɨɞɢɪɨɜɚɧɢɟ ɧɨɦɟɪɨɦ ɤɚɧɚɥɚ. Ɉɰɟɧɤɚ ɞɥɹ ɡɧɚɤɨɜɨɝɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ.} Static Real E Name "ɍɪɨɜɟɧɶ ɧɚɞɟɠɧɨɫɬɢ; Var Long I, J; Real A, B, C; Begin J = Answer; {ɉɪɚɜɢɥɶɧɵɣ ɨɬɜɟɬ – ɧɨɦɟɪ ɩɪɚɜɢɥɶɧɨɝɨ ɤɥɚɫɫɚ} B = 0; C = E * Reliability; {ɍɪɨɜɟɧɶ ɧɚɞɟɠɧɨɫɬɢ ɫ ɩɨɩɪɚɜɤɨɣ ɧɚ ɞɨɫɬɨɜɟɪɧɨɫɬɶ} For I = 1 To N Do If I = J Then Begin If Signals[I] < ɋ Then Begin B = B + Sqr(Signals[I] - ɋ); If Direv Then Back[I] = 2 * Weight * (Signals[I]-ɋ); End; End Else Begin If Signals[I] > -C Then Begin B = B + Sqr(Signals[I] + C); If Direv Then Back[I] = 2 * Weight * (Signals[I] + C); End End; Estim = Weight*B End
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Est Major1 ( N : Long) {Ʉɨɞɢɪɨɜɚɧɢɟ ɧɨɦɟɪɨɦ ɤɚɧɚɥɚ. Ɉɰɟɧɤɚ ɞɥɹ ɦɚɤɫɢɦɚɥɶɧɨɝɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ.} Static Real E Name "ɍɪɨɜɟɧɶ ɧɚɞɟɠɧɨɫɬɢ; Var Real A, B; Long I, J, K, Ans; RealArray[N+1] Al,Ind; Begin Ans = Answer; Ind[1] = Ans; Al[1] = Signals[Ans] - E * Reliability; Ind[N+1] = 0; Al[N+1] = -1.e40; K:=1; For I = 1 To N Do If I <> Ans Then Begin Al[K] = Signals[I]; Ind[K] = I; K = K + 1; End; {ɉɨɞɝɨɬɨɜɥɟɧ ɦɚɫɫɢɜ ɫɢɝɧɚɥɨɜ} For I = 2 To N-1 Do Begin A = Al[I]; K = I; For J = I+1 To N Do If Al[J] > A Then Begin K = J; A = Al[J]; End; {ɇɚɣɞɟɧ ɫɥɟɞɭɸɳɢɣ ɩɨ ɜɟɥɢɱɢɧɟ} Al[K] = Al[I]; Al[I] = A; J = Ind[K]; Ind[K] = Ind[I]; Ind[I] = J; End; {Ɇɚɫɫɢɜɵ ɨɬɫɨɪɬɢɪɨɜɚɧɵ} A = Al[1]; {ɋɭɦɦɚ ɩɟɪɜɵɯ I ɱɥɟɧɨɜ} I = 1; While (A / I <= Al[I+1]) Do Begin A = A + Al[I]; I = I + 1; End; {ȼ ɤɨɧɰɟ ɰɢɤɥɚ I-1 ɪɚɜɧɨ ɱɢɫɥɭ ɤɨɪɪɟɤɬɢɪɭɟɦɵɯ ɫɢɝɧɚɥɨɜ} B = A / I; {B – ɜɟɥɢɱɢɧɚ, ɤ ɤɨɬɨɪɨɣ ɞɨɥɠɧɵ ɫɬɪɟɦɢɬɶɫɹ} A = 0; {ɤɨɪɪɟɤɬɢɪɭɟɦɵɟ ɫɢɝɧɚɥɵ} For J = 1 To I Do Begin A = A + Sqr(Al[J] - B); If Direv Then Back[Ind[J]] = -2* Weight * (Al[J] - B); End; Estim = Weight * A End; Est BynaryCoded1 : ( N : Long) {Ɉɰɟɧɤɚ ɞɥɹ ɤɨɞɢɪɨɜɚɧɢɹ ɧɨɦɟɪɨɦ ɤɚɧɚɥɚ} Static Real E Name "ɍɪɨɜɟɧɶ ɧɚɞɟɠɧɨɫɬɢ; Var Long I, J, A, K; Real B, C; Begin A = Answer; B = 0; C = E * Reliability; {ɍɪɨɜɟɧɶ ɧɚɞɟɠɧɨɫɬɢ ɫ ɩɨɩɪɚɜɤɨɣ ɧɚ ɞɨɫɬɨɜɟɪɧɨɫɬɶ}
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For I = N To 1 By -1 Do Begin J = A / 2; K = A - 2 * J; A = J; If A = 1 Then Begin If Signals[I] < C Then Begin B = B + Sqr(Signals[I] - C); If Direv Then Back[I] = 2 * Weight * (Signals[I]-C); End; End Else Begin If Signals[I] > -C Then Begin B = B + Sqr(Signals[I] + C); If Direv Then Back[I] = 2 * Weight * (Signals[I] + C); End; End; Estim = Weight*B End Contents Temp : Empty1, Cloud : Binary1(2), Wind : BynaryCoded1(3), Rain : Major1(3); Cloud Link Meteorology.Cloud Wind Link Meteorology.Wind Rain Link Meteorology.Rain
{ɋɜɹɡɵɜɚɟɦ ɨɰɟɧɤɢ ɫ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚɦɢ}
Temp SetParameters 10, 273; Cloud SetParameters 0.1; Wind SetParameters 0.2; Rain SetParameters 0.15
{ɍɫɬɚɧɚɜɥɢɜɚɟɦ ɡɧɚɱɟɧɢɹ ɩɚɪɚɦɟɬɪɨɜ ɨɰɟɧɨɤ} {ɢ ɢɧɬɟɪɩɪɟɬɚɬɨɪɨɜ}
Weights 1, 1, 1, 1 Signals NumberOf(Signals,Temp) + NumberOf(Signals, Cloud) + NumberOf(Signals, Wind) + NumberOf(Signals, Rain) Connections Temp.Signals <=> Signals[1]; Cloud.Signals[1..2] <=> Signals[2; 3]; Wind.Signals[1..3] <=> Signals[4..6]; Rain.Signals[1..3] <=> Signals[7..9] Temp.Answer <=> Answer[1]; Cloud.Answer[1..2] <=> Answer[2]; Wind.Answer[1..3] <=> Answer[3]; Rain.Answer[1..3] <=> Answer[4] End Interpretator ɉɪɢɦɟɪ 2. Estimation Meteorology Contents Temp : Empty, Cloud : Binary(2), Wind : BynaryCoded(3), Rain : Major(3); Cloud Link Meteorology.Cloud Wind Link Meteorology.Wind Rain Link Meteorology.Rain
{ɋɜɹɡɵɜɚɟɦ ɨɰɟɧɤɢ ɫ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚɦɢ}
Temp SetParameters 10, 273; Cloud SetParameters 0.1; Wind SetParameters 0.2; Rain SetParameters 0.15
{ɍɫɬɚɧɚɜɥɢɜɚɟɦ ɡɧɚɱɟɧɢɹ ɩɚɪɚɦɟɬɪɨɜ ɨɰɟɧɨɤ} {ɢ ɢɧɬɟɪɩɪɟɬɚɬɨɪɨɜ}
End Interpretator
6.10 ɋɬɚɧɞɚɪɬ ɜɬɨɪɨɝɨ ɭɪɨɜɧɹ ɤɨɦɩɨɧɟɧɬɚ ɨɰɟɧɤɚ Ɂɚɩɪɨɫɵ ɤ ɤɨɦɩɨɧɟɧɬɟ ɨɰɟɧɤɚ ɦɨɠɧɨ ɪɚɡɛɢɬɶ ɧɚ ɩɹɬɶ ɝɪɭɩɩ:
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Ɉɰɟɧɢɜɚɧɢɟ. ɂɡɦɟɧɟɧɢɟ ɩɚɪɚɦɟɬɪɨɜ. Ɋɚɛɨɬɚ ɫɨ ɫɬɪɭɤɬɭɪɨɣ. ɂɧɢɰɢɚɰɢɹ ɪɟɞɚɤɬɨɪɚ ɢ ɤɨɧɫɬɪɭɤɬɨɪɚ ɨɰɟɧɤɢ. Ɉɛɪɚɛɨɬɤɚ ɨɲɢɛɨɤ. ɉɨɫɤɨɥɶɤɭ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪ ɦɨɠɟɬ ɪɚɛɨɬɚɬɶ ɨɞɧɨɜɪɟɦɟɧɧɨ ɫ ɧɟɫɤɨɥɶɤɢɦɢ ɫɟɬɹɦɢ, ɬɨ ɢ ɤɨɦɩɨɧɟɧɬ ɨɰɟɧɤɚ ɞɨɥɠɟɧ ɢɦɟɬɶ ɜɨɡɦɨɠɧɨɫɬɶ ɨɞɧɨɜɪɟɦɟɧɧɨɣ ɪɚɛɨɬɵ ɫ ɧɟɫɤɨɥɶɤɢɦɢ ɨɰɟɧɤɚɦɢ. ɉɨɷɬɨɦɭ ɛɨɥɶɲɢɧɫɬɜɨ ɡɚɩɪɨɫɨɜ ɤ ɨɰɟɧɤɟ ɫɨɞɟɪɠɚɬ ɹɜɧɨɟ ɭɤɚɡɚɧɢɟ ɢɦɟɧɢ ɨɰɟɧɤɢ. ɇɢɠɟ ɩɪɢɜɟɞɟɧɨ ɨɩɢɫɚɧɢɟ ɜɫɟɯ ɡɚɩɪɨɫɨɜ ɤ ɤɨɦɩɨɧɟɧɬɭ ɨɰɟɧɤɚ. Ʉɚɠɞɵɣ ɡɚɩɪɨɫ ɹɜɥɹɟɬɫɹ ɥɨɝɢɱɟɫɤɨɣ ɮɭɧɤɰɢɟɣ, ɜɨɡɜɪɚɳɚɸɳɟɣ ɡɧɚɱɟɧɢɟ ɢɫɬɢɧɚ, ɟɫɥɢ ɡɚɩɪɨɫ ɜɵɩɨɥɧɟɧ ɭɫɩɟɲɧɨ, ɢ ɥɨɠɶ – ɩɪɢ ɨɲɢɛɨɱɧɨɦ ɡɚɜɟɪɲɟɧɢɢ ɢɫɩɨɥɧɟɧɢɹ ɡɚɩɪɨɫɚ. ȼ ɡɚɩɪɨɫɚɯ ɜɬɨɪɨɣ ɢ ɬɪɟɬɶɟɣ ɝɪɭɩɩɵ ɩɪɢ ɨɛɪɚɳɟɧɢɢ ɤ ɱɚɫɬɧɵɦ ɨɰɟɧɤɚɦ ɢɫɩɨɥɶɡɭɟɬɫɹ ɫɥɟɞɭɸɳɢɣ ɫɢɧɬɚɤɫɢɫ: <ɉɨɥɧɨɟ ɢɦɹ ɱɚɫɬɧɨɣ ɨɰɟɧɤɢ> ::= <ɂɦɹ ɨɰɟɧɤɢ>.<ɉɫɟɜɞɨɧɢɦ ɱɚɫɬɧɨɣ ɨɰɟɧɤɢ> [[<ɇɨɦɟɪ ɷɤɡɟɦɩɥɹɪɚ>]] ɉɪɢ ɜɵɡɨɜɟ ɪɹɞɚ ɡɚɩɪɨɫɨɜ ɢɫɩɨɥɶɡɭɸɬɫɹ ɩɪɟɞɨɩɪɟɞɟɥɟɧɧɵɟ ɤɨɧɫɬɚɧɬɵ. ɂɯ ɡɧɚɱɟɧɢɹ ɩɪɢɜɟɞɟɧɵ ɜ ɬɚɛɥ. 3. 1. 2. 3. 4. 5.
6.10.1 Ɂɚɩɪɨɫ ɧɚ ɨɰɟɧɢɜɚɧɢɟ ȿɞɢɧɫɬɜɟɧɧɵɣ ɡɚɩɪɨɫ ɩɟɪɜɨɣ ɝɪɭɩɩɵ ɜɵɩɨɥɧɹɟɬ ɨɫɧɨɜɧɭɸ ɮɭɧɤɰɢɸ ɤɨɦɩɨɧɟɧɬɚ ɨɰɟɧɤɚ – ɜɵɱɢɫɥɹɟɬ ɨɰɟɧɤɭ ɢ, ɟɫɥɢ ɬɪɟɛɭɟɬɫɹ, ɦɚɫɫɢɜ ɩɪɨɢɡɜɨɞɧɵɯ ɨɰɟɧɤɢ ɩɨ ɨɰɟɧɢɜɚɟɦɵɦ ɫɢɝɧɚɥɚɦ.
6.10.1.1 Ɉɰɟɧɢɬɶ ɦɚɫɫɢɜ ɫɢɝɧɚɥɨɜ (Estimate) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function Estimate( EstName : PString; Signals, Back, Answers, Reliability: PRealArray; Direv : Logic; Var Estim : Real ) : Logic; C: Logic Estimate(PString EstName, PRealArray Signals, PRealArray* Back, PRealArray Answers, PRealArray Reliability, Logic Direv, Real* Estim) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɚ: EstName – ɭɤɚɡɚɬɟɥɶ ɧɚ ɫɬɪɨɤɭ ɫɢɦɜɨɥɨɜ, ɫɨɞɟɪɠɚɳɭɸ ɢɦɹ ɨɰɟɧɤɢ. Signals – ɭɤɚɡɚɬɟɥɶ ɧɚ ɦɚɫɫɢɜ ɨɰɟɧɢɜɚɟɦɵɯ ɫɢɝɧɚɥɨɜ. Back – ɭɤɚɡɚɬɟɥɶ ɧɚ ɦɚɫɫɢɜ ɩɪɨɢɡɜɨɞɧɵɯ ɨɰɟɧɤɢ ɩɨ ɨɰɟɧɢɜɚɟɦɵɦ ɫɢɝɧɚɥɚɦ. Answers – ɭɤɚɡɚɬɟɥɶ ɧɚ ɦɚɫɫɢɜ ɩɪɚɜɢɥɶɧɵɯ ɨɬɜɟɬɨɜ. Reliability – ɭɤɚɡɚɬɟɥɶ ɧɚ ɦɚɫɫɢɜ ɞɨɫɬɨɜɟɪɧɨɫɬɟɣ ɩɪɚɜɢɥɶɧɵɯ ɨɬɜɟɬɨɜ. Direv – ɩɪɢɡɧɚɤ ɧɟɨɛɯɨɞɢɦɨɫɬɢ ɜɵɱɢɫɥɟɧɢɹ ɩɪɨɢɡɜɨɞɧɵɯ (False – ɧɟ ɜɵɱɢɫɥɹɬɶ). Estim – ɜɵɱɢɫɥɟɧɧɚɹ ɨɰɟɧɤɚ. ɇɚɡɧɚɱɟɧɢɟ – ɜɵɱɢɫɥɹɟɬ ɨɰɟɧɤɭ ɦɚɫɫɢɜɚ ɫɢɝɧɚɥɨɜ Signals, ɢɫɩɨɥɶɡɭɹ ɨɰɟɧɤɭ, ɭɤɚɡɚɧɧɭɸ ɜ ɩɚɪɚɦɟɬɪɟ EstName. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. 1. ȿɫɥɢ Error <> 0, ɬɨ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 2. ȿɫɥɢ ɜ ɤɚɱɟɫɬɜɟ ɚɪɝɭɦɟɧɬɚ EstName ɞɚɧ ɩɭɫɬɨɣ ɭɤɚɡɚɬɟɥɶ, ɢɥɢ ɭɤɚɡɚɬɟɥɶ ɧɚ ɩɭɫɬɭɸ ɫɬɪɨɤɭ, ɬɨ ɢɫɩɨɥɧɹɸɳɢɦ ɡɚɩɪɨɫ ɨɛɴɟɤɬɨɦ ɹɜɥɹɟɬɫɹ ɩɟɪɜɚɹ ɨɰɟɧɤɚ ɜ ɫɩɢɫɤɟ ɨɰɟɧɨɤ ɤɨɦɩɨɧɟɧɬɚ ɨɰɟɧɤɚ. 3. ȿɫɥɢ ɫɩɢɫɨɤ ɨɰɟɧɨɤ ɤɨɦɩɨɧɟɧɬɚ ɨɰɟɧɤɚ ɩɭɫɬ ɢɥɢ ɢɦɹ ɨɰɟɧɤɢ, ɩɟɪɟɞɚɧɧɨɟ ɜ ɚɪɝɭɦɟɧɬɟ EstName, ɜ ɷɬɨɦ ɫɩɢɫɤɟ ɧɟ ɧɚɣɞɟɧɨ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 401 – ɧɟɜɟɪɧɨɟ ɢɦɹ ɨɰɟɧɤɢ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 4. ɉɪɨɢɡɜɨɞɢɬɫɹ ɜɵɱɢɫɥɟɧɢɟ ɨɰɟɧɤɢ ɨɰɟɧɤɨɣ, ɢɦɹ ɤɨɬɨɪɨɣ ɛɵɥɨ ɭɤɚɡɚɧɨ ɜ ɚɪɝɭɦɟɧɬɟ EstName. 5. ȿɫɥɢ ɜɨ ɜɪɟɦɹ ɜɵɩɨɥɧɟɧɢɹ ɡɚɩɪɨɫɚ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ, ɬɨ ɝɟɧɟɪɢɪɭɟɬɫɹ ɜɧɭɬɪɟɧɧɹɹ ɨɲɢɛɤɚ 404 – ɨɲɢɛɤɚ ɨɰɟɧɢɜɚɧɢɹ. ɍɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ. ȼɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. ȼ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɭɫɩɟɲɧɨ ɡɚɜɟɪɲɚɟɬɫɹ.
6.10.2 Ɉɫɬɚɥɶɧɵɟ ɡɚɩɪɨɫɵ ɇɢɠɟ ɩɪɢɜɟɞɟɧ ɫɩɢɫɨɤ ɡɚɩɪɨɫɨɜ, ɢɫɩɨɥɧɟɧɢɟ ɤɨɬɨɪɵɯ ɨɩɢɫɚɧɨ ɜ ɝɥɚɜɟ "Ɉɛɳɢɣ ɫɬɚɧɞɚɪɬ": esSetCurrent – ɋɞɟɥɚɬɶ ɨɰɟɧɤɭ ɬɟɤɭɳɢɦ esAdd – Ⱦɨɛɚɜɥɟɧɢɟ ɧɨɜɨɣ ɨɰɟɧɤɢ esDelete – ɍɞɚɥɟɧɢɟ ɨɰɟɧɤɢ esWrite – Ɂɚɩɢɫɶ ɨɰɟɧɤɢ esGetStructNames – ȼɟɪɧɭɬɶ ɢɦɟɧɚ ɱɚɫɬɧɵɯ ɨɰɟɧɨɤ
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esGetType – ȼɟɪɧɭɬɶ ɬɢɩ ɱɚɫɬɧɨɣ ɨɰɟɧɤɢ esGetData – ɉɨɥɭɱɢɬɶ ɩɚɪɚɦɟɬɪɵ ɱɚɫɬɧɨɣ ɨɰɟɧɤɢ esGetName – ɉɨɥɭɱɢɬɶ ɢɦɟɧɚ ɩɚɪɚɦɟɬɪɨɜ ɱɚɫɬɧɨɣ ɨɰɟɧɤɢ esSetData – ɍɫɬɚɧɨɜɢɬɶ ɩɚɪɚɦɟɬɪɵ ɱɚɫɬɧɨɣ ɨɰɟɧɤɢ esEdit – Ɋɟɞɚɤɬɢɪɨɜɚɬɶ ɨɰɟɧɤɭ OnError – ɍɫɬɚɧɨɜɢɬɶ ɨɛɪɚɛɨɬɱɢɤ ɨɲɢɛɨɤ GetError – Ⱦɚɬɶ ɧɨɦɟɪ ɨɲɢɛɤɢ FreeMemory – Ɉɫɜɨɛɨɞɢɬɶ ɩɚɦɹɬɶ ȼ ɡɚɩɪɨɫɟ esGetType ɜ ɩɟɪɟɦɟɧɧɨɣ TypeId ɜɨɡɜɪɚɳɚɟɬɫɹ ɡɧɚɱɟɧɢɟ ɨɞɧɨɣ ɢɡ ɩɪɟɞɨɩɪɟɞɟɥɟɧɧɵɯ ɤɨɧɫɬɚɧɬ, ɩɟɪɟɱɢɫɥɟɧɧɵɯ ɜ ɬɚɛɥ. 3. Ʉɪɨɦɟ ɬɨɝɨ, ɜɨ ɜɬɨɪɨɣ ɝɪɭɩɩɟ ɡɚɩɪɨɫɨɜ ɟɫɬɶ ɡɚɩɪɨɫ SetEstIntParameters ɚɧɚɥɨɝɢɱɧɵɣ ɡɚɩɪɨɫɭ esSetData, ɧɨ ɨɩɪɟɞɟɥɹɸɳɢɣ ɱɚɫɬɧɭɸ ɨɰɟɧɤɭ, ɩɚɪɚɦɟɬɪɵ ɤɨɬɨɪɨɣ ɢɡɦɟɧɹɸɬɫɹ, ɩɨ ɩɨɥɧɨɦɭ ɢɦɟɧɢ ɫɜɹɡɚɧɧɨɝɨ ɫ ɧɟɣ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ ɨɬɜɟɬɚ.
6.10.2.1 ɍɫɬɚɧɨɜɢɬɶ ɩɚɪɚɦɟɬɪɵ (SetEstIntParameters) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function SetEstIntParameters( IntName : PString; Param : PRealArray ) : Logic; C: Logic SetEstIntParameters(PString IntName, PRealArray Param) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: IntName – ɭɤɚɡɚɬɟɥɶ ɧɚ ɫɬɪɨɤɭ ɫɢɦɜɨɥɨɜ, ɫɨɞɟɪɠɚɳɭɸ ɩɨɥɧɨɟ ɢɦɹ ɱɚɫɬɧɨɝɨ ɢɧɬɟɪɩɪɟɬɚɬɨɪɚ ɨɬɜɟɬɚ. Param – ɚɞɪɟɫ ɦɚɫɫɢɜɚ ɩɚɪɚɦɟɬɪɨɜ. ɇɚɡɧɚɱɟɧɢɟ – ɡɚɦɟɧɹɟɬ ɡɧɚɱɟɧɢɹ ɩɚɪɚɦɟɬɪɨɜ ɱɚɫɬɧɨɣ ɨɰɟɧɤɢ, ɫɜɹɡɚɧɧɨɣ ɫ ɢɧɬɟɪɩɪɟɬɚɬɨɪɨɦ ɨɬɜɟɬɚ, ɭɤɚɡɚɧɧɨɝɨ ɜ ɚɪɝɭɦɟɧɬɟ IntName, ɧɚ ɡɧɚɱɟɧɢɹ, ɩɟɪɟɞɚɧɧɵɟ, ɜ ɚɪɝɭɦɟɧɬɟ Param. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. 1. Ɂɚɩɪɨɫ ɩɟɪɟɞɚɟɬɫɹ ɜɫɟɦ ɱɚɫɬɧɵɦ ɨɰɟɧɤɚɦ ɜɫɟɯ ɨɰɟɧɨɤ ɜ ɫɩɢɫɤɟ ɨɰɟɧɨɤ ɤɨɦɩɨɧɟɧɬɚ ɨɰɟɧɤɚ. 2. ȿɫɥɢ ɱɚɫɬɧɚɹ ɨɰɟɧɤɚ ɫɜɹɡɚɧɚ ɫ ɱɚɫɬɧɵɦ ɢɧɬɟɪɩɪɟɬɚɬɨɪɨɦ ɨɬɜɟɬɚ, ɢɦɹ ɤɨɬɨɪɨɝɨ ɭɤɚɡɚɧɨ ɜ ɚɪɝɭɦɟɧɬɟ IntName, ɬɨ ɬɟɤɭɳɢɟ ɡɧɚɱɟɧɢɹ ɩɚɪɚɦɟɬɪɨɜ ɱɚɫɬɧɨɣ ɨɰɟɧɤɢ ɡɚɦɟɧɹɸɬɫɹ ɧɚ ɡɧɚɱɟɧɢɹ, ɯɪɚɧɹɳɢɟɫɹ ɜ ɦɚɫɫɢɜɟ, ɚɞɪɟɫ ɤɨɬɨɪɨɝɨ ɩɟɪɟɞɚɧ ɜ ɚɪɝɭɦɟɧɬɟ Param,.
6.10.3 Ɉɲɢɛɤɢ ɤɨɦɩɨɧɟɧɬɚ ɨɰɟɧɤɚ ȼ ɬɚɛɥ. 7 ɩɪɢɜɟɞɟɧ ɩɨɥɧɵɣ ɫɩɢɫɨɤ ɨɲɢɛɨɤ, ɤɨɬɨɪɵɟ ɦɨɝɭɬ ɜɨɡɧɢɤɚɬɶ ɩɪɢ ɜɵɩɨɥɧɟɧɢɢ ɡɚɩɪɨɫɨɜ ɤɨɦɩɨɧɟɧɬɨɦ ɨɰɟɧɤɚ, ɢ ɞɟɣɫɬɜɢɹ ɫɬɚɧɞɚɪɬɧɨɝɨ ɨɛɪɚɛɨɬɱɢɤɚ ɨɲɢɛɨɤ. Ɍɚɛɥɢɰɚ 7. Ɉɲɢɛɤɢ ɤɨɦɩɨɧɟɧɬɚ ɨɰɟɧɤɚ ɢ ɞɟɣɫɬɜɢɹ ɫɬɚɧɞɚɪɬɧɨɝɨ ɨɛɪɚɛɨɬɱɢɤɚ ɨɲɢɛɨɤ. ʋɇɚɡɜɚɧɢɟ ɨɲɢɛɤɢɋɬɚɧɞɚɪɬɧɚɹ ɨɛɪɚɛɨɬɤɚ 401 ɇɟɜɟɪɧɨɟ ɢɦɹ ɨɰɟɧɤɢɁɚɧɟɫɟɧɢɟ ɧɨɦɟɪɚ ɜ Error 402 Ɉɲɢɛɤɚ ɫɱɢɬɵɜɚɧɢɹ ɨɰɟɧɤɢɁɚɧɟɫɟɧɢɟ ɧɨɦɟɪɚ ɜ Error 403 Ɉɲɢɛɤɚ ɫɨɯɪɚɧɟɧɢɹ ɨɰɟɧɤɢɁɚɧɟɫɟɧɢɟ ɧɨɦɟɪɚ ɜ Error 404 Ɉɲɢɛɤɚ ɜɵɱɢɫɥɟɧɢɹ ɨɰɟɧɤɢɁɚɧɟɫɟɧɢɟ ɧɨɦɟɪɚ ɜ Error
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7. ɂɫɩɨɥɧɢɬɟɥɶ Ʉɨɦɩɨɧɟɧɬ ɢɫɩɨɥɧɢɬɟɥɶ ɹɜɥɹɟɬɫɹ ɫɥɭɠɟɛɧɵɦ. ɗɬɨ ɨɡɧɚɱɚɟɬ, ɱɬɨ ɨɧ ɭɧɢɜɟɪɫɚɥɟɧ ɢ ɧɟɜɢɞɢɦ ɞɥɹ ɩɨɥɶɡɨɜɚɬɟɥɹ. ȼ ɨɬɥɢɱɢɟ ɨɬ ɜɫɟɯ ɞɪɭɝɢɯ ɤɨɦɩɨɧɟɧɬɨɜ ɢɫɩɨɥɧɢɬɟɥɶ ɧɟ ɜɵɩɨɥɧɹɟɬ ɧɢ ɨɞɧɨɣ ɹɜɧɨɣ ɮɭɧɤɰɢɢ ɜ ɨɛɭɱɟɧɢɢ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ, ɚ ɹɜɥɹɟɬɫɹ ɜɫɩɨɦɨɝɚɬɟɥɶɧɵɦ ɞɥɹ ɤɨɦɩɨɧɟɧɬɨɜ ɭɱɢɬɟɥɶ ɢ ɤɨɧɬɪɚɫɬɟɪ. Ɂɚɞɚɱɚ ɷɬɨɝɨ ɤɨɦɩɨɧɟɧɬɚ – ɭɩɪɨɫɬɢɬɶ ɪɚɛɨɬɭ ɤɨɦɩɨɧɟɧɬɨɜ ɭɱɢɬɟɥɶ ɢ ɤɨɧɬɪɚɫɬɟɪ. ɗɬɨɬ ɤɨɦɩɨɧɟɧɬ ɜɵɩɨɥɧɹɟɬ ɜɫɟɝɨ ɧɟɫɤɨɥɶɤɨ ɡɚɩɪɨɫɨɜ, ɩɪɟɨɛɪɚɡɭɹ ɤɚɠɞɵɣ ɢɡ ɧɢɯ ɜ ɩɨɫɥɟɞɨɜɚɬɟɥɶɧɨɫɬɶ ɡɚɩɪɨɫɨɜ ɤ ɪɚɡɥɢɱɧɵɦ ɤɨɦɩɨɧɟɧɬɚɦ. ȼ ɩɟɪɜɨɣ ɱɚɫɬɢ ɝɥɚɜɵ ɫɨɞɟɪɠɚɬɟɥɶɧɨ ɪɚɫɫɦɨɬɪɟɧɵ ɚɥɝɨɪɢɬɦɵ ɢɫɩɨɥɧɟɧɢɹ ɜɫɟɯ ɡɚɩɪɨɫɨɜ ɢɫɩɨɥɧɢɬɟɥɹ, ɚ ɜɨ ɜɬɨɪɨɣ ɱɚɫɬɢ ɩɪɢɜɟɞɟɧɨ ɢɯ ɮɨɪɦɚɥɶɧɨɟ ɨɩɢɫɚɧɢɟ. Ɉɬɦɟɬɢɦ, ɱɬɨ ɜɜɢɞɭ ɭɧɢɜɟɪɫɚɥɶɧɨɫɬɢ ɤɨɦɩɨɧɟɧɬɚ ɢɫɩɨɥɧɢɬɟɥɶ ɫɬɚɧɞɚɪɬ ɩɟɪɜɨɝɨ ɭɪɨɜɧɹ ɨɬɫɭɬɫɬɜɭɟɬ.
7.1 Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɨɜ ɢɫɩɨɥɧɢɬɟɥɹ. Ʉɚɤ ɛɵɥɨ ɨɩɢɫɚɧɨ ɜ ɝɥɚɜɟ «Ɏɭɧɤɰɢɨɧɚɥɶɧɵɟ ɤɨɦɩɨɧɟɧɬɵ», ɢɫɩɨɥɧɢɬɟɥɶ ɜɵɩɨɥɧɹɟɬ ɱɟɬɵɪɟ ɜɢɞɚ ɡɚɩɪɨɫɨɜ. 1. Ɍɟɫɬɢɪɨɜɚɧɢɟ ɪɟɲɟɧɢɹ ɩɪɢɦɟɪɚ. 2. Ɉɰɟɧɢɜɚɧɢɟ ɪɟɲɟɧɢɹ ɩɪɢɦɟɪɚ. 1. Ɉɰɟɧɢɜɚɧɢɟ ɪɟɲɟɧɢɹ ɩɪɢɦɟɪɚ ɫ ɜɵɱɢɫɥɟɧɢɟɦ ɝɪɚɞɢɟɧɬɚ. 2. Ɉɰɟɧɢɜɚɧɢɟ ɢ ɬɟɫɬɢɪɨɜɚɧɢɟ ɪɟɲɟɧɢɹ ɩɪɢɦɟɪɚ. ȼɫɟ ɩɟɪɟɱɢɫɥɟɧɧɵɟ ɡɚɩɪɨɫɵ ɪɚɛɨɬɚɸɬ ɫ ɬɟɤɭɳɟɣ ɫɟɬɶɸ ɢ ɬɟɤɭɳɢɦ ɩɪɢɦɟɪɨɦ ɡɚɞɚɱɧɢɤɚ. Ɉɞɧɚɤɨ ɤɨɦɩɨɧɟɧɬɭ ɡɚɞɚɱɧɢɤ ɧɟɨɛɯɨɞɢɦɨ ɭɤɚɡɚɬɶ, ɤɚɤɨɣ ɩɪɢɦɟɪ ɩɨɞɥɟɠɢɬ ɨɛɪɚɛɨɬɤɟ. Ʉɪɨɦɟ ɬɨɝɨ, ɜ ɝɥɚɜɟ «Ɉɰɟɧɤɚ ɢ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɨɬɜɟɬɚ» ɜɜɟɞɟɧ ɤɥɚɫɫ ɨɰɟɌɚɛɥɢɰɚ 1 ɧɨɤ, ɜɵɱɢɫɥɹɟɦɵɯ ɩɨ ɜɫɟɦɭ ɨɛɭɱɚɸɳɟɦɭ ɦɧɨɠɟɫɬɜɭ. ɉɚɪɚɦɟɬɪɵ ɡɚɩɪɨɫɚ ɞɥɹ ɩɨɡɚɞɚɱɧɨɣ ɪɚɛɨɬɵ Ɍɚɤɢɟ ɨɰɟɧɤɢ ɩɨɡɜɨɥɹɸɬ ɫɭɳɟɫɬɜɟɧɧɨ ɭɥɭɱɲɢɬɶ ɇɚɡɜɚɧɢɟ ɩɚɪɚɦɟɬɪɚ 1 2 3 4 ɨɛɭɱɚɟɦɨɫɬɶ ɫɟɬɢ ɢ ɭɫɤɨɪɢɬɶ ɟɟ ɨɛɭɱɟɧɢɟ. ɇɟɬ ɫɦɵɫɉɟɪɟɣɬɢ ɤ ɫɥɟɞɭɸɳɟɦɭ ɩɪɢɦɟɪɭ +/– +/– +/– +/– ɥɚ ɜɨɡɥɚɝɚɬɶ ɩɟɪɟɛɨɪ ɩɪɢɦɟɪɨɜ ɧɚ ɭɱɢɬɟɥɹ, ɩɨɫɤɨɥɶɤɭ ɷɬɨ ɫɧɢɠɚɟɬ ɩɨɥɟɡɧɨɫɬɶ ɤɨɦɩɨɧɟɧɬɚ ɢɫɩɨɥɧɢɬɟɥɶ. Ɉɫɬɚɧɨɜɢɬɶɫɹ ɜ ɤɨɧɰɟ ɨɛɭɱɚɸ- +/– +/– +/– +/– Ɍɚɤɢɦ ɨɛɪɚɡɨɦ, ɜɨɡɧɢɤɚɟɬ ɟɳɟ ɱɟɬɵɪɟ ɜɢɞɚ ɡɚɩɪɨɳɟɝɨ ɦɧɨɠɟɫɬɜɚ ɫɨɜ. ȼɵɱɢɫɥɹɬɶ ɨɰɟɧɤɭ – + + + 5. Ɍɟɫɬɢɪɨɜɚɧɢɟ ɪɟɲɟɧɢɹ ɜɫɟɯ ɩɪɢɦɟɪɨɜ ɨɛɭɂɧɬɟɪɩɪɟɬɢɪɨɜɚɬɶ ɨɬɜɟɬ + – – + ɱɚɸɳɟɝɨ ɦɧɨɠɟɫɬɜɚ. ȼɵɱɢɫɥɹɬɶ ɝɪɚɞɢɟɧɬ – – + – 6. Ɉɰɟɧɢɜɚɧɢɟ ɪɟɲɟɧɢɹ ɜɫɟɯ ɩɪɢɦɟɪɨɜ ɨɛɭɉɨɞɝɨɬɨɜɤɚ ɤ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɸ – – +/– – ɱɚɸɳɟɝɨ ɦɧɨɠɟɫɬɜɚ. 5. Ɉɰɟɧɢɜɚɧɢɟ ɪɟɲɟɧɢɹ ɜɫɟɯ ɩɪɢɦɟɪɨɜ ɨɛɭɌɚɛɥɢɰɚ 2 ɱɚɸɳɟɝɨ ɦɧɨɠɟɫɬɜɚ ɫ ɜɵɱɢɫɥɟɧɢɟɦ ɝɪɚɞɢɟɧɉɚɪɚɦɟɬɪɵ ɡɚɩɪɨɫɚ ɞɥɹ ɨɛɭɱɚɸɳɟɝɨ ɦɧɨɠɟɫɬɜɚ ɬɚ. ɜ ɰɟɥɨɦ 6. Ɉɰɟɧɢɜɚɧɢɟ ɢ ɬɟɫɬɢɪɨɜɚɧɢɟ ɪɟɲɟɧɢɹ ɜɫɟɯ ɇɚɡɜɚɧɢɟ ɩɚɪɚɦɟɬɪɚ 5 6 7 8 ɩɪɢɦɟɪɨɜ ɨɛɭɱɚɸɳɟɝɨ ɦɧɨɠɟɫɬɜɚ. ȼɵɱɢɫɥɹɬɶ ɨɰɟɧɤɭ – + + + Ʉɚɤ ɭɠɟ ɨɬɦɟɱɚɥɨɫɶ ɜ ɝɥɚɜɟ «Ɏɭɧɤɰɢɨɧɚɥɶɂɧɬɟɪɩɪɟɬɢɪɨɜɚɬɶ ɨɬɜɟɬ + – – + ɧɵɟ ɤɨɦɩɨɧɟɧɬɵ», ɤɚɠɞɭɸ ɢɡ ɩɪɢɜɟɞɟɧɧɵɯ ɱɟɬɜɟɪɨɤ ȼɵɱɢɫɥɹɬɶ ɝɪɚɞɢɟɧɬ – – + – ɡɚɩɪɨɫɨɜ ɦɨɠɧɨ ɨɛɴɟɞɢɧɢɬɶ ɜ ɨɞɢɧ ɡɚɩɪɨɫ ɫ ɩɚɪɚɉɨɞɝɨɬɨɜɤɚ ɤ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɸ – – +/– – ɦɟɬɪɚɦɢ. ȼ ɬɚɛɥ. 1 ɩɪɢɜɟɞɟɧ ɩɨɥɧɵɣ ɫɩɢɫɨɤ ɩɚɪɚɦɟɬɪɨɜ ɞɥɹ ɩɟɪɜɨɣ ɱɟɬɜɟɪɤɢ Ɍɚɛɥɢɰɚ 3 ɡɚɩɪɨɫɨɜ, ɚ ɜ ɬɚɛɥ. 2 – ɞɥɹ ɉɪɟɞɨɩɪɟɞɟɥɟɧɧɵɟ ɤɨɧɫɬɚɧɬɵ ɤɨɦɩɨɧɟɧɬɚ ɢɫɩɨɥɧɢɬɟɥɶ ɜɬɨɪɨɣ. ɇɚɡɜɚɧɢɟɂɞɟɧɬɢɮɢɤɚɬɨɪɁɧɚɱɟɧɢɟ ɋɢɦɜɨɥ «+» ɨɡɧɚȾɟɫɹɬ. ɒɟɫɬɧ. ɱɚɟɬ, ɱɬɨ ɜ ɡɚɩɪɨɫɟ, ɧɨɦɟɪ ȼɵɱɢɫɥɹɬɶ ɨɰɟɧɤɭ Estimate 1 0001 ɤɨɬɨɪɨɝɨ ɭɤɚɡɚɧ ɜ ɩɟɪɜɨɣ ɫɬɪɨɤɟ ɤɨɥɨɧɤɢ, ɜɨɡɦɨɠɂɧɬɟɪɩɪɟɬɢɪɨɜɚɬɶ ɨɬɜɟɬ Interpret 2 0002 ɧɨɫɬɶ, ɡɚɞɚɜɚɟɦɚɹ ɞɚɧɧɵɦ ȼɵɱɢɫɥɹɬɶ ɝɪɚɞɢɟɧɬ Gradient 4 0004 ɩɚɪɚɦɟɬɪɨɦ, ɞɨɥɠɧɚ ɛɵɬɶ ɉɨɞɝɨɬɨɜɤɚ ɤ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɸ Contrast 8 0008 ɢɫɩɨɥɶɡɨɜɚɧɚ. ɋɢɦɜɨɥ «–» – ɉɟɪɟɣɬɢ ɤ ɫɥɟɞɭɸɳɟɦɭ ɩɪɢɦɟɪɭ NextExample 16 0010 ɱɬɨ ɫɜɹɡɚɧɧɚɹ ɫ ɞɚɧɧɵɦ Ɉɫɬɚɧɨɜɢɬɶɫɹ ɜ ɤɨɧɰɟ ɨɛɭɱɚɸɳɟɝɨ StopOnEnd 32 0020 ɩɚɪɚɦɟɬɪɨɦ ɜɨɡɦɨɠɧɨɫɬɶ ɦɧɨɠɟɫɬɜɚ ɧɟ ɢɫɩɨɥɶɡɭɟɬɫɹ. ɋɢɦɜɨɥɵ ɍɫɬɚɧɚɜɥɢɜɚɬɶ ɨɬɜɟɬɵ PutAnswers 64 0040 «+/–» ɨɡɧɚɱɚɸɬ, ɱɬɨ ɡɚɩɪɨɫ ɍɫɬɚɧɚɜɥɢɜɚɬɶ ɨɰɟɧɤɢ PutEstimations 128 0080 ɦɨɠɟɬ, ɤɚɤ ɢɫɩɨɥɶɡɨɜɚɬɶ, ɍɫɬɚɧɚɜɥɢɜɚɬɶ ɭɜɟɪɟɧɧɨɫɬɶ ɜ ɨɬɜɟɬɟ PutReliability 256 0100 ɬɚɤ ɢ ɧɟ ɢɫɩɨɥɶɡɨɜɚɬɶ ɞɚɧ-
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ɧɭɸ ɜɨɡɦɨɠɧɨɫɬɶ. Ɉɬɦɟɬɢɦ, ɱɬɨ ɩɨɞɝɨɬɨɜɤɚ ɤ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɸ ɦɨɠɟɬ ɛɵɬɶ ɡɚɞɟɣɫɬɜɨɜɚɧɚ, ɬɨɥɶɤɨ ɟɫɥɢ ɩɪɨɢɡɜɨɞɢɬɫɹ ɜɵɱɢɫɥɟɧɢɟ ɝɪɚɞɢɟɧɬɚ, ɚ ɜɵɱɢɫɥɟɧɢɟ ɝɪɚɞɢɟɧɬɚ ɧɟɜɨɡɦɨɠɧɨ ɛɟɡ ɜɵɱɢɫɥɟɧɢɹ ɨɰɟɧɤɢ. Ɉɫɬɚɥɶɧɵɟ ɩɚɪɚɦɟɬɪɵ ɧɟɡɚɜɢɫɢɦɵ. Ɉɬɛɨɪ ɩɪɢɦɟɪɨɜ ɜ ɨɛɭɱɚɸɳɟɟ ɦɧɨɠɟɫɬɜɨ, ɨɬɤɪɵɬɢɟ ɫɟɚɧɫɚ ɪɚɛɨɬɵ ɫ ɡɚɞɚɱɧɢɤɨɦ ɞɨɥɠɧɵ ɜɵɩɨɥɧɹɬɶɫɹ ɭɱɢɬɟɥɟɦ ɢɥɢ ɤɨɧɬɪɚɫɬɟɪɨɦ. ɂɫɩɨɥɧɢɬɟɥɶ ɬɨɥɶɤɨ ɨɪɝɚɧɢɡɭɟɬ ɩɟɪɟɛɨɪ ɩɪɢɦɟɪɨɜ ɜ ɨɛɭɱɚɸɳɟɦ ɦɧɨɠɟɫɬɜɟ.
7.2 ɋɬɚɧɞɚɪɬ ɤɨɦɩɨɧɟɧɬɚ ɢɫɩɨɥɧɢɬɟɥɶ ɜɬɨɪɨɝɨ ɭɪɨɜɧɹ ȼ ɞɚɧɧɨɦ ɪɚɡɞɟɥɟ ɨɩɢɫɚɧɵ ɡɚɩɪɨɫɵ ɢɫɩɨɥɧɢɬɟɥɹ ɫ ɚɥɝɨɪɢɬɦɚɦɢ ɢɯ ɢɫɩɨɥɧɟɧɢɹ. ɉɪɢ ɨɩɢɫɚɧɢɢ ɡɚɩɪɨɫɨɜ ɢɫɩɨɥɶɡɭɟɬɫɹ ɚɪɝɭɦɟɧɬ Instruct, ɹɜɥɹɸɳɢɣɫɹ ɰɟɥɵɦ ɱɢɫɥɨɦ, ɩɪɢɧɢɦɚɸɳɢɦ ɡɧɚɱɟɧɢɟ ɨɞɧɨɣ ɢɡ ɩɪɟɞɨɩɪɟɞɟɥɟɧɧɵɯ ɤɨɧɫɬɚɧɬ, ɩɪɢɜɟɞɟɧɧɵɯ ɜ ɬɚɛɥ. 3., ɢɥɢ ɫɭɦɦɵ ɥɸɛɨɝɨ ɱɢɫɥɚ ɷɬɢɯ ɤɨɧɫɬɚɧɬ. Ⱥɪɝɭɦɟɧɬ Instruct ɹɜɥɹɟɬɫɹ ɫɨɜɨɤɭɩɧɨɫɬɶɸ ɲɟɫɬɢ ɛɢɬɨɜɵɯ ɮɥɚɝɨɜ. ȼ ɡɚɩɪɨɫɚɯ ɧɟ ɭɤɚɡɵɜɚɸɬɫɹ ɢɫɩɨɥɶɡɭɟɦɵɟ ɫɟɬɶ, ɨɰɟɧɤɚ ɢ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɨɬɜɟɬɚ, ɩɨɫɤɨɥɶɤɭ ɤɨɦɩɨɧɟɧɬ ɢɫɩɨɥɧɢɬɟɥɶ ɜɫɟɝɞɚ ɢɫɩɨɥɶɡɭɟɬ ɬɟɤɭɳɢɟ ɫɟɬɶ, ɨɰɟɧɤɭ ɢ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɨɬɜɟɬɚ.
7.2.1 ɉɨɡɚɞɚɱɧɚɹ ɨɛɪɚɛɨɬɤɚ (TaskWork) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function TaskWork(Instruct, Handle : Integer; Var Answers, Reliability : PRealArray; Var Estim : Real) : Logic; C: Logic TaskWork(Integer Instruct, Integer Handle, PRealArray* Answers, PRealArray* Reliability; Real* Estim) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: Instruct – ɫɨɞɟɪɠɢɬ ɢɧɫɬɪɭɤɰɢɢ ɨ ɫɩɨɫɨɛɟ ɢɫɩɨɥɧɟɧɢɹ. Handle – ɧɨɦɟɪ ɫɟɚɧɫɚ ɜ ɡɚɞɚɱɧɢɤɟ. Answers – ɭɤɚɡɚɬɟɥɶ ɧɚ ɦɚɫɫɢɜ ɜɵɱɢɫɥɟɧɧɵɯ ɨɬɜɟɬɨɜ. Reliability – ɭɤɚɡɚɬɟɥɶ ɧɚ ɦɚɫɫɢɜ ɤɨɷɮɮɢɰɢɟɧɬɨɜ ɭɜɟɪɟɧɧɨɫɬɢ ɫɟɬɢ ɜ ɨɬɜɟɬɚɯ. Estim – ɨɰɟɧɤɚ ɪɟɲɟɧɢɹ ɩɪɢɦɟɪɚ. ɇɚɡɧɚɱɟɧɢɟ – ɩɪɨɢɡɜɨɞɢɬ ɨɛɪɚɛɨɬɤɭ ɨɞɧɨɝɨ ɩɪɢɦɟɪɚ. ɉɟɪɟɦɟɧɧɵɟ, ɢɫɩɨɥɶɡɭɟɦɵɟ ɩɪɢ ɢɫɩɨɥɧɟɧɢɢ ɡɚɩɪɨɫɚ InArray, RelArray – ɚɞɪɟɫɚ ɦɚɫɫɢɜɨɜ ɞɥɹ ɨɛɦɟɧɨɜ ɫ ɡɚɞɚɱɧɢɤɨɦ. Back – ɚɞɪɟɫ ɦɚɫɫɢɜɚ ɞɥɹ ɨɛɦɟɧɨɜ ɫ ɨɰɟɧɤɨɣ. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. ȿɫɥɢ ɜ ɥɸɛɨɣ ɦɨɦɟɧɬ ɢɫɩɨɥɧɟɧɢɹ ɡɚɩɪɨɫɚ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ ɩɪɢ ɢɫɩɨɥɧɟɧɢɢ ɡɚɩɪɨɫɨɜ ɤ ɞɪɭɝɢɦ ɤɨɦɩɨɧɟɧɬɚɦ, ɬɨ ɢɫɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ, ɜɨɡɜɪɚɳɚɟɬɫɹ ɡɧɚɱɟɧɢɟ ɥɨɠɶ, ɨɲɢɛɤɚ ɤɨɦɩɨɧɟɧɬɚ ɢɫɩɨɥɧɢɬɟɥɶ ɧɟ ɝɟɧɟɪɢɪɭɟɬɫɹ. 1. ȿɫɥɢ ɜ ɚɪɝɭɦɟɧɬɟ Instruct ɭɫɬɚɧɨɜɥɟɧ ɛɢɬ Gradient ɢ ɧɟ ɭɫɬɚɧɨɜɥɟɧ ɛɢɬ Estimate, ɬɨ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ, ɢ ɝɟɧɟɪɢɪɭɟɬɫɹ ɨɲɢɛɤɚ 001 – ɇɟɤɨɪɪɟɤɬɧɨɟ ɫɨɱɟɬɚɧɢɟ ɮɥɚɝɨɜ ɜ ɚɪɝɭɦɟɧɬɟ Instruct. 2. ȿɫɥɢ ɜ ɚɪɝɭɦɟɧɬɟ Instruct ɭɫɬɚɧɨɜɥɟɧ ɛɢɬ Gradient, ɬɨ ɝɟɧɟɪɢɪɭɟɬɫɹ ɡɚɩɪɨɫ ɤ ɫɟɬɢ NullGradient ɫ ɚɪɝɭɦɟɧɬɨɦ Null. 3. ȿɫɥɢ ɜ ɚɪɝɭɦɟɧɬɟ Instruct ɭɫɬɚɧɨɜɥɟɧ ɛɢɬ NextExample, ɬɨ ɝɟɧɟɪɢɪɭɟɬɫɹ ɡɚɩɪɨɫ ɤ ɡɚɞɚɱɧɢɤɭ Next ɫ ɚɪɝɭɦɟɧɬɨɦ Handle. (ɉɟɪɟɯɨɞ ɤ ɫɥɟɞɭɸɳɟɦɭ ɩɪɢɦɟɪɭ) 4. Ƚɟɧɟɪɢɪɭɟɬɫɹ ɡɚɩɪɨɫ ɤ ɡɚɞɚɱɧɢɤɭ Last ɫ ɚɪɝɭɦɟɧɬɨɦ Handle. (ɉɪɨɜɟɪɤɚ, ɫɭɳɟɫɬɜɭɟɬ ɥɢ ɩɪɢɦɟɪ) 5. ȿɫɥɢ ɡɚɩɪɨɫ Last ɜɟɪɧɭɥ ɡɧɚɱɟɧɢɟ ɢɫɬɢɧɚ, ɬɨ 5.1. ȿɫɥɢ ɜ ɚɪɝɭɦɟɧɬɟ Instruct ɭɫɬɚɧɨɜɥɟɧ ɛɢɬ StopOnEnd, ɬɨ ɢɫɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ, ɜɨɡɜɪɚɳɚɟɬɫɹ ɡɧɚɱɟɧɢɟ ɥɨɠɶ. (ɉɪɢɦɟɪɚ ɧɟɬ, ɩɟɪɟɯɨɞ ɧɚ ɧɚɱɚɥɨ ɧɟ ɧɭɠɟɧ) 5.2. Ƚɟɧɟɪɢɪɭɟɬɫɹ ɡɚɩɪɨɫ ɤ ɡɚɞɚɱɧɢɤɭ Home ɫ ɚɪɝɭɦɟɧɬɨɦ Handle. (ɉɟɪɟɯɨɞ ɧɚ ɧɚɱɚɥɨ ɨɛɭɱɚɸɳɟɝɨ ɦɧɨɠɟɫɬɜɚ) 6. ɉɟɪɟɦɟɧɧɨɣ InArray ɩɪɢɫɜɚɢɜɚɟɬɫɹ ɡɧɚɱɟɧɢɟ Null ɢ ɝɟɧɟɪɢɪɭɟɬɫɹ ɡɚɩɪɨɫ ɤ ɡɚɞɚɱɧɢɤɭ Get ɫ ɚɪɝɭɦɟɧɬɚɦɢ Handle, InArray, tbPrepared (ɉɨɥɭɱɚɟɬ ɨɬ ɡɚɞɚɱɧɢɤɚ ɩɪɟɞɨɛɪɚɛɨɬɚɧɧɵɟ ɜɯɨɞɧɵɟ ɫɢɝɧɚɥɵ) 7. Ƚɟɧɟɪɢɪɭɟɬɫɹ ɡɚɩɪɨɫ ɤ ɫɟɬɢ Forw, ɫ ɚɪɝɭɦɟɧɬɚɦɢ Null, InArray (ɜɵɩɨɥɧɹɟɬɫɹ ɩɪɹɦɨɟ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɟ ɫɟɬɢ). 8. Ɉɫɜɨɛɨɠɞɚɟɬɫɹ ɦɚɫɫɢɜ InArray
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13. 14. 15. 16.
ɉɪɢɫɜɚɢɜɚɟɬ ɩɟɪɟɦɟɧɧɨɣ Data ɡɧɚɱɟɧɢɟ Null ɢ ɝɟɧɟɪɢɪɭɟɬ ɡɚɩɪɨɫ ɤ ɫɟɬɢ GetNetData ɫ ɚɪɝɭɦɟɧɬɚɦɢ Null, OutSignals, Data (ɉɨɥɭɱɚɟɬ ɨɬ ɫɟɬɢ ɜɵɯɨɞɧɵɟ ɫɢɝɧɚɥɵ). ȿɫɥɢ ɜ ɚɪɝɭɦɟɧɬɟ Instruct ɭɫɬɚɧɨɜɥɟɧ ɛɢɬ Interpret, ɬɨ 10.1. Ƚɟɧɟɪɢɪɭɟɬɫɹ ɡɚɩɪɨɫ ɤ ɢɧɬɟɪɩɪɟɬɚɬɨɪɭ ɨɬɜɟɬɚ Interpretate ɫ ɚɪɝɭɦɟɧɬɚɦɢ Data, Answers, Reliability. (ɉɪɨɢɡɜɨɞɢɬ ɢɧɬɟɪɩɪɟɬɚɰɢɸ ɨɬɜɟɬɚ) 10.2. ȿɫɥɢ ɜ ɚɪɝɭɦɟɧɬɟ Instruct ɭɫɬɚɧɨɜɥɟɧ ɛɢɬ PutAnswers, ɬɨ ɝɟɧɟɪɢɪɭɟɬɫɹ ɡɚɩɪɨɫ ɤ ɡɚɞɚɱɧɢɤɭ Put ɫ ɚɪɝɭɦɟɧɬɚɦɢ Handle, Answers, tbCalcAnswers (ɉɟɪɟɞɚɟɬ ɡɚɞɚɱɧɢɤɭ ɜɵɱɢɫɥɟɧɧɵɟ ɨɬɜɟɬɵ) 10.3. ȿɫɥɢ ɜ ɚɪɝɭɦɟɧɬɟ Instruct ɭɫɬɚɧɨɜɥɟɧ ɛɢɬ PutReliability, ɬɨ ɝɟɧɟɪɢɪɭɟɬɫɹ ɡɚɩɪɨɫ ɤ ɡɚɞɚɱɧɢɤɭ Put ɫ ɚɪɝɭɦɟɧɬɚɦɢ Handle, Reliability, tbCalcReliability (ɉɟɪɟɞɚɟɬ ɡɚɞɚɱɧɢɤɭ ɜɵɱɢɫɥɟɧɧɵɟ ɤɨɷɮɮɢɰɢɟɧɬɵ ɭɜɟɪɟɧɧɨɫɬɢ ɜ ɨɬɜɟɬɟ) ȿɫɥɢ ɜ ɚɪɝɭɦɟɧɬɟ Instruct ɭɫɬɚɧɨɜɥɟɧ ɛɢɬ Gradient, ɬɨ ɫɨɡɞɚɟɬɫɹ ɦɚɫɫɢɜ Back ɬɨɝɨ ɠɟ ɪɚɡɦɟɪɚ, ɱɬɨ ɢ Data. ȼ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ ɩɟɪɟɦɟɧɧɨɣ Back ɩɪɢɫɜɚɢɜɚɟɬɫɹ ɡɧɚɱɟɧɢɟ Null. ȿɫɥɢ ɜ ɚɪɝɭɦɟɧɬɟ Instruct ɭɫɬɚɧɨɜɥɟɧ ɛɢɬ Estimate, ɬɨ 12.1. ɉɟɪɟɦɟɧɧɨɣ InArray ɩɪɢɫɜɚɢɜɚɟɬɫɹ ɡɧɚɱɟɧɢɟ Null ɢ ɝɟɧɟɪɢɪɭɟɬɫɹ ɡɚɩɪɨɫ ɤ ɡɚɞɚɱɧɢɤɭ Get ɫ ɚɪɝɭɦɟɧɬɚɦɢ Handle, InArray, tbAnswers (ɉɨɥɭɱɚɟɬ ɨɬ ɡɚɞɚɱɧɢɤɚ ɩɪɚɜɢɥɶɧɵɟ ɨɬɜɟɬɵ) 12.2. ɉɟɪɟɦɟɧɧɨɣ RelArray ɩɪɢɫɜɚɢɜɚɟɬɫɹ ɡɧɚɱɟɧɢɟ Null ɢ ɝɟɧɟɪɢɪɭɟɬɫɹ ɡɚɩɪɨɫ ɤ ɡɚɞɚɱɧɢɤɭ Get ɫ ɚɪɝɭɦɟɧɬɚɦɢ Handle, RelArray, tbCalcReliability(ɉɨɥɭɱɚɟɬ ɨɬ ɡɚɞɚɱɧɢɤɚ ɞɨɫɬɨɜɟɪɧɨɫɬɢ ɨɬɜɟɬɨɜ) 12.3. Ƚɟɧɟɪɢɪɭɟɬɫɹ ɡɚɩɪɨɫ ɤ ɨɰɟɧɤɟ Estimate ɫ ɚɪɝɭɦɟɧɬɚɦɢ Data, Back, InArray, RelArray, Direv, Estim. ȼɦɟɫɬɨ Direv ɩɟɪɟɞɚɟɬɫɹ ɧɨɥɶ, ɟɫɥɢ ɜ ɚɪɝɭɦɟɧɬɟ Instruct ɭɫɬɚɧɨɜɥɟɧ ɛɢɬ Gradient, ɢ 1 ɜ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ. (ȼɵɱɢɫɥɹɟɬ ɨɰɟɧɤɭ ɩɪɢɦɟɪɚ ɢ, ɜɨɡɦɨɠɧɨ, ɩɪɨɢɡɜɨɞɧɵɟ) 12.4. ȿɫɥɢ ɜ ɚɪɝɭɦɟɧɬɟ Instruct ɭɫɬɚɧɨɜɥɟɧ ɛɢɬ PutEstimations, ɬɨ ɝɟɧɟɪɢɪɭɟɬɫɹ ɡɚɩɪɨɫ ɤ ɡɚɞɚɱɧɢɤɭ Put ɫ ɚɪɝɭɦɟɧɬɚɦɢ Handle, Estim, tbEstimations (ɉɟɪɟɞɚɟɬ ɡɚɞɚɱɧɢɤɭ ɨɰɟɧɤɭ ɩɪɢɦɟɪɚ) 12.5. Ɉɫɜɨɛɨɠɞɚɟɬ ɦɚɫɫɢɜɵ InArray ɢ RelArray. ȿɫɥɢ ɜ ɚɪɝɭɦɟɧɬɟ Instruct ɭɫɬɚɧɨɜɥɟɧ ɛɢɬ Gradient, ɬɨ ɝɟɧɟɪɢɪɭɟɬɫɹ ɡɚɩɪɨɫ ɤ ɫɟɬɢ Back, ɫ ɚɪɝɭɦɟɧɬɚɦɢ Null, Back. Ɉɫɜɨɛɨɠɞɚɟɬ ɦɚɫɫɢɜ Back. (ȼɵɩɨɥɧɹɟɬɫɹ ɨɛɪɚɬɧɨɟ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɟ ɫɟɬɢ) Ɉɫɜɨɛɨɠɞɚɟɬɫɹ ɦɚɫɫɢɜ Data. ȿɫɥɢ ɜ ɚɪɝɭɦɟɧɬɟ Instruct ɭɫɬɚɧɨɜɥɟɧ ɛɢɬ Contrast, ɬɨ ɝɟɧɟɪɢɪɭɟɬɫɹ ɡɚɩɪɨɫ ɤ ɤɨɧɬɪɚɫɬɟɪɭ ContrastExample ɫ ɚɪɝɭɦɟɧɬɨɦ ɢɫɬɢɧɚ. Ɂɚɜɟɪɲɚɟɬ ɢɫɩɨɥɧɟɧɢɟ, ɜɨɡɜɪɚɳɚɹ ɡɧɚɱɟɧɢɟ ɢɫɬɢɧɚ
7.2.2 Ɉɛɪɚɛɨɬɤɚ ɨɛɭɱɚɸɳɟɝɨ ɦɧɨɠɟɫɬɜɚ (TaskSetWork) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function TaskSetWork(Instruct, Handle : Integer; Var Tasks : Integer; Var Correct : PRealArray; Var Estim : Real) : Logic; C: Logic TaskSetWork(Integer Instruct, Integer Handle, Integer* Tasks, PRealArray* Correct, Real* Estim) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: Instruct – ɫɨɞɟɪɠɢɬ ɢɧɫɬɪɭɤɰɢɢ ɨ ɫɩɨɫɨɛɟ ɢɫɩɨɥɧɟɧɢɹ. Handle – ɧɨɦɟɪ ɫɟɚɧɫɚ ɜ ɡɚɞɚɱɧɢɤɟ. Tasks – ɱɢɫɥɨ ɩɪɢɦɟɪɨɜ ɜ ɨɛɭɱɚɸɳɟɦ ɦɧɨɠɟɫɬɜɟ. Correct – ɭɤɚɡɚɬɟɥɶ ɧɚ ɦɚɫɫɢɜ, ɩɟɪɜɵɣ ɷɥɟɦɟɧɬ ɤɨɬɨɪɨɝɨ ɪɚɜɟɧ ɱɢɫɥɭ ɩɪɚɜɢɥɶɧɵɯ ɨɬɜɟɬɨɜ ɧɚ ɩɟɪɜɭɸ ɩɨɞɡɚɞɚɱɭ ɢ ɬ.ɞ. Estim – ɫɪɟɞɧɹɹ ɨɰɟɧɤɚ ɪɟɲɟɧɢɹ ɜɫɟɯ ɩɪɢɦɟɪɨɜ ɨɛɭɱɚɸɳɟɝɨ ɦɧɨɠɟɫɬɜɚ. ɇɚɡɧɚɱɟɧɢɟ – ɩɪɨɢɡɜɨɞɢɬ ɨɛɪɚɛɨɬɤɭ ɜɫɟɯ ɩɪɢɦɟɪɨɜ ɨɛɭɱɚɸɳɟɝɨ ɦɧɨɠɟɫɬɜɚ. ɉɟɪɟɦɟɧɧɵɟ, ɢɫɩɨɥɶɡɭɟɦɵɟ ɩɪɢ ɢɫɩɨɥɧɟɧɢɢ ɡɚɩɪɨɫɚ InArray, AnsArray, RelArray – ɚɞɪɟɫɚ ɦɚɫɫɢɜɨɜ ɞɥɹ ɨɛɦɟɧɨɜ ɫ ɡɚɞɚɱɧɢɤɨɦ. Answers – ɭɤɚɡɚɬɟɥɶ ɧɚ ɦɚɫɫɢɜ ɜɵɱɢɫɥɟɧɧɵɯ ɨɬɜɟɬɨɜ. Reliability – ɭɤɚɡɚɬɟɥɶ ɧɚ ɦɚɫɫɢɜ ɤɨɷɮɮɢɰɢɟɧɬɨɜ ɭɜɟɪɟɧɧɨɫɬɢ ɫɟɬɢ ɜ ɨɬɜɟɬɚɯ. Back – ɚɞɪɟɫ ɦɚɫɫɢɜɚ ɞɥɹ ɨɛɦɟɧɨɜ ɫ ɨɰɟɧɤɨɣ. Work – ɪɚɛɨɱɚɹ ɩɟɪɟɦɟɧɧɚɹ ɬɢɩɚ Real ɞɥɹ ɩɨɞɫɱɟɬɚ ɫɭɦɦɚɪɧɨɣ ɨɰɟɧɤɢ. Weight – ɪɚɛɨɱɚɹ ɩɟɪɟɦɟɧɧɚɹ ɬɢɩɚ Real ɞɥɹ ɜɟɫɚ ɩɪɢɦɟɪɚ. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ.
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ȿɫɥɢ ɜ ɥɸɛɨɣ ɦɨɦɟɧɬ ɢɫɩɨɥɧɟɧɢɹ ɡɚɩɪɨɫɚ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ ɩɪɢ ɢɫɩɨɥɧɟɧɢɢ ɡɚɩɪɨɫɨɜ ɤ ɞɪɭɝɢɦ ɤɨɦɩɨɧɟɧɬɚɦ, ɬɨ ɢɫɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ, ɨɫɜɨɛɨɠɞɚɸɬɫɹ ɜɫɟ ɫɨɡɞɚɧɧɵɟ ɜ ɧɟɦ ɦɚɫɫɢɜɵ, ɜɨɡɜɪɚɳɚɟɬɫɹ ɡɧɚɱɟɧɢɟ ɥɨɠɶ, ɨɲɢɛɤɚ ɤɨɦɩɨɧɟɧɬɚ ɢɫɩɨɥɧɢɬɟɥɶ ɧɟ ɝɟɧɟɪɢɪɭɟɬɫɹ. Ɂɧɚɱɟɧɢɟ ɛɢɬ NextExample ɢ StopOnEnd ɜ ɚɪɝɭɦɟɧɬɟ Instruct ɢɝɧɨɪɢɪɭɸɬɫɹ. 1. ȿɫɥɢ ɜ ɚɪɝɭɦɟɧɬɟ Instruct ɭɫɬɚɧɨɜɥɟɧ ɛɢɬ Gradient ɢ ɧɟ ɭɫɬɚɧɨɜɥɟɧ ɛɢɬ Estimate, ɬɨ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ, ɢ ɝɟɧɟɪɢɪɭɟɬɫɹ ɨɲɢɛɤɚ 001 – ɇɟɤɨɪɪɟɤɬɧɨɟ ɫɨɱɟɬɚɧɢɟ ɮɥɚɝɨɜ ɜ ɚɪɝɭɦɟɧɬɟ Instruct. 2. ȿɫɥɢ ɜ ɚɪɝɭɦɟɧɬɟ Instruct ɭɫɬɚɧɨɜɥɟɧ ɛɢɬ Interpret, ɬɨ ɫɨɡɞɚɸɬɫɹ ɦɚɫɫɢɜɵ Answers ɢ Reliability ɬɨɝɨ ɠɟ ɪɚɡɦɟɪɚ, ɱɬɨ ɢ Correct 3. ȼɵɩɨɥɧɹɟɬɫɹ ɫɥɟɞɭɸɳɢɣ ɮɪɚɝɦɟɧɬ ɩɪɨɝɪɚɦɦɵ (Ɉɛɧɭɥɟɧɢɟ ɦɚɫɫɢɜɚ ɤɨɥɢɱɟɫɬɜ ɩɪɚɜɢɥɶɧɵɯ ɨɬɜɟɬɨɜ) 3.1. For I = 1 To TLong(Correct[0]) Do 3.2. Correct[I] = 0 4. Ɉɛɧɭɥɹɟɦ ɫɱɟɬɱɢɤ ɱɢɫɥɚ ɩɪɢɦɟɪɨɜ: Tasks = 0 5. Ɉɛɧɭɥɹɟɦ ɫɭɦɦɚɪɧɭɸ ɨɰɟɧɤɭ: Work = 0 6. ɉɟɪɟɦɟɧɧɨɣ Back ɩɪɢɫɜɚɢɜɚɟɬɫɹ ɡɧɚɱɟɧɢɟ Null. 7. ɉɪɢɫɜɚɢɜɚɟɬ ɩɟɪɟɦɟɧɧɨɣ Data ɡɧɚɱɟɧɢɟ Null ɢ ɝɟɧɟɪɢɪɭɟɬ ɡɚɩɪɨɫ ɤ ɫɟɬɢ GetNetData ɫ ɚɪɝɭɦɟɧɬɚɦɢ Null, OutSignals, Data. (ɉɨɥɭɱɚɟɬ ɨɬ ɫɟɬɢ ɜɵɯɨɞɧɵɟ ɫɢɝɧɚɥɵ, ɞɥɹ ɜɵɹɫɧɟɧɢɹ ɪɚɡɦɟɪɧɨɫɬɢ ɦɚɫɫɢɜɚ Data. ɋɚɦɢ ɡɧɚɱɟɧɢɹ ɫɢɝɧɚɥɨɜ ɧɟ ɧɭɠɧɵ) 8. ȿɫɥɢ ɜ ɚɪɝɭɦɟɧɬɟ Instruct ɭɫɬɚɧɨɜɥɟɧ ɛɢɬ Gradient, ɬɨ 8.1. Ƚɟɧɟɪɢɪɭɟɬɫɹ ɡɚɩɪɨɫ ɤ ɫɟɬɢ NullGradient ɫ ɚɪɝɭɦɟɧɬɨɦ Null. 8.2. ɋɨɡɞɚɟɬɫɹ ɦɚɫɫɢɜ Back ɬɨɝɨ ɠɟ ɪɚɡɦɟɪɚ, ɱɬɨ ɢ Data. 9. Ƚɟɧɟɪɢɪɭɟɬɫɹ ɡɚɩɪɨɫ ɤ ɡɚɞɚɱɧɢɤɭ Home ɫ ɚɪɝɭɦɟɧɬɨɦ Handle. (ɉɟɪɟɯɨɞ ɧɚ ɧɚɱɚɥɨ ɨɛɭɱɚɸɳɟɝɨ ɦɧɨɠɟɫɬɜɚ) 10. ɉɟɪɟɦɟɧɧɨɣ InArray ɩɪɢɫɜɚɢɜɚɟɬɫɹ ɡɧɚɱɟɧɢɟ Null ɢ ɝɟɧɟɪɢɪɭɟɬɫɹ ɡɚɩɪɨɫ ɤ ɡɚɞɚɱɧɢɤɭ Get ɫ ɚɪɝɭɦɟɧɬɚɦɢ Handle, InArray, tbPrepared (ɋɨɡɞɚɟɦ ɦɚɫɫɢɜ InArray ɞɥɹ ɩɨɥɭɱɟɧɢɹ ɨɬ ɡɚɞɚɱɧɢɤɚ ɩɪɟɞɨɛɪɚɛɨɬɚɧɧɵɯ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ) 11. ɉɟɪɟɦɟɧɧɨɣ AnsArray ɩɪɢɫɜɚɢɜɚɟɬɫɹ ɡɧɚɱɟɧɢɟ Null ɢ ɝɟɧɟɪɢɪɭɟɬɫɹ ɡɚɩɪɨɫ ɤ ɡɚɞɚɱɧɢɤɭ Get ɫ ɚɪɝɭɦɟɧɬɚɦɢ Handle, AnsArray, tbAnswers (ɋɨɡɞɚɟɦ ɦɚɫɫɢɜ AnsArray ɞɥɹ ɩɨɥɭɱɟɧɢɹ ɨɬ ɡɚɞɚɱɧɢɤɚ ɩɪɚɜɢɥɶɧɵɯ ɨɬɜɟɬɨɜ) 12. ȿɫɥɢ ɜ ɚɪɝɭɦɟɧɬɟ Instruct ɭɫɬɚɧɨɜɥɟɧ ɛɢɬ Estimate, ɬɨ ɫɨɡɞɚɟɬɫɹ ɦɚɫɫɢɜ RelArray ɬɨɝɨ ɠɟ ɪɚɡɦɟɪɚ, ɱɬɨ ɢ AnsArray. 13. Ƚɟɧɟɪɢɪɭɟɬɫɹ ɡɚɩɪɨɫ ɤ ɡɚɞɚɱɧɢɤɭ Last ɫ ɚɪɝɭɦɟɧɬɨɦ Handle. (ɉɪɨɜɟɪɤɚ, ɫɭɳɟɫɬɜɭɟɬ ɥɢ ɩɪɢɦɟɪ) 14. ȿɫɥɢ ɡɚɩɪɨɫ Last ɜɟɪɧɭɥ ɡɧɚɱɟɧɢɟ ɥɨɠɶ, ɬɨ 14.1. Tasks = Tasks + 1 14.2. Ƚɟɧɟɪɢɪɭɟɬɫɹ ɡɚɩɪɨɫ ɤ ɡɚɞɚɱɧɢɤɭ Get ɫ ɚɪɝɭɦɟɧɬɚɦɢ Handle, InArray, tbPrepared (ɉɨɥɭɱɚɟɬ ɨɬ ɡɚɞɚɱɧɢɤɚ ɩɪɟɞɨɛɪɚɛɨɬɚɧɧɵɟ ɜɯɨɞɧɵɟ ɫɢɝɧɚɥɵ) 14.3. Ƚɟɧɟɪɢɪɭɟɬɫɹ ɡɚɩɪɨɫ ɤ ɫɟɬɢ Forw, ɫ ɚɪɝɭɦɟɧɬɚɦɢ Null, InArray. (ȼɵɩɨɥɧɹɟɬɫɹ ɩɪɹɦɨɟ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɟ ɫɟɬɢ) 14.4. Ƚɟɧɟɪɢɪɭɟɬ ɡɚɩɪɨɫ ɤ ɫɟɬɢ GetNetData ɫ ɚɪɝɭɦɟɧɬɚɦɢ Null, OutSignals, Data. (ɉɨɥɭɱɚɟɬ ɨɬ ɫɟɬɢ ɜɵɯɨɞɧɵɟ ɫɢɝɧɚɥɵ) 14.5. ȿɫɥɢ ɜ ɚɪɝɭɦɟɧɬɟ Instruct ɭɫɬɚɧɨɜɥɟɧ ɛɢɬ Interpret, ɬɨ 14.5.1. Ƚɟɧɟɪɢɪɭɟɬɫɹ ɡɚɩɪɨɫ ɤ ɢɧɬɟɪɩɪɟɬɚɬɨɪɭ ɨɬɜɟɬɚ Interpretate ɫ ɚɪɝɭɦɟɧɬɚɦɢ Data, Answers, Reliability. (ɉɪɨɢɡɜɨɞɢɬ ɢɧɬɟɪɩɪɟɬɚɰɢɸ ɨɬɜɟɬɚ) 14.5.2. ȿɫɥɢ ɜ ɚɪɝɭɦɟɧɬɟ Instruct ɭɫɬɚɧɨɜɥɟɧ ɛɢɬ PutAnswers, ɬɨ ɝɟɧɟɪɢɪɭɟɬɫɹ ɡɚɩɪɨɫ ɤ ɡɚɞɚɱɧɢɤɭ Put ɫ ɚɪɝɭɦɟɧɬɚɦɢ Handle, Answers, tbCalcAnswers (ɉɟɪɟɞɚɟɬ ɡɚɞɚɱɧɢɤɭ ɜɵɱɢɫɥɟɧɧɵɟ ɨɬɜɟɬɵ) 14.5.3. ȿɫɥɢ ɜ ɚɪɝɭɦɟɧɬɟ Instruct ɭɫɬɚɧɨɜɥɟɧ ɛɢɬ PutReliability, ɬɨ ɝɟɧɟɪɢɪɭɟɬɫɹ ɡɚɩɪɨɫ ɤ ɡɚɞɚɱɧɢɤɭ Put ɫ ɚɪɝɭɦɟɧɬɚɦɢ Handle, Reliability, tbCalcReliability (ɉɟɪɟɞɚɟɬ ɡɚɞɚɱɧɢɤɭ ɜɵɱɢɫɥɟɧɧɵɟ ɤɨɷɮɮɢɰɢɟɧɬɵ ɭɜɟɪɟɧɧɨɫɬɢ ɜ ɨɬɜɟɬɟ) 14.5.4. Ƚɟɧɟɪɢɪɭɟɬɫɹ ɡɚɩɪɨɫ ɤ ɡɚɞɚɱɧɢɤɭ Get ɫ ɚɪɝɭɦɟɧɬɚɦɢ Handle, AnsArray, tbAnswers (ɉɨɥɭɱɚɟɬ ɨɬ ɡɚɞɚɱɧɢɤɚ ɩɪɚɜɢɥɶɧɵɟ ɨɬɜɟɬɵ) 14.5.5. ȼɵɩɨɥɧɹɟɬɫɹ ɫɥɟɞɭɸɳɢɣ ɮɪɚɝɦɟɧɬ ɩɪɨɝɪɚɦɦɵ (ɉɨɞɫɱɢɬɵɜɚɸɬɫɹ ɩɪɚɜɢɥɶɧɨ ɩɨɥɭɱɟɧɧɵɟ ɨɬɜɟɬɵ) 14.5.5.1.For I = 1 To TLong(Correct[0]) Do 14.5.5.2. If Answers[I] = AnsArray[I] Then TLong(Correct[I]) = TLong(Correct[I]) + 1 14.6. ȿɫɥɢ ɜ ɚɪɝɭɦɟɧɬɟ Instruct ɭɫɬɚɧɨɜɥɟɧ ɛɢɬ Estimate, ɬɨ 14.6.1. ȿɫɥɢ ɜ ɚɪɝɭɦɟɧɬɟ Instruct ɧɟ ɭɫɬɚɧɨɜɥɟɧ ɛɢɬ Interpret, ɬɨ ɝɟɧɟɪɢɪɭɟɬɫɹ ɡɚɩɪɨɫ ɤ ɡɚɞɚɱɧɢɤɭ Get ɫ ɚɪɝɭɦɟɧɬɚɦɢ Handle, AnsArray, tbAnswers (ɉɨɥɭɱɚɟɬ ɨɬ ɡɚɞɚɱɧɢɤɚ ɩɪɚɜɢɥɶɧɵɟ ɨɬɜɟɬɵ)
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Ƚɟɧɟɪɢɪɭɟɬɫɹ ɡɚɩɪɨɫ ɤ ɡɚɞɚɱɧɢɤɭ Get ɫ ɚɪɝɭɦɟɧɬɚɦɢ Handle, RelArray, tbCalcReliability (ɉɨɥɭɱɚɟɬ ɨɬ ɡɚɞɚɱɧɢɤɚ ɞɨɫɬɨɜɟɪɧɨɫɬɢ ɨɬɜɟɬɨɜ) 14.6.3. Ƚɟɧɟɪɢɪɭɟɬɫɹ ɡɚɩɪɨɫ ɤ ɨɰɟɧɤɟ Estimate ɫ ɚɪɝɭɦɟɧɬɚɦɢ Data, Back, AnsArray, RelArray, Direv, Estim. ȼɦɟɫɬɨ Direv ɩɟɪɟɞɚɟɬɫɹ ɧɨɥɶ, ɟɫɥɢ ɜ ɚɪɝɭɦɟɧɬɟ Instruct ɭɫɬɚɧɨɜɥɟɧ ɛɢɬ Gradient, ɢ 1 ɜ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ. (ȼɵɱɢɫɥɹɟɬ ɨɰɟɧɤɭ ɩɪɢɦɟɪɚ ɢ, ɜɨɡɦɨɠɧɨ, ɩɪɨɢɡɜɨɞɧɵɟ) 14.6.4. Ƚɟɧɟɪɢɪɭɟɬɫɹ ɡɚɩɪɨɫ ɤ ɡɚɞɚɱɧɢɤɭ Get ɫ ɚɪɝɭɦɟɧɬɚɦɢ Handle, Weight, tbWeight (ɉɨɥɭɱɚɟɬ ɨɬ ɡɚɞɚɱɧɢɤɚ ɜɟɫ ɩɪɢɦɟɪɚ) 14.6.5. Work = Work + Estim * Weight (ɉɨɞɫɱɢɬɵɜɚɟɦ ɫɭɦɦɚɪɧɭɸ ɨɰɟɧɤɭ) 14.6.6. ȿɫɥɢ ɜ ɚɪɝɭɦɟɧɬɟ Instruct ɭɫɬɚɧɨɜɥɟɧ ɛɢɬ PutEstimations, ɬɨ ɝɟɧɟɪɢɪɭɟɬɫɹ ɡɚɩɪɨɫ ɤ ɡɚɞɚɱɧɢɤɭ Put ɫ ɚɪɝɭɦɟɧɬɚɦɢ Handle, Estim, tbEstimations (ɉɟɪɟɞɚɟɬ ɡɚɞɚɱɧɢɤɭ ɨɰɟɧɤɭ ɩɪɢɦɟɪɚ) 14.7. ȿɫɥɢ ɜ ɚɪɝɭɦɟɧɬɟ Instruct ɭɫɬɚɧɨɜɥɟɧ ɛɢɬ Gradient, ɬɨ ɝɟɧɟɪɢɪɭɟɬɫɹ ɡɚɩɪɨɫ ɤ ɫɟɬɢ Back, ɫ ɚɪɝɭɦɟɧɬɚɦɢ Null, Back. (ȼɵɩɨɥɧɹɟɬɫɹ ɨɛɪɚɬɧɨɟ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɟ ɫɟɬɢ) 14.8. ȿɫɥɢ ɜ ɚɪɝɭɦɟɧɬɟ Instruct ɭɫɬɚɧɨɜɥɟɧ ɛɢɬ Contrast, ɬɨ ɝɟɧɟɪɢɪɭɟɬɫɹ ɡɚɩɪɨɫ ɤ ɤɨɧɬɪɚɫɬɟɪɭ ContrastExample ɫ ɚɪɝɭɦɟɧɬɨɦ ɥɨɠɶ. 14.9. Ƚɟɧɟɪɢɪɭɟɬɫɹ ɡɚɩɪɨɫ ɤ ɡɚɞɚɱɧɢɤɭ Next ɫ ɚɪɝɭɦɟɧɬɨɦ Handle. (ɉɟɪɟɯɨɞ ɤ ɫɥɟɞɭɸɳɟɦɭ ɩɪɢɦɟɪɭ) 14.10. ɉɟɪɟɯɨɞ ɤ ɲɚɝɭ 13 ɚɥɝɨɪɢɬɦɚ. ȼɵɱɢɫɥɹɟɦ ɫɪɟɞɧɸɸ ɨɰɟɧɤɭ: If Tasks = 0 Then Estim = 0 Else Estim = Work / Task ȿɫɥɢ ɜ ɚɪɝɭɦɟɧɬɟ Instruct ɭɫɬɚɧɨɜɥɟɧ ɛɢɬ Contrast, ɬɨ ɝɟɧɟɪɢɪɭɟɬɫɹ ɡɚɩɪɨɫ ɤ ɤɨɧɬɪɚɫɬɟɪɭ ContrastExample ɫ ɚɪɝɭɦɟɧɬɨɦ ɢɫɬɢɧɚ. Ɉɫɜɨɛɨɠɞɚɸɬɫɹ ɦɚɫɫɢɜɵ Data, AnsArray ɢ InArray. ȿɫɥɢ ɜ ɚɪɝɭɦɟɧɬɟ Instruct ɭɫɬɚɧɨɜɥɟɧ ɛɢɬ Estimate, ɬɨ ɨɫɜɨɛɨɠɞɚɟɬɫɹ ɦɚɫɫɢɜ ɢ RelArray. ȿɫɥɢ ɜ ɚɪɝɭɦɟɧɬɟ Instruct ɭɫɬɚɧɨɜɥɟɧ ɛɢɬ Interpret, ɬɨ ɨɫɜɨɛɨɠɞɚɸɬɫɹ ɦɚɫɫɢɜɵ Answers ɢ Reliability. ȿɫɥɢ Back <> Null ɨɫɜɨɛɨɠɞɚɟɬɫɹ ɦɚɫɫɢɜ Back. Ɂɚɜɟɪɲɚɟɬ ɢɫɩɨɥɧɟɧɢɟ, ɜɨɡɜɪɚɳɚɹ ɡɧɚɱɟɧɢɟ ɢɫɬɢɧɚ 14.6.2.
15. 16. 17. 18. 19. 20. 21.
7.2.3 Ɉɲɢɛɤɢ ɤɨɦɩɨɧɟɧɬɚ ɢɫɩɨɥɧɢɬɟɥɶ ȼ ɬɚɛɥ. 4 ɩɪɢɜɟɞɟɧ ɩɨɥɧɵɣ ɫɩɢɫɨɤ ɨɲɢɛɨɤ, ɤɨɬɨɪɵɟ ɦɨɝɭɬ ɜɨɡɧɢɤɚɬɶ ɩɪɢ ɜɵɩɨɥɧɟɧɢɢ ɡɚɩɪɨɫɨɜ ɤɨɦɩɨɧɟɧɬɨɦ ɢɫɩɨɥɧɢɬɟɥɶ, ɢ ɞɟɣɫɬɜɢɹ ɫɬɚɧɞɚɪɬɧɨɝɨ ɨɛɪɚɛɨɬɱɢɤɚ ɨɲɢɛɨɤ. Ɍɚɛɥɢɰɚ 4. Ɉɲɢɛɤɢ ɤɨɦɩɨɧɟɧɬɚ ɢɫɩɨɥɧɢɬɟɥɶ ɢ ɞɟɣɫɬɜɢɹ ɫɬɚɧɞɚɪɬɧɨɝɨ ɨɛɪɚɛɨɬɱɢɤɚ ɨɲɢɛɨɤ. ʋɇɚɡɜɚɧɢɟ ɨɲɢɛɤɢɋɬɚɧɞɚɪɬɧɚɹ ɨɛɪɚɛɨɬɤɚ 001 ɇɟɤɨɪɪɟɤɬɧɨɟ ɫɨɱɟɬɚɧɢɟ ɮɥɚɝɨɜ ɜ ɚɪɝɭɦɟɧɬɟ Instruct. Ɂɚɧɟɫɟɧɢɟ ɧɨɦɟɪɚ ɜ Error
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8. ɍɱɢɬɟɥɶ ɗɬɨɬ ɤɨɦɩɨɧɟɧɬ ɧɟ ɹɜɥɹɟɬɫɹ ɫɬɨɥɶ ɭɧɢɜɟɪɫɚɥɶɧɵɦ ɤɚɤ ɡɚɞɚɱɧɢɤ, ɨɰɟɧɤɚ ɢɥɢ ɧɟɣɪɨɧɧɚɹ ɫɟɬɶ, ɩɨɫɤɨɥɶɤɭ ɫɭɳɟɫɬɜɭɟɬ ɪɹɞ ɚɥɝɨɪɢɬɦɨɜ ɨɛɭɱɟɧɢɹ ɠɟɫɬɤɨ ɩɪɢɜɹɡɚɧɧɵɯ ɤ ɚɪɯɢɬɟɤɬɭɪɟ ɧɟɣɪɨɧɧɨɣ ɫɟɬɢ. ɉɪɢɦɟɪɚɦɢ ɬɚɤɢɯ ɚɥɝɨɪɢɬɦɨɜ ɦɨɝɭɬ ɫɥɭɠɢɬɶ ɨɛɭɱɟɧɢɟ (ɮɨɪɦɢɪɨɜɚɧɢɟ ɫɢɧɚɩɬɢɱɟɫɤɨɣ ɤɚɪɬɵ) ɫɟɬɢ ɏɨɩɮɢɥɞɚ, ɨɛɭɱɟɧɢɟ ɫɟɬɢ Ʉɨɯɨɧɟɧɚ ɢ ɪɹɞ ɞɪɭɝɢɯ ɚɧɚɥɨɝɢɱɧɵɯ ɫɟɬɟɣ. Ɉɞɧɚɤɨ ɜ ɝɥɚɜɟ «Ɉɩɢɫɚɧɢɟ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ» ɩɪɢɜɨɞɢɬɫɹ ɫɩɨɫɨɛ ɮɨɪɦɢɪɨɜɚɧɢɹ ɫɟɬɟɣ, ɩɨɡɜɨɥɹɸɳɢɣ ɨɛɭɱɚɬɶ ɫɟɬɢ ɏɨɩɮɢɥɞɚ ɢ Ʉɨɯɨɧɟɧɚ ɦɟɬɨɞɨɦ ɨɛɪɚɬɧɨɝɨ ɪɚɫɩɪɨɫɬɪɚɧɟɧɢɹ ɨɲɢɛɤɢ. Ɉɩɢɫɵɜɚɟɦɵɣ ɜ ɷɬɨɣ ɝɥɚɜɟ ɫɬɚɧɞɚɪɬ ɤɨɦɩɨɧɟɧɬɚ ɭɱɢɬɟɥɶ ɨɪɢɟɧɬɢɪɨɜɚɧ ɜ ɩɟɪɜɭɸ ɨɱɟɪɟɞɶ ɧɚ ɨɛɭɱɟɧɢɟ ɞɜɨɣɫɬɜɟɧɧɵɯ ɫɟɬɟɣ (ɫɟɬɟɣ ɨɛɪɚɬɧɨɝɨ ɪɚɫɩɪɨɫɬɪɚɧɟɧɢɹ ɨɲɢɛɤɢ).
8.1 ɑɬɨ ɦɨɠɧɨ ɨɛɭɱɚɬɶ ɦɟɬɨɞɨɦ ɞɜɨɣɫɬɜɟɧɧɨɫɬɢ Ʉɚɤ ɩɪɚɜɢɥɨ, ɦɟɬɨɞ ɞɜɨɣɫɬɜɟɧɧɨɫɬɢ (ɨɛɪɚɬɧɨɝɨ ɪɚɫɩɪɨɫɬɪɚɧɟɧɢɹ ɨɲɢɛɤɢ) ɢɫɩɨɥɶɡɭɸɬ ɞɥɹ ɩɨɞɫɬɪɨɣɤɢ ɩɚɪɚɦɟɬɪɨɜ ɧɟɣɪɨɧɧɨɣ ɫɟɬɢ. Ɉɞɧɚɤɨ, ɤɚɤ ɛɵɥɨ ɩɨɤɚɡɚɧɨ ɜ ɝɥɚɜɟ «Ɉɩɢɫɚɧɢɟ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ», ɫɟɬɶ ɦɨɠɟɬ ɜɵɱɢɫɥɹɬɶ ɧɟ ɬɨɥɶɤɨ ɝɪɚɞɢɟɧɬ ɮɭɧɤɰɢɢ ɨɰɟɧɤɢ ɩɨ ɨɛɭɱɚɟɦɵɦ ɩɚɪɚɦɟɬɪɚɦ ɫɟɬɢ, ɧɨ ɢ ɩɨ ɜɯɨɞɧɵɦ ɫɢɝɧɚɥɚɦ ɫɟɬɢ. ɂɫɩɨɥɶɡɭɹ ɝɪɚɞɢɟɧɬ ɮɭɧɤɰɢɢ ɨɰɟɧɤɢ ɩɨ ɜɯɨɞɧɵɦ ɫɢɝɧɚɥɚɦ ɫɟɬɢ ɦɨɠɧɨ ɪɟɲɚɬɶ ɡɚɞɚɱɭ, ɨɛɪɚɬɧɭɸ ɩɨ ɨɬɧɨɲɟɧɢɸ ɤ ɨɛɭɱɟɧɢɸ ɧɟɣɪɨɧɧɨɣ ɫɟɬɢ. Ɋɚɫɫɦɨɬɪɢɦ ɫɥɟɞɭɸɳɢɣ ɩɪɢɦɟɪ. ɉɭɫɬɶ ɟɫɬɶ ɫɟɬɶ, ɨɛɭɱɟɧɧɚɹ ɩɪɟɞɫɤɚɡɵɜɚɬɶ ɩɨ ɬɟɤɭɳɟɦɭ ɫɨɫɬɨɹɧɢɸ ɛɨɥɶɧɨɝɨ ɢ ɧɚɛɨɪɭ ɩɪɢɦɟɧɹɟɦɵɯ ɥɟɤɚɪɫɬɜ ɫɨɫɬɨɹɧɢɟ ɛɨɥɶɧɨɝɨ ɱɟɪɟɡ ɧɟɤɨɬɨɪɵɣ ɩɪɨɦɟɠɭɬɨɤ ɜɪɟɦɟɧɢ. ɉɨɫɬɭɩɢɥ ɧɨɜɵɣ ɛɨɥɶɧɨɣ. ȿɝɨ ɩɚɪɚɦɟɬɪɵ ɜɜɟɥɢ ɫɟɬɢ ɢ ɨɧɚ ɜɵɞɚɥɚ ɩɪɨɝɧɨɡ. ɂɡ ɩɪɨɝɧɨɡɚ ɫɥɟɞɭɟɬ ɭɯɭɞɲɟɧɢɟ ɧɟɤɨɬɨɪɵɯ ɩɚɪɚɦɟɬɪɨɜ ɫɨɫɬɨɹɧɢɹ ɛɨɥɶɧɨɝɨ. ȼɨɡɶɦɟɦ ɜɵɞɚɧɧɵɣ ɫɟɬɶɸ ɩɪɨɝɧɨɡ, ɡɚɦɟɧɢɦ ɡɧɚɱɟɧɢɹ ɩɚɪɚɦɟɬɪɨɜ, ɩɨ ɤɨɬɨɪɵɦ ɧɚɛɥɸɞɚɟɬɫɹ ɭɯɭɞɲɟɧɢɟ, ɧɚ ɠɟɥɚɟɦɵɟ ɡɧɚɱɟɧɢɹ. ɉɨɥɭɱɟɧɧɵɣ ɜɟɤɬɨɪ ɨɬɜɟɬɨɜ ɨɛɴɹɜɢɦ ɩɪɚɜɢɥɶɧɵɦ ɨɬɜɟɬɨɦ. ɂɦɟɹ ɩɪɚɜɢɥɶɧɵɣ ɨɬɜɟɬ ɢ ɨɬɜɟɬ, ɜɵɞɚɧɧɵɣ ɫɟɬɶɸ, ɜɵɱɢɫɥɢɦ ɝɪɚɞɢɟɧɬ ɮɭɧɤɰɢɢ ɨɰɟɧɤɢ ɩɨ ɜɯɨɞɧɵɦ ɫɢɝɧɚɥɚɦ ɫɟɬɢ. ȼ ɫɨɨɬɜɟɬɫɬɜɢɢ ɫɨ ɡɧɚɱɟɧɢɹɦɢ ɷɥɟɦɟɧɬɨɜ ɝɪɚɞɢɟɧɬɚ ɢɡɦɟɧɢɦ ɡɧɚɱɟɧɢɹ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɫɟɬɢ ɬɚɤ, ɱɬɨɛɵ ɨɰɟɧɤɚ ɭɦɟɧɶɲɢɥɚɫɶ. ɉɪɨɞɟɥɚɜ ɷɬɭ ɩɪɨɰɟɞɭɪɭ ɧɟɫɤɨɥɶɤɨ ɪɚɡ, ɩɨɥɭɱɢɦ ɜɟɤɬɨɪ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ, ɩɨɪɨɠɞɚɸɳɢɯ ɩɪɚɜɢɥɶɧɵɣ ɨɬɜɟɬ. Ⱦɚɥɟɟ ɜɪɚɱ ɞɨɥɠɟɧ ɨɩɪɟɞɟɥɢɬɶ, ɤɚɤɢɦ ɫɩɨɫɨɛɨɦ (ɤɚɤɢɦɢ ɥɟɤɚɪɫɬɜɚɦɢ ɢɥɢ ɩɪɨɰɟɞɭɪɚɦɢ) ɩɟɪɟɜɟɫɬɢ ɛɨɥɶɧɨɝɨ ɜ ɬɪɟɛɭɟɦɨɟ (ɩɨɥɭɱɟɧɧɨɟ ɜ ɯɨɞɟ ɨɛɭɱɟɧɢɹ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ) ɫɨɫɬɨɹɧɢɟ. ȼ ɛɨɥɶɲɢɧɫɬɜɟ ɫɥɭɱɚɟɜ ɱɚɫɬɶ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɧɟ ɩɨɞɥɟɠɢɬ ɢɡɦɟɧɟɧɢɸ (ɧɚɩɪɢɦɟɪ ɩɨɥ ɢɥɢ ɜɨɡɪɚɫɬ ɛɨɥɶɧɨɝɨ). ȼ ɷɬɨɦ ɫɥɭɱɚɟ ɷɬɢ ɜɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɞɨɥɠɧɵ ɛɵɬɶ ɩɨɦɟɱɟɧɵ ɤɚɤ ɧɟ ɨɛɭɱɚɟɦɵɟ (ɫɦ. ɢɫɩɨɥɶɡɨɜɚɧɢɟ ɦɚɫɤɢ ɨɛɭɱɚɟɦɨɫɬɢ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɜ ɝɥɚɜɟ «Ɉɩɢɫɚɧɢɟ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ»). Ɍɚɤɢɦ ɨɛɪɚɡɨɦ, ɫɩɨɫɨɛɧɨɫɬɶ ɫɟɬɟɣ ɜɵɱɢɫɥɹɬɶ ɝɪɚɞɢɟɧɬ ɮɭɧɤɰɢɢ ɨɰɟɧɤɢ ɩɨ ɜɯɨɞɧɵɦ ɩɚɪɚɦɟɬɪɚɦ ɫɟɬɢ ɩɨɡɜɨɥɹɟɬ ɪɟɲɚɬɶ ɜɩɨɥɧɟ ɨɫɦɵɫɥɟɧɧɭɸ ɨɛɪɚɬɧɭɸ ɡɚɞɚɱɭ: ɬɚɤ ɩɨɞɨɛɪɚɬɶ ɜɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɫɟɬɢ, ɱɬɨɛɵ ɜɵɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɭɞɨɜɥɟɬɜɨɪɹɥɢ ɡɚɞɚɧɧɵɦ ɬɪɟɛɨɜɚɧɢɹɦ. Ʉɪɨɦɟ ɬɨɝɨ, ɢɫɩɨɥɶɡɨɜɚɧɢɟ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ɩɨɡɜɨɥɹɟɬ ɫɬɚɜɢɬɶ ɧɨɜɵɟ ɜɨɩɪɨɫɵ ɩɟɪɟɞ ɢɫɫɥɟɞɨɜɚɬɟɥɟɦ. ȼ ɩɪɚɤɬɢɤɟ ɝɪɭɩɩɵ «ɇɟɣɪɨɄɨɦɩ» ɛɵɥ ɫɥɟɞɭɸɳɢɣ ɫɥɭɱɚɣ. Ȼɵɥɚ ɩɨɫɬɚɜɥɟɧɚ ɡɚɞɚɱɚ ɨɛɭɱɢɬɶ ɫɟɬɶ ɫɬɚɜɢɬɶ ɞɢɚɝɧɨɡ ɜɬɨɪɢɱɧɨɝɨ ɢɦɦɭɧɨɞɟɮɢɰɢɬɚ ɩɨ ɞɚɧɧɵɦ ɚɧɚɥɢɡɨɜ ɤɪɨɜɢ ɢ ɤɥɟɬɨɱɧɨɝɨ ɦɟɬɚɛɨɥɢɡɦɚ. ȼɫɹ ɨɛɭɱɚɸɳɚɹ ɜɵɛɨɪɤɚ ɛɵɥɚ ɪɚɡɛɢɬɚ ɧɚ ɞɜɚ ɤɥɚɫɫɚ: ɛɨɥɶɧɵɟ ɢ ɡɞɨɪɨɜɵɟ. ɉɪɢ ɚɧɚɥɢɡɟ ɛɚɡɵ ɞɚɧɧɵɯ ɫɬɚɧɞɚɪɬɧɵɦɢ ɫɬɚɬɢɫɬɢɱɟɫɤɢɦɢ ɦɟɬɨɞɚɦɢ ɡɧɚɱɢɦɵɯ ɨɬɥɢɱɢɣ ɨɛɧɚɪɭɠɢɬɶ ɧɟ ɭɞɚɥɨɫɶ. ɋɟɬɶ ɨɤɚɡɚɥɚɫɶ ɧɟ ɫɩɨɫɨɛɧɚ ɨɛɭɱɢɬɶɫɹ. Ⱦɚɥɟɟ ɭ ɢɫɫɥɟɞɨɜɚɬɟɥɹ ɛɵɥɨ ɞɜɚ ɩɭɬɢ: ɥɢɛɨ ɭɜɟɥɢɱɢɬɶ ɱɢɫɥɨ ɧɟɣɪɨɧɨɜ ɜ ɫɟɬɢ, ɥɢɛɨ ɨɩɪɟɞɟɥɢɬɶ, ɱɬɨ ɦɟɲɚɟɬ ɨɛɭɱɟɧɢɸ. ɂɫɫɥɟɞɨɜɚɬɟɥɢ ɜɵɛɪɚɥɢ ɜɬɨɪɨɣ ɩɭɬɶ. ɉɪɢ ɨɛɭɱɟɧɢɢ ɫɟɬɢ ɛɵɥɚ ɩɪɢɦɟɧɟɧɚ ɫɥɟɞɭɸɳɚɹ ɩɪɨɰɟɞɭɪɚ: ɤɚɤ ɬɨɥɶɤɨ ɨɛɭɱɟɧɢɟ ɫɟɬɢ ɨɫɬɚɧɚɜɥɢɜɚɥɨɫɶ ɢɡ-ɡɚ ɧɟɜɨɡɦɨɠɧɨɫɬɢ ɞɚɥɶɧɟɣɲɟɝɨ ɭɦɟɧɶɲɟɧɢɹ ɨɰɟɧɤɢ, ɩɪɢɦɟɪ, ɢɦɟɸɳɢɣ ɧɚɢɯɭɞɲɭɸ ɨɰɟɧɤɭ, ɢɫɤɥɸɱɚɥɫɹ ɢɡ ɨɛɭɱɚɸɳɟɝɨ ɦɧɨɠɟɫɬɜɚ. ɉɨɫɥɟ ɬɨɝɨ, ɤɚɤ ɫɟɬɶ ɨɛɭɱɢɥɚɫɶ ɪɟɲɟɧɢɸ ɡɚɞɚɱɢ ɧɚ ɭɫɟɱɟɧɧɨɦ ɨɛɭɱɚɸɳɟɦ ɦɧɨɠɟɫɬɜɟ, ɛɵɥ ɩɪɨɜɟɞɟɧ ɚɧɚɥɢɡ ɢɫɤɥɸɱɟɧɧɵɯ ɩɪɢɦɟɪɨɜ. ȼɵɹɫɧɢɥɨɫɶ, ɱɬɨ ɢɫɤɥɸɱɟɧɨ ɨɤɨɥɨ ɩɨɥɨɜɢɧɵ ɛɨɥɶɧɵɯ. Ɍɨɝɞɚ ɦɧɨɠɟɫɬɜɨ ɛɨɥɶɧɵɯ ɛɵɥɨ ɪɚɡɛɢɬɨ ɧɚ ɞɜɚ ɤɥɚɫɫɚ – ɛɨɥɶɧɵɟ1 (ɨɫɬɚɜɲɢɟɫɹ ɜ ɨɛɭɱɚɸɳɟɦ ɦɧɨɠɟɫɬɜɟ) ɢ ɛɨɥɶɧɵɟ2 (ɢɫɤɥɸɱɟɧɧɵɟ). ɉɪɢ ɬɚɤɨɦ ɪɚɡɛɢɟɧɢɢ ɨɛɭɱɚɸɳɟɣ ɜɵɛɨɪɤɢ ɫɬɚɧɞɚɪɬɧɵɟ ɦɟɬɨɞɵ ɫɬɚɬɢɫɬɢɤɢ ɩɨɤɚɡɚɥɢ ɡɧɚɱɢɦɵɟ ɪɚɡɥɢɱɢɹ ɜ ɩɚɪɚɦɟɬɪɚɯ ɤɥɚɫɫɨɜ. Ɉɛɭɱɟɧɢɟ ɫɟɬɢ ɤɥɚɫɫɢɮɢɤɚɰɢɢ ɧɚ ɬɪɢ ɤɥɚɫɫɚ ɛɵɫɬɪɨ ɡɚɜɟɪɲɢɥɨɫɶ ɩɨɥɧɵɦ ɭɫɩɟɯɨɦ. ɉɪɢ ɫɨɞɟɪɠɚɬɟɥɶɧɨɦ ɚɧɚɥɢɡɟ ɩɪɢɦɟɪɨɜ, ɫɨɫɬɚɜɥɹɸɳɢɯ ɤɥɚɫɫɵ ɛɨɥɶɧɵɟ1 ɢ ɛɨɥɶɧɵɟ2, ɛɵɥɨ ɭɫɬɚɧɨɜɥɟɧɨ, ɱɬɨ ɤ ɤɥɚɫɫɭ ɛɨɥɧɵɟ1 ɨɬɧɨɫɹɬɫɹ ɛɨɥɶɧɵɟ ɧɚ ɡɚɜɟɪɲɚɸɳɟɣ ɫɬɚɞɢɢ ɡɚɛɨɥɟɜɚɧɢɹ, ɚ ɤ ɤɥɚɫɫɭ ɛɨɥɶɧɵɟ2 – ɧɚ ɧɚɱɚɥɶɧɨɣ. Ɋɚɧɟɟ ɬɚɤɨɟ ɪɚɡɛɢɟɧɢɟ ɛɨɥɶɧɵɯ ɧɟ ɩɪɨɜɨɞɢɥɨɫɶ. Ɍɚɤɢɦ ɨɛɪɚɡɨɦ, ɨɛɭɱɟɧɢɟ ɧɟɣɪɨɧɧɨɣ ɫɟɬɢ ɪɟɲɟɧɢɸ ɩɪɢɤɥɚɞɧɨɣ ɡɚɞɚɱɢ ɩɨɫɬɚɜɢɥɨ ɩɟɪɟɞ ɢɫɫɥɟɞɨɜɚɬɟɥɟɦ ɫɨɞɟɪɠɚɬɟɥɶɧɵɣ ɜɨɩɪɨɫ, ɩɨɡɜɨɥɢɜɲɢɣ ɩɨɥɭɱɢɬɶ ɧɨɜɨɟ ɡɧɚɧɢɟ ɨ ɩɪɟɞɦɟɬɧɨɣ ɨɛɥɚɫɬɢ. ɉɨɞɜɨɞɹ ɢɬɨɝɢ ɷɬɨɝɨ ɪɚɡɞɟɥɚ, ɦɨɠɧɨ ɫɤɚɡɚɬɶ, ɱɬɨ, ɢɫɩɨɥɶɡɭɹ ɦɟɬɨɞ ɞɜɨɣɫɬɜɟɧɧɨɫɬɢ ɜ ɨɛɭɱɟɧɢɢ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ɦɨɠɧɨ: 1. Ɉɛɭɱɚɬɶ ɫɟɬɶ ɪɟɲɟɧɢɸ ɡɚɞɚɱɢ. 2. ɉɨɞɛɢɪɚɬɶ ɜɯɨɞɧɵɟ ɞɚɧɧɵɟ ɬɚɤ, ɱɬɨɛɵ ɧɚ ɜɵɯɨɞɟ ɧɟɣɪɨɧɧɨɣ ɫɟɬɢ ɛɵɥ ɡɚɞɚɧɧɵɣ ɨɬɜɟɬ. 3. ɋɬɚɜɢɬɶ ɜɨɩɪɨɫɵ ɨ ɫɨɨɬɜɟɬɫɬɜɢɢ ɜɯɨɞɧɵɯ ɞɚɧɧɵɯ ɡɚɞɚɱɧɢɤɚ ɩɨɫɬɚɧɨɜɤɟ ɧɟɣɪɨɫɟɬɟɜɨɣ ɡɚɞɚɱɢ.
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8.2 Ɉɩɢɫɚɧɢɟ ɚɥɝɨɪɢɬɦɨɜ ɨɛɭɱɟɧɢɹ ȼɫɟ ɚɥɝɨɪɢɬɦɵ ɨɛɭɱɟɧɢɹ ɫɟɬɟɣ ɦɟɬɨɞɨɦ ɨɛɪɚɬɧɨɝɨ ɪɚɫɩɪɨɫɬɪɚɧɟɧɢɹ ɨɲɢɛɤɢ ɨɩɢɪɚɸɬɫɹ ɧɚ ɫɩɨɫɨɛɧɨɫɬɶ ɫɟɬɢ ɜɵɱɢɫɥɹɬɶ ɝɪɚɞɢɟɧɬ ɮɭɧɤɰɢɢ ɨɲɢɛɤɢ ɩɨ ɨɛɭɱɚɸɳɢɦ ɩɚɪɚɦɟɬɪɚɦ. Ⱦɚɠɟ ɩɪɚɜɢɥɨ ɏɟɛɛɚ ɢɫɩɨɥɶɡɭɟɬ ɜɟɤɬɨɪ ɩɫɟɜɞɨɝɪɚɞɢɟɧɬɚ, ɜɵɱɢɫɥɹɟɦɵɣ ɫɟɬɶɸ ɩɪɢ ɢɫɩɨɥɶɡɨɜɚɧɢɢ ɡɟɪɤɚɥɶɧɨɝɨ ɩɨɪɨɝɨɜɨɝɨ ɷɥɟɦɟɧɬɚ (ɫɦ. ɪɚɡɞɟɥ «ɉɨɪɨɝɨɜɵɣ ɷɥɟɦɟɧɬ» ɝɥɚɜɵ «Ɉɩɢɫɚɧɢɟ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ»). Ɍɚɤɢɦ ɨɛɪɚɡɨɦ, ɚɤɬ ɨɛɭɱɟɧɢɹ ɫɨɫɬɨɢɬ ɢɡ ɜɵɱɢɫɥɟɧɢɹ ɝɪɚɞɢɟɧɬɚ ɢ ɫɨɛɫɬɜɟɧɧɨ ɨɛɭɱɟɧɢɹ ɫɟɬɢ (ɦɨɞɢɮɢɤɚɰɢɢ ɩɚɪɚɦɟɬɪɨɜ ɫɟɬɢ). Ɉɞɧɚɤɨ, ɫɭɳɟɫɬɜɭɟɬ ɦɧɨɠɟɫɬɜɨ ɧɟ ɝɪɚɞɢɟɧɬɧɵɯ ɦɟɬɨɞɨɜ ɨɛɭɱɟɧɢɹ, ɬɚɤɢɯ, ɤɚɤ ɦɟɬɨɞ ɩɨɤɨɨɪɞɢɧɚɬɧɨɝɨ ɫɩɭɫɤɚ, ɦɟɬɨɞ ɫɥɭɱɚɣɧɨɝɨ ɩɨɢɫɤɚ ɢ ɰɟɥɨɟ ɫɟɦɟɣɫɬɜɨ ɦɟɬɨɞɨɜ Ɇɨɧɬɟ-Ʉɚɪɥɨ. ȼɫɟ ɷɬɢ ɦɟɬɨɞɵ ɦɨɝɭɬ ɢɫɩɨɥɶɡɨɜɚɬɶɫɹ ɩɪɢ ɨɛɭɱɟɧɢɢ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ, ɯɨɬɹ, ɤɚɤ ɩɪɚɜɢɥɨ, ɨɧɢ ɦɟɧɟɟ ɷɮɮɟɤɬɢɜɧɵ, ɱɟɦ ɝɪɚɞɢɟɧɬɧɵɟ ɦɟɬɨɞɵ. ɇɟɤɨɬɨɪɵɟ ɜɚɪɢɚɧɬɵ ɦɟɬɨɞɨɜ ɨɛɭɱɟɧɢɹ ɨɩɢɫɚɧɵ ɞɚɥɟɟ ɜ ɷɬɨɣ ɝɥɚɜɟ. ɉɨɫɤɨɥɶɤɭ ɨɛɭɱɟɧɢɟ ɞɜɨɣɫɬɜɟɧɧɵɯ ɫɟɬɟɣ ɫ ɬɨɱɤɢ ɡɪɟɧɢɹ ɢɫɩɨɥɶɡɭɟɦɨɝɨ ɦɚɬɟɦɚɬɢɱɟɫɤɨɝɨ ɚɩɩɚɪɚɬɚ ɷɤɜɢɜɚɥɟɧɬɧɨ ɡɚɞɚɱɟ ɦɧɨɝɨɦɟɪɧɨɣ ɨɩɬɢɦɢɡɚɰɢɢ, ɬɨ ɜ ɞɚɧɧɨɣ ɝɥɚɜɟ ɪɚɫɫɦɨɬɪɟɧɵ ɬɨɥɶɤɨ ɧɟɫɤɨɥɶɤɨ ɦɟɬɨɞɨɜ ɨɛɭɱɟɧɢɹ, ɧɚɢɛɨɥɟɟ ɢɫɩɨɥɶɡɭɟɦɵɯ ɩɪɢ ɨɛɭɱɟɧɢɢ ɫɟɬɟɣ. Ȼɨɥɟɟ ɩɨɥɧɨɟ ɩɪɟɞɫɬɚɜɥɟɧɢɟ ɨ ɦɟɬɨɞɚɯ ɨɩɬɢɦɢɡɚɰɢɢ, ɞɨɩɭɫɤɚɸɳɢɯ ɢɫɩɨɥɶɡɨɜɚɧɢɟ ɜ ɨɛɭɱɟɧɢɢ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ, ɦɨɠɧɨ ɩɨɥɭɱɢɬɶ ɢɡ ɤɧɢɝ ɩɨ ɦɟɬɨɞɚɦ ɨɩɬɢɦɢɡɚɰɢɢ (ɫɦ. ɧɚɩɪɢɦɟɪ [48, 103, 142]).
8.2.1 Ʉɪɚɬɤɢɣ ɨɛɡɨɪ ɦɚɤɪɨɤɨɦɚɧɞ ɭɱɢɬɟɥɹ ɉɪɢ ɨɩɢɫɚɧɢɢ ɦɟɬɨɞɨɜ ɢɫɩɨɥɶɡɭɟɬɫɹ ɧɚɛɨɪ ɦɚɤɪɨɫɨɜ, ɩɪɢɜɟɞɟɧɧɵɣ ɜ ɬɚɛɥ. 2. ȼ ɬɚɛɥ. 2 ɞɚɧɨ ɩɨɹɫɧɟɧɢɟ ɜɵɩɨɥɧɹɟɦɵɯ ɦɚɤɪɨɫɚɦɢ ɞɟɣɫɬɜɢɣ. ȼɫɟ ɦɚɤɪɨɤɨɦɚɧɞɵ ɦɨɝɭɬ ɨɩɟɪɢɪɨɜɚɬɶ ɫ ɞɚɧɧɵɦɢ ɤɚɤ ɩɪɨɫɬɪɚɧɫɬɜɚ ɩɚɪɚɦɟɬɪɨɜ, ɬɚɤ ɢ ɩɪɨɫɬɪɚɧɫɬɜɚ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɫɟɬɢ. ȼ ɩɟɪɜɨɣ ɱɚɫɬɢ ɝɥɚɜɵ ɩɨɥɚɝɚɟɬɫɹ, ɱɬɨ ɨɛɴɟɤɬ ɨɛɭɱɟɧɢɹ ɭɫɬɚɧɨɜɥɟɧ ɡɚɪɚɧɟɟ. ȼ ɦɚɤɪɨɫɚɯ ɢɫɩɨɥɶɡɭɸɬɫɹ ɩɨɧɹɬɢɹ ɢ ɚɪɝɭɦɟɧɬɵ, ɩɪɢɜɟɞɟɧɧɵɟ ɜ ɬɚɛɥ. 1. ɋɩɢɫɨɤ ɦɚɤɪɨɤɨɦɚɧɞ ɩɪɢɜɟɞɟɧ ɜ ɬɚɛɥ. 2. ɉɪɢ ɨɩɢɫɚɧɢɢ ɦɟɬɨɞɨɜ ɨɛɭɱɟɧɢɹ ɜɫɟ ɚɪɝɭɦɟɧɬɵ ɢɦɟɸɬ Ɍɚɛɥɢɰɚ 1 ɉɨɧɹɬɢɹ ɢ ɚɪɝɭɦɟɧɬɵ ɦɚɤɪɨɤɨɦɚɧɞ ɭɱɢɬɟɥɹ (ɩɪɟɞɜɚɪɢɬɟɥɶɧɵɣ ɫɩɢɫɨɤ) ɇɚɡɜɚɧɢɟɋɦɵɫɥ ɌɨɱɤɚɌɨɱɤɚ ɜ ɩɪɨɫɬɪɚɧɫɬɜɟ ɩɚɪɚɦɟɬɪɨɜ ɢɥɢ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ. Ⱥɧɚɥɨɝɢɱɧɚ ɜɟɤɬɨɪɭ. ȼɟɤɬɨɪȼɟɤɬɨɪ ɜ ɩɪɨɫɬɪɚɧɫɬɜɟ ɩɚɪɚɦɟɬɪɨɜ ɢɥɢ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ. Ⱥɧɚɥɨɝɢɱɟɧ ɬɨɱɤɟ. ȼɟɤɬɨɪ_ɦɢɧɢɦɭɦɨɜȼɟɤɬɨɪ ɦɢɧɢɦɚɥɶɧɵɯ ɡɧɚɱɟɧɢɣ ɩɚɪɚɦɟɬɪɨɜ ɢɥɢ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ. ȼɟɤɬɨɪ_ɦɚɤɫɢɦɭɦɨɜȼɟɤɬɨɪ ɦɚɤɫɢɦɚɥɶɧɵɯ ɡɧɚɱɟɧɢɣ ɩɚɪɚɦɟɬɪɨɜ ɢɥɢ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ. ɍɤɚɡɚɬɟɥɶ_ɧɚ_ɜɟɤɬɨɪȺɞɪɟɫ ɜɟɤɬɨɪɚ. ɂɫɩɨɥɶɡɭɟɬɫɹ ɞɥɹ ɩɟɪɟɞɚɱɢ ɜɟɤɬɨɪɨɜ ɜ ɦɚɤɪɨɤɨɦɚɧɞɵ. ɉɭɫɬɨɣ_ɭɤɚɡɚɬɟɥɶɍɤɚɡɚɬɟɥɶ ɧɚ ɨɬɫɭɬɫɬɜɭɸɳɢɣ ɜɟɤɬɨɪ. Ɍɚɛɥɢɰɚ 2 ɋɩɢɫɨɤ ɦɚɤɪɨɤɨɦɚɧɞ ɭɱɢɬɟɥɹ (ɩɪɟɞɜɚɪɢɬɟɥɶɧɵɣ). ɇɚɡɜɚɧɢɟȺɪɝɭɦɟɧɬɵ (ɬɢɩɵ) ȼɵɩɨɥɧɹɟɦɵɟ ɞɟɣɫɬɜɢɹ ɋɨɡɞɚɬɶ_ɜɟɤɬɨɪɍɤɚɡɚɬɟɥɶ _ɧɚ_ɜɟɤɬɨɪɋɨɡɞɚɟɬ ɷɤɡɟɦɩɥɹɪ ɜɟɤɬɨɪɚ ɫ ɧɟɨɩɪɟɞɟɥɟɧɧɵɦɢ ɡɧɚɱɟɧɢɹɦɢ. Ⱥɞɪɟɫ ɜɟɤɬɨɪɚ ɩɨɦɟɳɚɟɬɫɹ ɜ ɍɤɚɡɚɬɟɥɶ_ɧɚ_ɜɟɤɬɨɪ. Ɉɫɜɨɛɨɞɢɬɶ_ɜɟɤɬɨɪɍɤɚɡɚɬɟɥɶ _ɧɚ_ɜɟɤɬɨɪɈɫɜɨɛɨɠɞɚɟɬ ɩɚɦɹɬɶ ɡɚɧɹɬɭɸ ɜɟɤɬɨɪɨɦ, ɪɚɫɩɨɥɨɠɟɧɧɵɦ ɩɨ ɚɞɪɟɫɭ ɍɤɚɡɚɬɟɥɶ_ɧɚ_ɜɟɤɬɨɪ. ɋɥɭɱɚɣɧɵɣ_ɜɟɤɬɨɪɍɤɚɡɚɬɟɥɶ _ɧɚ_ɜɟɤɬɨɪȼ ɜɟɤɬɨɪɟ, ɧɚ ɤɨɬɨɪɵɣ ɭɤɚɡɵɜɚɟɬ ɍɤɚɡɚɬɟɥɶ_ɧɚ_ɜɟɤɬɨɪ, ɝɟɧɟɪɢɪɭɟɬɫɹ ɜɟɤɬɨɪ, ɤɚɠɞɚɹ ɢɡ ɤɨɨɪɞɢɧɚɬ ɤɨɬɨɪɨɝɨ ɹɜɥɹɟɬɫɹ ɫɥɭɱɚɣɧɨɣ ɜɟɥɢɱɢɧɨɣ, ɪɚɜɧɨɦɟɪɧɨ ɪɚɫɩɪɟɞɟɥɟɧɧɨɣ ɧɚ ɢɧɬɟɪɜɚɥɟ ɦɟɠɞɭ ɡɧɚɱɟɧɢɹɦɢ ɫɨɨɬɜɟɬɫɬɜɭɸɳɢɯ ɤɨɨɪɞɢɧɚɬ ɜɟɤɬɨɪɨɜ ȼɟɤɬɨɪ_ɦɢɧɢɦɭɦɨɜ ɢ ȼɟɤɬɨɪ_ɦɚɤɫɢɦɭɦɨɜ. Ɇɨɞɢɮɢɤɚɰɢɹ_ɜɟɤɬɨɪɚɍɤɚɡɚɬɟɥɶ _ɧɚ_ɜɟɤɬɨɪ Ƚɟɧɟɪɢɪɭɟɬ ɡɚɩɪɨɫ ɧɚ ɦɨɞɢɮɢɤɚɰɢɸ ɜɟɤɬɨɪɚ (ɫɦ. ɪɚɡɞɟɥ ɋɬɚɪɵɣ_ɒɚɝ «ɉɪɨɜɟɫɬɢ ɨɛɭɱɟɧɢɟ (Modify)» ɝɥɚɜɵ «Ɉɩɢɫɚɧɢɟ ɧɟɣɪɨɧɇɨɜɵɣ_ɒɚɝ ɧɵɯ ɫɟɬɟɣ»). Ɉɩɬɢɦɢɡɚɰɢɹ_ɲɚɝɚɍɤɚɡɚɬɟɥɶ _ɧɚ_ɜɟɤɬɨɪ ɉɪɨɢɡɜɨɞɢɬ ɩɨɞɛɨɪ ɨɩɬɢɦɚɥɶɧɨɝɨ ɲɚɝɚ (ɫɦ. ɪɢɫ. 3). ɇɚɱɚɥɶɧɵɣ_ɒɚɝ ɋɨɯɪɚɧɢɬɶ_ɜɟɤɬɨɪɍɤɚɡɚɬɟɥɶ _ɧɚ_ɜɟɤɬɨɪɋɤɨɩɢɪɨɜɚɬɶ ɬɟɤɭɳɢɣ ɜɟɤɬɨɪ ɜ ɜɟɤɬɨɪ, ɭɤɚɡɚɧɧɵɣ ɜ ɚɪɝɭɦɟɧɬɟ ɍɤɚɡɚɬɟɥɶ_ɧɚ_ɜɟɤɬɨɪ. ɍɫɬɚɧɨɍɤɚɡɚɬɟɥɶ_ɧɚ_ɜɟɤɬɨɪɋɤɨɩɢɪɨɜɚɬɶ ɜɟɤɬɨɪ, ɭɤɚɡɚɧɧɵɣ ɜ ɚɪɝɭɦɟɧɬɟ ɍɤɚɡɚɜɢɬɶ_ɩɚɪɚɦɟɬɪɵ ɬɟɥɶ_ɧɚ_ɜɟɤɬɨɪ, ɜ ɬɟɤɭɳɢɣ ɜɟɤɬɨɪ. ȼɵɱɢɫɥɢɬɶ_ɨɰɟɧɤɭɈɰɟɧɤɚȼɵɱɢɫɥɹɟɬ ɨɰɟɧɤɭ ɬɟɤɭɳɟɝɨ ɜɟɤɬɨɪɚ. ȼɵɱɢɫɥɟɧɧɭɸ ɜɟɥɢɱɢɧɭ ɫɤɥɚɞɵɜɚɟɬ ɜ ɚɪɝɭɦɟɧɬ Ɉɰɟɧɤɚ. ȼɵɱɢɫɥɢɬɶ_ɝɪɚɞɢɟɧɬȼɵɱɢɫɥɹɟɬ ɝɪɚɞɢɟɧɬ ɮɭɧɤɰɢɢ ɨɰɟɧɤɢ.
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ɬɢɩ, ɨɩɪɟɞɟɥɹɟɦɵɣ ɬɢɩɨɦ ɚɪɝɭɦɟɧɬɚ ɦɚɤɪɨɤɨɦɚɧɞɵ. ȿɫɥɢ ɜ ɨɩɢɫɚɧɢɢ ɦɚɤɪɨɤɨɦɚɧɞɵ ɜ ɬɚɛɥ. 2 ɬɢɩ ɚɪɝɭɦɟɧɬɚ ɧɟ ɫɨɨɬɜɟɬɫɬɜɭɟɬ ɧɢ ɨɞɧɨɦɭ ɢɡ ɬɢɩɨɜ, ɩɪɢɜɟɞɟɧɧɵɯ ɜ ɬɚɛɥ. 1, ɬɨ ɷɬɢ ɚɪɝɭɦɟɧɬɵ ɢɦɟɸɬ ɱɢɫɥɨɜɨɣ ɬɢɩ.
8.2.2 ɇɟɝɪɚɞɢɟɧɬɧɵɟ ɦɟɬɨɞɵ ɨɛɭɱɟɧɢɹ ɋɪɟɞɢ ɧɟɝɪɚɞɢɟɧɬɧɵɯ ɦɟɬɨɞɨɜ ɪɚɫɫɦɨɬɪɢɦ ɫɥɟɞɭɸɳɢɟ ɦɟɬɨɞɵ, ɤɚɠɞɵɣ ɢɡ ɤɨɬɨɪɵɯ ɹɜɥɹɟɬɫɹ ɩɪɟɞɫɬɚɜɢɬɟɥɟɦ ɰɟɥɨɝɨ ɫɟɦɟɣɫɬɜɚ ɦɟɬɨɞɨɜ ɨɩɬɢɦɢɡɚɰɢɢ: 1. Ɇɟɬɨɞ ɫɥɭɱɚɣɧɨɣ ɫɬɪɟɥɶɛɵ (ɩɪɟɞɫɬɚɜɢɬɟɥɶ ɫɟɦɟɣɫɬɜɚ ɦɟɬɨɞɨɜ Ɇɨɧɬɟ-Ʉɚɪɥɨ). 2. Ɇɟɬɨɞ ɩɨɤɨɨɪɞɢɧɚɬɧɨɝɨ ɫɩɭɫɤɚ (ɩɫɟɜɞɨɝɪɚɞɢɟɧɬɧɵɣ ɦɟɬɨɞ). 3. Ɇɟɬɨɞ ɫɥɭɱɚɣɧɨɝɨ ɩɨɢɫɤɚ (ɩɫɟɜɞɨɝɪɚɞɢɟɧɬɧɵɣ 1. ɋɨɡɞɚɬɶ_ɜɟɤɬɨɪ ȼ1 ɦɟɬɨɞ). 2. ɋɨɡɞɚɬɶ_ɜɟɤɬɨɪ ȼ2 4. Ɇɟɬɨɞ ɇɟɥɞɟɪɚ-Ɇɢɞɚ. 3. ȼɵɱɢɫɥɢɬɶ_ɨɰɟɧɤɭ Ɉ1 4. ɋɨɯɪɚɧɢɬɶ_ɜɤɬɨɪ ȼ1 8.2.2.1 Ɇɟɬɨɞ ɫɥɭɱɚɣɧɨɣ ɫɬɪɟɥɶɛɵ 5. ɍɫɬɚɧɨɜɢɬɶ_ɩɚɪɚɦɟɬɪɵ ȼ1 ɂɞɟɹ ɦɟɬɨɞɚ ɫɥɭɱɚɣɧɨɣ ɫɬɪɟɥɶɛɵ ɫɨɫɬɨɢɬ ɜ ɝɟɧɟ6. ɋɥɭɱɚɣɧɵɣ_ɜɟɤɬɨɪ ȼ2 ɪɚɰɢɢ ɛɨɥɶɲɨɣ ɩɨɫɥɟɞɨɜɚɬɟɥɶɧɨɫɬɢ ɫɥɭɱɚɣɧɵɯ ɬɨɱɟɤ ɢ 7. Ɇɨɞɢɮɢɤɚɰɢɹ_ɜɟɤɬɨɪɚ ȼ2, 0, 1 ɜɵɱɢɫɥɟɧɢɹ ɨɰɟɧɤɢ ɜ ɤɚɠɞɨɣ ɢɡ ɧɢɯ. ɉɪɢ ɞɨɫɬɚɬɨɱɧɨɣ 8. ȼɵɱɢɫɥɢɬɶ_ɨɰɟɧɤɭ Ɉ2 ɞɥɢɧɟ ɩɨɫɥɟɞɨɜɚɬɟɥɶɧɨɫɬɢ ɦɢɧɢɦɭɦ ɛɭɞɟɬ ɧɚɣɞɟɧ. Ɂɚɩɢɫɶ 9. ȿɫɥɢ Ɉ2<Ɉ1 ɬɨ ɩɟɪɟɯɨɞ ɤ ɲɚɝɭ 11 ɷɬɨɣ ɩɪɨɰɟɞɭɪɵ ɧɚ ɦɚɤɪɨɹɡɵɤɟ ɩɪɢɜɟɞɟɧɚ ɧɚ ɪɢɫ. 1 10. ɉɟɪɟɯɨɞ ɤ ɲɚɝɭ 5 Ɉɫɬɚɧɨɜɤɚ ɞɚɧɧɨɣ ɩɪɨɰɟɞɭɪɵ ɩɪɨɢɡɜɨɞɢɬɫɹ ɩɨ 11. Ɉ1=Ɉ2 ɤɨɦɚɧɞɟ ɩɨɥɶɡɨɜɚɬɟɥɹ ɢɥɢ ɩɪɢ ɜɵɩɨɥɧɟɧɢɢ ɭɫɥɨɜɢɹ, ɱɬɨ 12. ɉɟɪɟɯɨɞ ɤ ɲɚɝɭ 4 Ɉ1 ɫɬɚɥɨ ɦɟɧɶɲɟ ɧɟɤɨɬɨɪɨɣ ɡɚɞɚɧɧɨɣ ɜɟɥɢɱɢɧɵ. ɋɭɳɟɫɬ13. ɍɫɬɚɧɨɜɢɬɶ_ɩɚɪɚɦɟɬɪɵ ȼ1 ɜɭɟɬ ɨɝɪɨɦɧɨɟ ɪɚɡɧɨɨɛɪɚɡɢɟ ɦɨɞɢɮɢɤɚɰɢɣ ɷɬɨɝɨ ɦɟɬɨɞɚ. 14. Ɉɫɜɨɛɨɞɢɬɶ_ɜɟɤɬɨɪ ȼ1 ɇɚɢɛɨɥɟɟ ɩɪɨɫɬɨɣ ɹɜɥɹɟɬɫɹ ɦɟɬɨɞ ɫɥɭɱɚɣɧɨɣ ɫɬɪɟɥɶɛɵ ɫ 15. Ɉɫɜɨɛɨɞɢɬɶ_ɜɟɤɬɨɪ ȼ2 ɭɦɟɧɶɲɟɧɢɟɦ ɪɚɞɢɭɫɚ. ɉɪɢɦɟɪ ɩɪɨɰɟɞɭɪɵ, ɪɟɚɥɢɡɭɸɳɟɣ Ɋɢɫ. 1. ɉɪɨɫɬɟɣɲɢɣ ɚɥɝɨɪɢɬɦ ɦɟɬɨɞɚ ɫɥɭɷɬɨɬ ɦɟɬɨɞ, ɩɪɢɜɟɞɟɧ ɧɚ ɪɢɫ. 2. ȼ ɷɬɨɦ ɦɟɬɨɞɟ ɟɫɬɶ ɞɜɚ ɱɚɣɧɨɣ ɫɬɪɟɥɶɛɵ ɩɚɪɚɦɟɬɪɚ, ɡɚɞɚɜɚɟɦɵɯ ɩɨɥɶɡɨɜɚɬɟɥɟɦ: 1. ɋɨɡɞɚɬɶ_ɜɟɤɬɨɪ ȼ1 ɑɢɫɥɨ_ɩɨɩɵɬɨɤ – ɱɢɫɥɨ ɧɟɭɞɚɱɧɵɯ 2. ɋɨɡɞɚɬɶ_ɜɟɤɬɨɪ ȼ2 ɩɪɨɛɧɵɯ ɝɟɧɟɪɚɰɢɣ ɜɟɤɬɨɪɚ ɩɪɢ 3. ȼɵɱɢɫɥɢɬɶ_ɨɰɟɧɤɭ Ɉ1 ɨɞɧɨɦ ɪɚɞɢɭɫɟ. 4. ɑɢɫɥɨ_ɋɦɟɧ_Ɋɚɞɢɭɫɚ=1 Ɇɢɧɢɦɚɥɶɧɵɣ_ɪɚɞɢɭɫ – ɦɢɧɢɦɚɥɶɧɨɟ 5. Ɋɚɞɢɭɫ=1/ ɑɢɫɥɨ_ɋɦɟɧ_Ɋɚɞɢɭɫɚ ɡɧɚɱɟɧɢɟ ɪɚɞɢɭɫɚ, ɩɪɢ ɤɨɬɨɪɨɦ 6. ɉɨɩɵɬɤɚ=0 ɩɪɨɞɨɥɠɚɟɬ ɪɚɛɨɬɚɬɶ ɚɥɝɨɪɢɬɦ. 7. ɋɨɯɪɚɧɢɬɶ_ɜɤɬɨɪ ȼ1 ɂɞɟɹ ɷɬɨɝɨ ɦɟɬɨɞɚ ɫɨɫɬɨɢɬ ɜ ɫɥɟ- 8. ɍɫɬɚɧɨɜɢɬɶ_ɩɚɪɚɦɟɬɪɵ ȼ1 ɞɭɸɳɟɦ. Ɂɚɞɚɞɢɦɫɹ ɧɚɱɚɥɶɧɵɦ ɫɨɫɬɨɹɧɢɟɦ 9. ɋɥɭɱɚɣɧɵɣ_ɜɟɤɬɨɪ ȼ2 ɜɟɤɬɨɪɚ ɩɚɪɚɦɟɬɪɨɜ. ɇɨɜɵɣ ɜɟɤɬɨɪ ɩɚɪɚ- 10. Ɇɨɞɢɮɢɤɚɰɢɹ_ɜɟɤɬɨɪɚ ȼ2, 1, Ɋɚɞɢɭɫ ɦɟɬɪɨɜ ɛɭɞɟɦ ɢɫɤɚɬɶ ɤɚɤ ɫɭɦɦɭ ɧɚɱɚɥɶɧɨɝɨ 11. ȼɵɱɢɫɥɢɬɶ_ɨɰɟɧɤɭ Ɉ2 ɢ ɫɥɭɱɚɣɧɨɝɨ, ɭɦɧɨɠɟɧɧɨɝɨ ɧɚ ɪɚɞɢɭɫ, 12. ɉɨɩɵɬɤɚ=ɉɨɩɵɬɤɚ+1 ɜɟɤɬɨɪɨɜ. ȿɫɥɢ ɩɨɫɥɟ ɑɢɫɥɨ_ɩɨɩɵɬɨɤ ɫɥɭ- 13. ȿɫɥɢ Ɉ2<Ɉ1 ɬɨ ɩɟɪɟɯɨɞ ɤ ɲɚɝɭ 16 ɱɚɣɧɵɯ ɝɟɧɟɪɚɰɢɣ ɧɟ ɩɪɨɢɡɨɲɥɨ ɭɦɟɧɶɲɟ- 14. ȿɫɥɢ ɉɨɩɵɬɤɚ<=ɑɢɫɥɨ_ɩɨɩɵɬɨɤ ɬɨ ɩɟɪɟɯɨɞ ɤ ɲɚɝɭ 8 ɧɢɹ ɨɰɟɧɤɢ, ɬɨ ɭɦɟɧɶɲɚɟɦ ɪɚɞɢɭɫ. ȿɫɥɢ 15. ɉɟɪɟɯɨɞ ɤ ɲɚɝɭ 18 ɩɪɨɢɡɨɲɥɨ ɭɦɟɧɶɲɟɧɢɟ ɨɰɟɧɤɢ, ɬɨ ɩɨɥɭ- 16. Ɉ1=Ɉ2 ɱɟɧɧɵɣ ɜɟɤɬɨɪ ɨɛɴɹɜɥɹɟɦ ɧɚɱɚɥɶɧɵɦ ɢ 17. ɉɟɪɟɯɨɞ ɤ ɲɚɝɭ 6 ɩɪɨɞɨɥɠɚɟɦ ɩɪɨɰɟɞɭɪɭ ɫ ɬɟɦ ɠɟ ɲɚɝɨɦ. 18. ɑɢɫɥɨ_ɋɦɟɧ_Ɋɚɞɢɭɫɚ= ɑɢɫɥɨ_ɋɦɟɧ_Ɋɚɞɢɭɫɚ+1 ȼɚɠɧɨ, ɱɬɨɛɵ ɩɨɫɥɟɞɨɜɚɬɟɥɶɧɨɫɬɶ ɭɦɟɧɶ- 19. Ɋɚɞɢɭɫ=1/ ɑɢɫɥɨ_ɋɦɟɧ_Ɋɚɞɢɭɫɚ ɲɚɸɳɢɯɫɹ ɪɚɞɢɭɫɨɜ ɨɛɪɚɡɨɜɵɜɚɥɚ ɪɚɫɯɨ- 20. ȿɫɥɢ ɪɚɞɢɭɫ>= Ɇɢɧɢɦɚɥɶɧɵɣ_ɪɚɞɢɭɫ ɬɨ ɩɟɪɟɯɨɞ ɤ ɞɹɳɢɣɫɹ ɪɹɞ. ɉɪɢɦɟɪɨɦ ɬɚɤɨɣ ɩɨɫɥɟɞɨɜɚɲɚɝɭ 6 ɬɟɥɶɧɨɫɬɢ ɦɨɠɟɬ ɫɥɭɠɢɬɶ ɢɫɩɨɥɶɡɨɜɚɧɧɵɣ 21. ɍɫɬɚɧɨɜɢɬɶ_ɩɚɪɚɦɟɬɪɵ ȼ1 ɜ ɩɪɢɦɟɪɟ ɧɚ ɪɢɫ. 2 ɪɹɞ 1 n . 22. Ɉɫɜɨɛɨɞɢɬɶ_ɜɟɤɬɨɪ ȼ1 Ɉɬɦɟɱɟɧ ɪɹɞ ɫɥɭɱɚɟɜ, ɤɨɝɞɚ ɦɟɬɨɞ 23. Ɉɫɜɨɛɨɞɢɬɶ_ɜɟɤɬɨɪ ȼ2 ɫɥɭɱɚɣɧɨɣ ɫɬɪɟɥɶɛɵ ɫ ɭɦɟɧɶɲɟɧɢɟɦ ɪɚɞɢɭ- Ɋɢɫ. 2. Ⱥɥɝɨɪɢɬɦ ɦɟɬɨɞɚ ɫɥɭɱɚɣɧɨɣ ɫɬɪɟɥɶɛɵ ɫ ɭɦɟɧɶɲɟɧɢɟɦ ɪɚɞɢɭɫɚ ɫɚ ɪɚɛɨɬɚɟɬ ɛɵɫɬɪɟɟ ɝɪɚɞɢɟɧɬɧɵɯ ɦɟɬɨɞɨɜ, ɧɨ ɷɬɨ ɫɤɨɪɟɟ ɢɫɤɥɸɱɟɧɢɟ, ɱɟɦ ɩɪɚɜɢɥɨ.
8.2.2.2 Ɇɟɬɨɞ ɩɨɤɨɨɪɞɢɧɚɬɧɨɝɨ ɫɩɭɫɤɚ ɂɞɟɹ ɷɬɨɝɨ ɦɟɬɨɞɚ ɫɨɫɬɨɢɬ ɜ ɬɨɦ, ɱɬɨ ɟɫɥɢ ɜ ɡɚɞɚɱɟ ɫɥɨɠɧɨ ɢɥɢ ɞɨɥɝɨ ɜɵɱɢɫɥɹɬɶ ɝɪɚɞɢɟɧɬ, ɬɨ ɦɨɠɧɨ ɩɨɫɬɪɨɢɬɶ ɜɟɤɬɨɪ, ɨɛɥɚɞɚɸɳɢɣ ɩɪɢɛɥɢɡɢɬɟɥɶɧɨ ɬɟɦɢ ɠɟ ɫɜɨɣɫɬɜɚɦɢ, ɱɬɨ ɢ ɝɪɚɞɢɟɧɬ ɫɥɟɞɭɸɳɢɦ ɩɭɬɟɦ. Ⱦɚɟɦ ɦɚɥɨɟ ɩɨɥɨɠɢɬɟɥɶɧɨɟ ɩɪɢɪɚɳɟɧɢɟ ɩɟɪɜɨɣ ɤɨɨɪɞɢɧɚɬɟ ɜɟɤɬɨɪɚ. ȿɫɥɢ ɨɰɟɧɤɚ ɩɪɢ ɷɬɨɦ ɭɜɟ-
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ɥɢɱɢɥɚɫɶ, ɬɨ ɩɪɨɛɭɟɦ ɨɬɪɢɰɚɬɟɥɶɧɨɟ ɩɪɢɪɚɳɟɧɢɟ. Ⱦɚɥɟɟ ɬɚɤ ɠɟ ɩɨɫɬɭɩɚɟɦ ɫɨ ɜɫɟɦɢ ɨɫɬɚɥɶɧɵɦɢ ɤɨɨɪɞɢɧɚɬɚɦɢ. ȼ ɪɟɡɭɥɶɬɚɬɟ ɩɨɥɭɱɚɟɦ ɜɟɤɬɨɪ, ɜ ɧɚɩɪɚɜɥɟɧɢɢ ɤɨɬɨɪɨɝɨ ɨɰɟɧɤɚ ɭɛɵɜɚɟɬ. Ⱦɥɹ ɜɵɱɢɫɥɟɧɢɹ ɬɚɤɨɝɨ ɜɟɤɬɨɪɚ ɩɨɬɪɟɛɭɟɬɫɹ, ɤɚɤ ɦɢɧɢɦɭɦ, ɫɬɨɥɶɤɨ ɜɵɱɢɫɥɟɧɢɣ ɮɭɧɤɰɢɢ ɨɰɟɧɤɢ, ɫɤɨɥɶɤɨ ɤɨɨɪɞɢɧɚɬ ɭ ɜɟɤɬɨɪɚ. ȼ ɯɭɞɲɟɦ ɫɥɭɱɚɟ ɩɨɬɪɟɛɭɟɬɫɹ ɜ ɞɜɚ ɪɚɡɚ ɛɨɥɶɲɟɟ ɱɢɫɥɨ ɜɵɱɢɫɥɟɧɢɣ ɮɭɧɤɰɢɢ ɨɰɟɧɤɢ. ȼɪɟɦɹ ɠɟ ɧɟɨɛɯɨɞɢɦɨɟ ɞɥɹ ɜɵɱɢɫɥɟɧɢɹ ɝɪɚɞɢɟɧɬɚ ɜ ɫɥɭɱɚɟ ɢɫɩɨɥɶɡɨɜɚɧɢɹ ɞɜɨɣɫɬɜɟɧɧɵɯ ɫɟɬɟɣ ɦɨɠɧɨ ɨɰɟɧɢɬɶ ɤɚɤ 2-3 ɜɵɱɢɫɥɟɧɢɹ ɮɭɧɤɰɢɢ ɨɰɟɧɤɢ. Ɍɚɤɢɦ ɨɛɪɚɡɨɦ, ɭɱɢɬɵɜɚɹ ɫɩɨɫɨɛɧɨɫɬɶ ɞɜɨɣɫɬɜɟɧɧɵɯ ɫɟɬɟɣ ɛɵɫɬɪɨ ɜɵɱɢɫɥɹɬɶ ɝɪɚɞɢɟɧɬ, ɦɨɠɧɨ ɫɞɟɥɚɬɶ ɜɵɜɨɞ ɨ ɧɟɰɟɥɟɫɨɨɛɪɚɡɧɨɫɬɢ ɩɪɢɦɟɧɟɧɢɹ ɦɟɬɨɞɚ ɩɨɤɨɨɪɞɢɧɚɬɧɨɝɨ ɫɩɭɫɤɚ ɜ ɨɛɭɱɟɧɢɢ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ.
8.2.2.3 ɉɨɞɛɨɪ ɨɩɬɢɦɚɥɶɧɨɝɨ ɲɚɝɚ Ⱦɚɧɧɵɣ ɪɚɡɞɟɥ ɩɨɫɜɹɳɟɧ ɨɩɢɫɚɧɢɸ ɦɚɤɪɨɤɨɦɚɧɞɵ Ɉɩɬɢɦɢɡɚɰɢɹ_ɒɚɝɚ. ɗɬɚ ɦɚɤɪɨɤɨɦɚɧɞɚ ɱɚɫɬɨ ɢɫɩɨɥɶɡɭɟɬɫɹ ɜ ɨɩɢɫɚɧɢɢ ɩɪɨɰɟɞɭɪ ɨɛɭɱɟɧɢɹ ɢ ɧɟ ɫɬɨɥɶ ɨɱɟɜɢɞɧɚ ɤɚɤ ɞɪɭɝɢɟ ɦɚɤɪɨɤɨɦɚɧɞɵ. ɉɨɷɬɨɦɭ ɟɟ ɬɟɤɫɬ ɩɪɢɜɟɞɟɧ ɧɚ ɪɢɫ. 3. ɂɞɟɹ ɩɨɞɛɨɪɚ ɨɩɬɢɦɚɥɶɧɨɝɨ ɲɚɝɚ ɫɨɫɬɨɢɬ ɜ ɬɨɦ, ɱɬɨ ɩɪɢ ɧɚɥɢɱɢɢ ɧɚɩɪɚɜɥɟɧɢɹ ɜ ɤɨɬɨɪɨɦ ɩɪɨɢɡɜɨɞɢɬɫɹ ɫɩɭɫɤ (ɢɡɦɟɧɟɧɢɟ ɩɚɪɚɦɟɬɪɨɜ) ɡɚɞɚɱɚ ɦɧɨɝɨɦɟɪɧɨɣ ɨɩɬɢɦɢɡɚɰɢɢ ɜ ɩɪɨɫɬɪɚɧɫɬɜɟ ɩɚɪɚɦɟɬɪɨɜ ɫɜɨɞɢɬɫɹ ɤ ɨɞɧɨɦɟɪɧɨɣ ɨɩɬɢɦɢɡɚɰɢɢ – ɩɨɞɛɨɪɭ ɲɚɝɚ. ɉɭɫɬɶ ɡɚɞɚɧɵ ɧɚɱɚɥɶɧɵɣ ɲɚɝ (ɒ2) ɢ ɧɚɩɪɚɜɥɟɧɢɟ ɫɩɭɫɤɚ (ɚɧɬɢɝɪɚɞɢɟɧɬ ɢɥɢ ɫɥɭɱɚɣɧɨɟ) (ɇ). Ɍɨɝɞɚ ɜɵɱɢɫɥɢɦ ɜɟɥɢɱɢɧɭ Ɉ1 – ɨɰɟɧɤɭ ɜ ɬɟɤɭɳɟɣ ɬɨɱɤɟ ɩɪɨɫɬɪɚɧɫɬɜɚ ɩɚɪɚɦɟɬɪɨɜ. ɂɡɦɟɧɢɜ ɩɚɪɚɦɟɬɪɵ ɧɚ ɜɟɤɬɨɪ ɧɚɩɪɚɜɥɟɧɢɹ, ɭɦɧɨɠɟɧɧɵɣ ɧɚ ɜɟɥɢɱɢɧɭ ɩɪɨɛɧɨɝɨ ɲɚɝɚ, ɜɵɱɢɫɥɢɦ ɜɟɥɢɱɢɧɭ ɨɰɟɧɤɢ ɜ ɧɨɜɨɣ ɬɨɱɤɟ – Ɉ2. ȿɫɥɢ Ɉ2 ɨɤɚɡɚɥɨɫɶ ɦɟɧɶɲɟ ɥɢɛɨ ɪɚɜɧɨ Ɉ1, ɬɨ ɭɜɟɥɢɱɢɜɚɟɦ ɲɚɝ ɢ ɫɧɨɜɚ ɜɵɱɢɫɥɹɟɦ ɨɰɟɧɤɭ. ɉɪɨɞɨɥɠɚɟɦ ɷɬɭ ɩɪɨɰɟɞɭɪɭ ɞɨ ɬɟɯ ɩɨɪ, ɩɨɤɚ ɧɟ ɩɨɥɭɱɢɬɫɹ ɨɰɟɧɤɚ, ɛɨɥɶɲɚɹ ɩɪɟɞɵɞɭɳɟɣ. Ɂɧɚɹ ɬɪɢ ɩɨɫɥɟɞɧɢɯ ɡɧɚɱɟɧɢɹ ɜɟɥɢɱɢɧɵ ɲɚɝɚ ɢ ɨɰɟɧɤɢ, ɢɫɩɨɥɶɡɭɟɦ ɤɜɚɞɪɚɬɢɱɧɭɸ ɨɩɬɢɦɢɡɚɰɢɸ – ɩɨ ɬɪɟɦ ɬɨɱɤɚɦ ɩɨɫɬɪɨɢɦ ɩɚɪɚɛɨɥɭ ɢ ɫɥɟɞɭɸɳɢɣ ɲɚɝ ɫɞɟɥɚɟɦ ɜ ɜɟɪɲɢɧɭ ɩɚɪɚɛɨɥɵ. ɉɨɫɥɟ ɧɟɫɤɨɥɶɤɢɯ ɲɚɝɨɜ ɤɜɚɞɪɚɬɢɱɧɨɣ ɨɩɬɢɦɢɡɚɰɢɢ ɩɨɥɭɱɚɟɦ ɩɪɢɛɥɢɠɟɧɧɨɟ ɡɧɚɱɟɧɢɟ ɨɩɬɢɦɚɥɶɧɨɝɨ ɲɚɝɚ. ȿɫɥɢ ɩɨɫɥɟ ɩɟɪɜɨɝɨ ɩɪɨɛɧɨɝɨ ɲɚɝɚ ɩɨɥɭɱɢɥɨɫɶ Ɉ2 ɛɨɥɶɲɟɟ Ɉ1, ɬɨ ɭɦɟɧɶɲɚɟɦ ɲɚɝ ɞɨ ɬɟɯ ɩɨɪ, ɩɨɤɚ ɧɟ ɩɨɥɭɱɢɦ ɨɰɟɧɤɭ, ɦɟɧɶɲɟ ɱɟɦ Ɉ1. ɉɨɫɥɟ ɷɬɨɝɨ ɩɪɨɢɡɜɨɞɢɦ ɤɜɚɞɪɚɬɢɱɧɭɸ ɨɩɬɢɦɢɡɚɰɢɸ.
1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37. 38. 39. 40.
ɋɨɡɞɚɬɶ_ɜɟɤɬɨɪ ȼ ɋɨɯɪɚɧɢɬɶ_ɜɤɬɨɪ ȼ ȼɵɱɢɫɥɢɬɶ_ɨɰɟɧɤɭ Ɉ1 ɒ1=0 Ɇɨɞɢɮɢɤɚɰɢɹ_ɜɟɤɬɨɪɚ ɇ, 1, ɒ2 ȼɵɱɢɫɥɢɬɶ_ɨɰɟɧɤɭ Ɉ2 ȿɫɥɢ Ɉ1<Ɉ2 ɬɨ ɩɟɪɟɯɨɞ ɤ ɲɚɝɭ 15 ɒ3=ɒ2*3 ɍɫɬɚɧɨɜɢɬɶ_ɩɚɪɚɦɟɬɪɵ ȼ Ɇɨɞɢɮɢɤɚɰɢɹ_ɜɟɤɬɨɪɚ ɇ, 1, ɒ3 ȼɵɱɢɫɥɢɬɶ_ɨɰɟɧɤɭ Ɉ3 ȿɫɥɢ Ɉ3>Ɉ2 ɬɨ ɩɟɪɟɯɨɞ ɤ ɲɚɝɭ 21 Ɉ1=Ɉ2 Ɉ2=Ɉ3 ɒ1=ɒ2 ɒ2=ɒ3 ɉɟɪɟɯɨɞ ɤ ɲɚɝɭ 8 ɒ3=ɒ2 Ɉ3=Ɉ2 ɒ2=ɒ3/3 ɍɫɬɚɧɨɜɢɬɶ_ɩɚɪɚɦɟɬɪɵ ȼ Ɇɨɞɢɮɢɤɚɰɢɹ_ɜɟɤɬɨɪɚ ɇ, 1, ɒ2 ȼɵɱɢɫɥɢɬɶ_ɨɰɟɧɤɭ Ɉ3 ȿɫɥɢ Ɉ2>=Ɉ1 ɬɨ ɩɟɪɟɯɨɞ ɤ ɲɚɝɭ 15 ɑɢɫɥɨ_ɩɚɪɚɛɨɥ=0 ɒ=((ɒ3ɒ3-ɒ2ɒ2)Ɉ1+(ɒ1ɒ1-ɒ3ɒ3)Ɉ2 +(ɒ2ɒ2ɒ1ɒ1)Ɉ3)/(2((ɒ3-ɒ2)Ɉ1+(ɒ1-ɒ3)Ɉ2 +(ɒ2-ɒ1)Ɉ3)) ɍɫɬɚɧɨɜɢɬɶ_ɩɚɪɚɦɟɬɪɵ ȼ Ɇɨɞɢɮɢɤɚɰɢɹ_ɜɟɤɬɨɪɚ ɇ, 1, ɒ ȼɵɱɢɫɥɢɬɶ_ɨɰɟɧɤɭ Ɉ ȿɫɥɢ ɒ>ɒ2 ɬɨ ɩɟɪɟɯɨɞ ɤ ɲɚɝɭ 32 ȿɫɥɢ Ɉ>Ɉ2 ɬɨ ɩɟɪɟɯɨɞ ɤ ɲɚɝɭ 30 ɒ3=ɒ2 Ɉ3=Ɉ2 Ɉ2=Ɉ ɒ2=ɒ ɉɟɪɟɯɨɞ ɤ ɲɚɝɭ 36 ɒ1=ɒ Ɉ1=Ɉ ɉɟɪɟɯɨɞ ɤ ɲɚɝɭ 36 ȿɫɥɢ Ɉ>Ɉ2 ɬɨ ɩɟɪɟɯɨɞ ɤ ɲɚɝɭ 35 ɒ3=ɒ2 Ɉ3=Ɉ2 Ɉ2=Ɉ ɒ2=ɒ ɉɟɪɟɯɨɞ ɤ ɲɚɝɭ 36 ɒ1=ɒ Ɉ1=Ɉ ɑɢɫɥɨ_ɩɚɪɚɛɨɥ= ɑɢɫɥɨ_ɩɚɪɚɛɨɥ+1 ȿɫɥɢ ɑɢɫɥɨ_ɩɚɪɚɛɨɥ<Ɇɚɤɫɢɦɚɥɶɧɨɟ_ɑɢɫɥɨ_ɉɚɪɚɛɨɥ ɬɨ ɩɟɪɟɯɨɞ ɤ ɲɚɝɭ 22 ɍɫɬɚɧɨɜɢɬɶ_ɩɚɪɚɦɟɬɪɵ ȼ Ɇɨɞɢɮɢɤɚɰɢɹ_ɜɟɤɬɨɪɚ ɇ, 1, ɒ2 Ɉɫɜɨɛɨɞɢɬɶ_ɜɟɤɬɨɪ ȼ Ɋɢɫ. 3. Ⱥɥɝɨɪɢɬɦ ɨɩɬɢɦɢɡɚɰɢɢ ɲɚɝɚ
8.2.2.4 Ɇɟɬɨɞ ɫɥɭɱɚɣɧɨɝɨ ɩɨɢɫɤɚ ɗɬɨɬ ɦɟɬɨɞ ɩɨɯɨɠ ɧɚ ɦɟɬɨɞ ɫɥɭɱɚɣɧɨɣ ɫɬɪɟɥɶɛɵ ɫ ɭɦɟɧɶɲɟɧɢɟɦ ɪɚɞɢɭɫɚ, ɨɞɧɚɤɨ ɜ ɟɝɨ ɨɫɧɨɜɟ ɥɟɠɢɬ ɞɪɭɝɚɹ ɢɞɟɹ – ɫɝɟɧɟɪɢɪɭɟɦ ɫɥɭɱɚɣɧɵɣ ɜɟɤɬɨɪ ɢ ɛɭɞɟɦ ɢɫɩɨɥɶɡɨɜɚɬɶ ɟɝɨ ɜɦɟɫɬɨ ɝɪɚɞɢɟɧɬɚ. ɗɬɨɬ ɦɟɬɨɞ ɢɫɩɨɥɶɡɭɟɬ ɨɞɧɨɦɟɪɧɭɸ ɨɩɬɢɦɢɡɚɰɢɸ – ɩɨɞɛɨɪ ɲɚɝɚ. Ɉɞɧɨɦɟɪɧɚɹ ɨɩɬɢɦɢɡɚɰɢɹ ɨɩɢɫɚɧɚ ɜ ɪɚɡɞɟɥɟ
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«Ɉɞɧɨɦɟɪɧɚɹ ɨɩɬɢɦɢɡɚɰɢɹ». ɉɪɨɰɟɞɭɪɚ ɫɥɭɱɚɣɧɨɝɨ ɩɨɢɫɤɚ ɩɪɢɜɟɞɟɧɚ ɧɚ ɪɢɫ. 4. ȼ ɷɬɨɦ ɦɟɬɨɞɟ ɟɫɬɶ ɞɜɚ ɩɚɪɚɦɟɬɪɚ, ɡɚɞɚɜɚɟɦɵɯ ɩɨɥɶɡɨɜɚɬɟɥɟɦ. ɑɢɫɥɨ_ɩɨɩɵɬɨɤ – ɱɢɫɥɨ ɧɟɭɞɚɱɧɵɯ ɩɪɨɛɧɵɯ ɝɟɧɟɪɚɰɢɣ ɜɟɤɬɨɪɚ ɩɪɢ ɨɞɧɨɦ ɪɚɞɢɭɫɟ. Ɇɢɧɢɦɚɥɶɧɵɣ_ɪɚɞɢɭɫ – ɦɢɧɢɦɚɥɶɧɨɟ ɡɧɚɱɟɧɢɟ ɪɚɞɢɭɫɚ, ɩɪɢ ɤɨɬɨɪɨɦ ɩɪɨɞɨɥɠɚɟɬ ɪɚɛɨɬɚɬɶ ɚɥɝɨɪɢɬɦ. ɂɞɟɹ ɷɬɨɝɨ ɦɟɬɨɞɚ ɫɨɫɬɨɢɬ ɜ 1. ɋɨɡɞɚɬɶ_ɜɟɤɬɨɪ ɇ ɫɥɟɞɭɸɳɟɦ. Ɂɚɞɚɞɢɦɫɹ ɧɚɱɚɥɶɧɵɦ ɫɨ2. ɑɢɫɥɨ_ɋɦɟɧ_Ɋɚɞɢɭɫɚ=1 ɫɬɨɹɧɢɟɦ ɜɟɤɬɨɪɚ ɩɚɪɚɦɟɬɪɨɜ. ɇɨɜɵɣ 3. ɉɨɩɵɬɤɚ=0 ɜɟɤɬɨɪ ɩɚɪɚɦɟɬɪɨɜ ɛɭɞɟɦ ɢɫɤɚɬɶ ɤɚɤ 4. Ɋɚɞɢɭɫ=1/ ɑɢɫɥɨ_ɋɦɟɧ_Ɋɚɞɢɭɫɚ ɫɭɦɦɭ ɧɚɱɚɥɶɧɨɝɨ ɢ ɫɥɭɱɚɣɧɨɝɨ, ɭɦɧɨ5. ɋɥɭɱɚɣɧɵɣ_ɜɟɤɬɨɪ ɇ ɠɟɧɧɨɝɨ ɧɚ ɪɚɞɢɭɫ, ɜɟɤɬɨɪɨɜ. ȿɫɥɢ ɩɨɫɥɟ 6. Ɉɩɬɢɦɢɡɚɰɢɹ ɲɚɝɚ ɇ Ɋɚɞɢɭɫ ɑɢɫɥɨ_ɩɨɩɵɬɨɤ ɫɥɭɱɚɣɧɵɯ ɝɟɧɟɪɚɰɢɣ ɧɟ 7. ɉɨɩɵɬɤɚ=ɉɨɩɵɬɤɚ+1 ɩɪɨɢɡɨɲɥɨ ɭɦɟɧɶɲɟɧɢɹ ɨɰɟɧɤɢ, ɬɨ 8. ȿɫɥɢ Ɋɚɞɢɭɫ=0 ɬɨ ɉɨɩɵɬɤɚ=0 ɭɦɟɧɶɲɚɟɦ ɪɚɞɢɭɫ. ȿɫɥɢ ɩɪɨɢɡɨɲɥɨ 9. ȿɫɥɢ ɉɨɩɵɬɤɚ<=ɑɢɫɥɨ_ɩɨɩɵɬɨɤ ɬɨ ɩɟɪɟɯɨɞ ɤ ɲɚɝɭ 4 ɭɦɟɧɶɲɟɧɢɟ ɨɰɟɧɤɢ, ɬɨ ɩɨɥɭɱɟɧɧɵɣ 10. ɑɢɫɥɨ_ɋɦɟɧ_Ɋɚɞɢɭɫɚ= ɑɢɫɥɨ_ɋɦɟɧ_Ɋɚɞɢɭɫɚ+1 ɜɟɤɬɨɪ ɨɛɴɹɜɥɹɟɦ ɧɚɱɚɥɶɧɵɦ ɢ ɩɪɨɞɨɥ11. Ɋɚɞɢɭɫ=1/ ɑɢɫɥɨ_ɋɦɟɧ_Ɋɚɞɢɭɫɚ ɠɚɟɦ ɩɪɨɰɟɞɭɪɭ ɫ ɬɟɦ ɠɟ ɲɚɝɨɦ. ȼɚɠɧɨ, 12. ȿɫɥɢ Ɋɚɞɢɭɫ>= Ɇɢɧɢɦɚɥɶɧɵɣ_ɪɚɞɢɭɫ ɬɨ ɩɟɪɟɯɨɞ ɤ ɱɬɨɛɵ ɩɨɫɥɟɞɨɜɚɬɟɥɶɧɨɫɬɶ ɭɦɟɧɶɲɚɸɲɚɝɭ 3 ɳɢɯɫɹ ɪɚɞɢɭɫɨɜ ɨɛɪɚɡɨɜɵɜɚɥɚ ɪɚɫɯɨɞɹ13. Ɉɫɜɨɛɨɞɢɬɶ_ɜɟɤɬɨɪ ɇ ɳɢɣɫɹ ɪɹɞ. ɉɪɢɦɟɪɨɦ ɬɚɤɨɣ ɩɨɫɥɟɞɨɜɚɊɢɫ. 4. Ⱥɥɝɨɪɢɬɦ ɦɟɬɨɞɚ ɫɥɭɱɚɣɧɨɝɨ ɩɨɢɫɤɚ ɬɟɥɶɧɨɫɬɢ ɦɨɠɟɬ ɫɥɭɠɢɬɶ ɢɫɩɨɥɶɡɨɜɚɧɧɵɣ ɜ ɩɪɢɦɟɪɟ ɧɚ ɪɢɫ. 4 ɪɹɞ 1 n .
8.2.2.5 Ɇɟɬɨɞ ɇɟɥɞɟɪɚ-Ɇɢɞɚ ɗɬɨɬ ɦɟɬɨɞ ɹɜɥɹɟɬɫɹ ɨɞɧɢɦ ɢɡ ɧɚɢɛɨɥɟɟ ɛɵɫɬɪɵɯ ɢ ɧɚɢɛɨɥɟɟ ɧɚɞɟɠɧɵɯ ɧɟ ɝɪɚɞɢɟɧɬɧɵɯ ɦɟɬɨɞɨɜ ɦɧɨɝɨɦɟɪɧɨɣ ɨɩɬɢɦɢɡɚɰɢɢ. ɂɞɟɹ ɷɬɨɝɨ ɦɟɬɨɞɚ ɫɨɫɬɨɢɬ ɜ ɫɥɟɞɭɸɳɟɦ. ȼ ɩɪɨɫɬɪɚɧɫɬɜɟ ɨɩɬɢɦɢɡɢɪɭɟɦɵɯ ɩɚɪɚɦɟɬɪɨɜ ɝɟɧɟɪɢɪɭɟɬɫɹ ɫɥɭɱɚɣɧɚɹ ɬɨɱɤɚ. Ɂɚɬɟɦ ɫɬɪɨɢɬɫɹ n-ɦɟɪɧɵɣ ɫɢɦɩɥɟɤɫ ɫ ɰɟɧɬɪɨɦ ɜ ɷɬɨɣ ɬɨɱɤɟ, ɢ ɞɥɢɧɨɣ ɫɬɨɪɨɧɵ l. Ⱦɚɥɟɟ ɜ ɤɚɠɞɨɣ ɢɡ ɜɟɪɲɢɧ ɫɢɦɩɥɟɤɫɚ ɜɵɱɢɫɥɹɟɬɫɹ ɡɧɚɱɟɧɢɟ ɨɰɟɧɤɢ. ȼɵɛɢɪɚɟɬɫɹ ɜɟɪɲɢɧɚ ɫ ɧɚɢɛɨɥɶɲɟɣ ɨɰɟɧɤɨɣ. ȼɵɱɢɫɥɹɟɬɫɹ ɰɟɧɬɪ ɬɹɠɟɫɬɢ ɨɫɬɚɥɶɧɵɯ n ɜɟɪɲɢɧ. ɉɪɨɜɨɞɢɬɫɹ ɨɩɬɢɦɢɡɚɰɢɹ ɲɚɝɚ ɜ ɧɚɩɪɚɜɥɟɧɢɢ ɨɬ ɧɚɢɯɭɞɲɟɣ ɜɟɪɲɢɧɵ ɤ ɰɟɧɬɪɭ ɬɹɠɟɫɬɢ ɨɫɬɚɥɶɧɵɯ ɜɟɪɲɢɧ. ɗɬɚ ɩɪɨɰɟɞɭɪɚ ɩɨɜɬɨɪɹɟɬɫɹ ɞɨ ɬɟɯ ɩɨɪ, ɩɨɤɚ ɧɟ ɨɤɚɠɟɬɫɹ, ɱɬɨ ɨɩɬɢɦɢɡɚɰɢɹ ɧɟ ɢɡɦɟɧɹɟɬ ɩɨɥɨɠɟɧɢɹ ɜɟɪɲɢɧɵ. ɉɨɫɥɟ ɷɬɨɝɨ ɜɵɛɢɪɚɟɬɫɹ ɜɟɪɲɢɧɚ ɫ ɧɚɢɥɭɱɲɟɣ ɨɰɟɧɤɨɣ ɢ ɜɨɤɪɭɝ ɧɟɟ ɫɧɨɜɚ ɫɬɪɨɢɬɫɹ ɫɢɦɩɥɟɤɫ ɫ ɦɟɧɶɲɢɦɢ ɪɚɡɦɟɪɚɦɢ (ɧɚɩɪɢɦɟɪ l / 2 ). ɉɪɨɰɟɞɭɪɚ ɩɪɨɞɨɥɠɚɟɬɫɹ ɞɨ ɬɟɯ ɩɨɪ, ɩɨɤɚ ɪɚɡɦɟɪ ɫɢɦɩɥɟɤɫɚ, ɤɨɬɨɪɵɣ ɧɟɨɛɯɨɞɢɦɨ ɩɨɫɬɪɨɢɬɶ, ɧɟ ɨɤɚɠɟɬɫɹ ɦɟɧɶɲɟ ɬɪɟɛɭɟɦɨɣ ɬɨɱɧɨɫɬɢ. Ɉɞɧɚɤɨ, ɧɟɫɦɨɬɪɹ ɧɚ ɫɜɨɸ ɧɚɞɟɠɧɨɫɬɶ, ɩɪɢɦɟɧɟɧɢɟ ɷɬɨɝɨ ɦɟɬɨɞɚ ɤ ɨɛɭɱɟɧɢɸ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ɡɚɬɪɭɞɧɟɧɨ ɛɨɥɶɲɨɣ ɪɚɡɦɟɪɧɨɫɬɶɸ ɩɪɨɫɬɪɚɧɫɬɜɚ ɩɚɪɚɦɟɬɪɨɜ.
8.2.3 Ƚɪɚɞɢɟɧɬɧɵɟ ɦɟɬɨɞɵ ɨɛɭɱɟɧɢɹ ɂɡɭɱɟɧɢɸ ɝɪɚɞɢɟɧɬɧɵɯ ɦɟɬɨɞɨɜ ɨɛɭɱɟɧɢɹ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ɩɨɫɜɹɳɟɧɨ ɦɧɨɠɟɫɬɜɨ ɪɚɛɨɬ [47, 64, 90] (ɫɨɫɥɚɬɶɫɹ ɧɚ ɜɫɟ ɪɚɛɨɬɵ ɩɨ ɷɬɨɣ ɬɟɦɟ ɧɟ ɩɪɟɞɫɬɚɜɥɹɟɬɫɹ ɜɨɡɦɨɠɧɵɦ, ɩɨɷɬɨɦɭ ɞɚɧɚ ɫɫɵɥɤɚ ɧɚ ɪɚɛɨɬɵ, ɝɞɟ ɷɬɚ ɬɟɦɚ ɢɫɫɥɟɞɨɜɚɧɚ ɧɚɢɛɨɥɟɟ ɞɟɬɚɥɶɧɨ). Ʉɪɨɦɟ ɬɨɝɨ, ɫɭɳɟɫɬɜɭɟɬ ɦɧɨɠɟɫɬɜɨ ɩɭɛɥɢɤɚɰɢɣ, ɩɨɫɜɹɳɟɧɧɵɯ ɝɪɚɞɢɟɧɬɧɵɦ ɦɟɬɨɞɚɦ ɩɨɢɫɤɚ ɦɢɧɢɦɭɦɚ ɮɭɧɤɰɢɢ [48, 103] (ɤɚɤ ɢ ɜ ɩɪɟɞɵɞɭɳɟɦ ɫɥɭɱɚɟ, ɫɫɵɥɤɢ ɞɚɧɵ ɬɨɥɶɤɨ ɧɚ ɞɜɟ ɪɚɛɨɬɵ, ɤɨɬɨɪɵɟ ɩɨɤɚɡɚɥɢɫɶ ɧɚɢɛɨɥɟɟ ɭɞɚɱɧɵɦɢ). Ⱦɚɧɧɵɣ ɪɚɡɞɟɥ ɧɟ ɩɪɟɬɟɧɞɭɟɬ ɧɚ ɤɚɤɭɸ-ɥɢɛɨ ɩɨɥɧɨɬɭ ɪɚɫɫɦɨɬɪɟɧɢɹ ɝɪɚɞɢɟɧɬɧɵɯ ɦɟɬɨɞɨɜ ɩɨɢɫɤɚ ɦɢɧɢɦɭɦɚ. ȼ ɧɟɦ ɩɪɢɜɟɞɟɧɵ ɬɨɥɶɤɨ ɧɟɫɤɨɥɶɤɨ ɦɟɬɨɞɨɜ, ɩɪɢɦɟɧɹɜɲɢɯɫɹ ɜ ɪɚɛɨɬɟ ɝɪɭɩɩɨɣ «ɇɟɣɪɨɄɨɦɩ». ȼɫɟ ɝɪɚɞɢɟɧɬɧɵɟ ɦɟɬɨɞɵ ɨɛɴɟɞɢɧɟɧɵ ɢɫɩɨɥɶɡɨɜɚɧɢɟɦ ɝɪɚɞɢɟɧɬɚ ɤɚɤ ɨɫɧɨɜɵ ɞɥɹ ɜɵɱɢɫɥɟɧɢɹ ɧɚɩɪɚɜɥɟɧɢɹ ɫɩɭɫɤɚ.
8.2.3.1 Ɇɟɬɨɞ ɧɚɢɫɤɨɪɟɣɲɟɝɨ ɫɩɭɫɤɚ ɇɚɢɛɨɥɟɟ ɢɡɜɟɫɬɧɵɦ ɫɪɟɞɢ ɝɪɚɞɢɟɧɬɧɵɯ ɦɟɬɨɞɨɜ ɹɜɥɹɟɬɫɹ ɦɟɬɨɞ ɧɚɢɫɤɨɪɟɣɲɟɝɨ ɫɩɭɫɤɚ. ɂɞɟɹ ɷɬɨɝɨ ɦɟɬɨɞɚ ɩɪɨɫɬɚ: ɩɨɫɤɨɥɶɤɭ ɜɟɤɬɨɪ ɝɪɚɞɢɟɧɬɚ ɭɤɚɡɵɜɚɟɬ ɧɚɩɪɚɜɥɟɧɢɟ ɧɚɢɫɤɨɪɟɣɲɟɝɨ ɜɨɡɪɚɫɬɚɧɢɹ ɮɭɧɤɰɢɢ, ɬɨ ɦɢɧɢɦɭɦ ɫɥɟɞɭɟɬ ɢɫɤɚɬɶ ɜ ɨɛɪɚɬɧɨɦ ɧɚɩɪɚɜɥɟɧɢɢ. ɉɨɫɥɟɞɨɜɚɬɟɥɶɧɨɫɬɶ ɞɟɣɫɬɜɢɣ ɩɪɢɜɟɞɟɧɚ ɧɚ ɪɢɫ. 5.
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ȼɵɱɢɫɥɢɬɶ_ɨɰɟɧɤɭ Ɉ2 Ɉ1=Ɉ2 ȼɵɱɢɫɥɢɬɶ_ɝɪɚɞɢɟɧɬ Ɉɩɬɢɦɢɡɚɰɢɹ ɲɚɝɚ ɉɭɫɬɨɣ_ɭɤɚɡɚɬɟɥɶ ɒɚɝ ȼɵɱɢɫɥɢɬɶ_ɨɰɟɧɤɭ Ɉ2 ȿɫɥɢ Ɉ1-Ɉ2<Ɍɨɱɧɨɫɬɶ ɬɨ ɩɟɪɟɯɨɞ ɤ ɲɚɝɭ 2 Ɋɢɫ. 5. Ɇɟɬɨɞ ɧɚɢɫɤɨɪɟɣɲɟɝɨ ɫɩɭɫɤɚ
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ɗɬɨɬ ɦɟɬɨɞ ɪɚɛɨɬɚɟɬ, ɤɚɤ ɩɪɚɜɢɥɨ, ɧɚ ɩɨɪɹɞɨɤ ɛɵɫɬɪɟɟ ɦɟɬɨɞɨɜ ɫɥɭɱɚɣɧɨɝɨ ɩɨɢɫɤɚ. Ɉɧ ɢɦɟɟɬ ɞɜɚ ɩɚɪɚɦɟɬɪɚ – Ɍɨɱɧɨɫɬɶ, ɩɨɤɚɡɵɜɚɸɳɢɣ, ɱɬɨ ɟɫɥɢ ɢɡɦɟɧɟɧɢɟ ɨɰɟɧɤɢ ɡɚ ɲɚɝ ɦɟɬɨɞɚ ɦɟɧɶɲɟ ɱɟɦ Ɍɨɱɧɨɫɬɶ, ɬɨ ɨɛɭɱɟɧɢɟ ɨɫɬɚɧɚɜɥɢɜɚɟɬɫɹ; ɒɚɝ – ɧɚɱɚɥɶɧɵɣ ɲɚɝ ɞɥɹ ɨɩɬɢɦɢɡɚɰɢɢ ɲɚɝɚ. Ɂɚɦɟɬɢɦ, ɱɬɨ ɲɚɝ ɩɨɫɬɨɹɧɧɨ ɢɡɦɟɧɹɟɬɫɹ ɜ ɯɨɞɟ ɨɩɬɢɦɢɡɚɰɢɢ ɲɚɝɚ. Ɉɫɬɚɧɨɜɢɦɫɹ ɧɚ ɨɫɧɨɜɧɵɯ ɧɟɞɨɫɬɚɬɤɚɯ ɷɬɨɝɨ ɦɟɬɨɞɚ. ȼɨ-ɩɟɪɜɵɯ, ɷɬɢ ɦɟɬɨɞɨɦ ɧɚɯɨɞɢɬɫɹ ɬɨɬ ɦɢɧɢɦɭɦ, ɜ ɨɛɥɚɫɬɶ ɩɪɢɬɹɠɟɧɢɹ ɤɨɬɨɪɨɝɨ ɩɨɩɚɞɟɬ ɧɚɱɚɥɶɧɚɹ ɬɨɱɤɚ. ɗɬɨɬ ɦɢɧɢɦɭɦ ɦɨɠɟɬ ɧɟ ɛɵɬɶ ɝɥɨɛɚɥɶɧɵɦ. ɋɭɳɟɫɬɜɭɟɬ ɧɟɫɤɨɥɶɤɨ ɫɩɨɫɨɛɨɜ ɜɵɯɨɞɚ ɢɡ ɷɬɨɝɨ ɩɨɥɨɠɟɧɢɹ. ɇɚɢɛɨɥɟɟ ɩɪɨɫɬɨɣ ɢ ɞɟɣɫɬɜɟɧɧɵɣ – ɫɥɭɱɚɣɧɨɟ ɢɡɦɟɧɟɧɢɟ ɩɚɪɚɦɟɬɪɨɜ ɫ ɞɚɥɶɧɟɣɲɢɦ ɩɨɜɬɨɪɧɵɦ ɨɛɭɱɟɧɢɟ ɦɟɬɨɞɨɦ ɧɚɢɫɤɨɪɟɣɲɟɝɨ ɫɩɭɫɤɚ. Ʉɚɤ ɩɪɚɜɢɥɨ, ɷɬɨɬ ɦɟɬɨɞ ɩɨɡɜɨɥɹɟɬ ɡɚ ɧɟɫɤɨɥɶɤɨ ɰɢɤɥɨɜ ɨɛɭɱɟɧɢɹ ɫ ɩɨɫɥɟɞɭɸɳɢɦ ɫɥɭɱɚɣɧɵɦ ɢɡɦɟɧɟɧɢɟɦ ɩɚɪɚɦɟɬɪɨɜ ɧɚɣɬɢ ɝɥɨɛɚɥɶɧɵɣ ɦɢɧɢɦɭɦ. ɚ) ȼɬɨɪɵɦ ɫɟɪɶɟɡɧɵɦ ɧɟɞɨɫɬɚɬɤɨɦ ɦɟɬɨɞɚ ɧɚɢɫɤɨɪɟɣɲɟɝɨ ɫɩɭɫɤɚ ɹɜɥɹɟɬɫɹ ɟɝɨ ɱɭɜɫɬɜɢɬɟɥɶɧɨɫɬɶ ɤ ɮɨɪɦɟ ɨɤɪɟɫɬɧɨɫɬɢ ɦɢɧɢɦɭɦɚ. ɇɚ ɪɢɫ. 6ɚ ɩɪɨɢɥɥɸɫɬɪɢɪɨɜɚɧɚ ɬɪɚɟɤɬɨɪɢɹ ɫɩɭɫɤɚ ɩɪɢ ɢɫɩɨɥɶɡɨɜɚɧɢɢ ɦɟɬɨɞɚ ɧɚɢɫɤɨɪɟɣɲɟɝɨ ɫɩɭɫɤɚ, ɜ ɫɥɭɱɚɟ, ɟɫɥɢ ɜ ɨɤɪɟɫɬɧɨɫɬɢ ɦɢɧɢɦɭɦɚ ɥɢɧɢɢ ɭɪɨɜɛ) ɧɹ ɮɭɧɤɰɢɢ ɨɰɟɧɤɢ ɹɜɥɹɸɬɫɹ ɤɪɭɝɚɦɢ (ɪɚɫɫɦɚɬɪɢɜɚɟɬɫɹ ɞɜɭɦɟɪɧɵɣ ɫɥɭɱɚɣ). ȼ ɷɬɨɦ ɫɥɭɱɚɟ ɦɢɧɢɦɭɦ ɞɨɫɬɢɝɚɟɬɫɹ ɡɚ ɨɞɢɧ ɲɚɝ. ɇɚ ɪɢɫ. 6ɛ ɩɪɢɜɟɞɟɧɚ ɬɪɚɟɤɬɨɪɢɹ ɦɟɬɨɞɚ ɧɚɢɫɤɨɪɟɣɲɟɝɨ ɫɩɭɫɤɚ ɜ ɫɥɭɱɚɟ ɷɥɥɢɩɬɢɱɟɫɤɢɯ ɥɢɧɢɣ ɭɪɨɜɧɹ. ȼɢɞɧɨ, ɱɬɨ ɜ ɜ) ɷɬɨɣ ɫɢɬɭɚɰɢɢ ɡɚ ɨɞɢɧ ɲɚɝ ɦɢɧɢɦɭɦ ɞɨɫɬɢɝɚɟɬɫɹ Ɋɢɫ. 6. Ɍɪɚɟɤɬɨɪɢɢ ɫɩɭɫɤɚ ɩɪɢ ɪɚɡɥɢɱɧɵɯ ɤɨɧɮɢɬɨɥɶɤɨ ɢɡ ɬɨɱɟɤ, ɪɚɫɩɨɥɨɠɟɧɧɵɯ ɧɚ ɨɫɹɯ ɷɥɥɢɩɫɨɜ. ɝɭɪɚɰɢɹɯ ɨɤɪɟɫɬɧɨɫɬɢ ɦɢɧɢɦɭɦɚ ɢ ɪɚɡɧɵɯ ɦɟɬɨɂɡ ɥɸɛɨɣ ɞɪɭɝɨɣ ɬɨɱɤɢ ɫɩɭɫɤ ɛɭɞɟɬ ɩɪɨɢɫɯɨɞɢɬɶ ɞɚɯ ɨɩɬɢɦɢɡɚɰɢɢ. ɩɨ ɥɨɦɚɧɨɣ, ɤɚɠɞɨɟ ɡɜɟɧɨ ɤɨɬɨɪɨɣ ɨɪɬɨɝɨɧɚɥɶɧɨ ɤ ɫɨɫɟɞɧɢɦ ɡɜɟɧɶɹɦ, ɚ ɞɥɢɧɚ ɡɜɟɧɶɟɜ ɭɛɵɜɚɟɬ. Ʌɟɝɤɨ ɩɨɤɚɡɚɬɶ ɱɬɨ ɞɥɹ ɬɨɱɧɨɝɨ ɞɨɫɬɢɠɟɧɢɹ ɦɢɧɢɦɭɦɚ ɩɨɬɪɟɛɭɟɬɫɹ ɛɟɫɤɨɧɟɱɧɨɟ ɱɢɫɥɨ ɲɚɝɨɜ ɦɟɬɨɞɚ ɝɪɚɞɢɟɧɬɧɨɝɨ ɫɩɭɫɤɚ. ɗɬɨɬ ɷɮɮɟɤɬ ɩɨɥɭɱɢɥ ɧɚɡɜɚɧɢɟ ɨɜɪɚɠɧɨɝɨ, ɚ ɦɟɬɨɞɵ ɨɩɬɢɦɢɡɚɰɢɢ, ɩɨɡɜɨɥɹɸɳɢɟ ɛɨɪɨɬɶɫɹ ɫ ɷɬɢɦ ɷɮɮɟɤɬɨɦ – ɚɧɬɢɨɜɪɚɠɧɵɯ.
8.2.3.2 kParTan Ɉɞɧɢɦ ɢɡ ɩɪɨɫɬɟɣɲɢɯ ɚɧɬɢɨɜ1. ɋɨɡɞɚɬɶ_ɜɟɤɬɨɪ ȼ1 ɪɚɠɧɵɯ ɦɟɬɨɞɨɜ ɹɜɥɹɟɬɫɹ ɦɟɬɨɞ kParTan. 2. ɋɨɡɞɚɬɶ_ɜɟɤɬɨɪ ȼ2 ɂɞɟɹ ɦɟɬɨɞɚ ɫɨɫɬɨɢɬ ɜ ɬɨɦ, ɱɬɨɛɵ ɡɚ3. ɒɚɝ=1 ɩɨɦɧɢɬɶ ɧɚɱɚɥɶɧɭɸ ɬɨɱɤɭ, ɡɚɬɟɦ ɜɵɩɨɥ4. ȼɵɱɢɫɥɢɬɶ_ɨɰɟɧɤɭ Ɉ2 ɧɢɬɶ k ɲɚɝɨɜ ɨɩɬɢɦɢɡɚɰɢɢ ɩɨ ɦɟɬɨɞɭ 5. ɋɨɯɪɚɧɢɬɶ_ɜɟɤɬɨɪ ȼ1 ɧɚɢɫɤɨɪɟɣɲɟɝɨ ɫɩɭɫɤɚ, ɡɚɬɟɦ ɫɞɟɥɚɬɶ 6. Ɉ1=Ɉ2 ɲɚɝ ɨɩɬɢɦɢɡɚɰɢɢ ɩɨ ɧɚɩɪɚɜɥɟɧɢɸ ɢɡ 7. N=0 ɧɚɱɚɥɶɧɨɣ ɬɨɱɤɢ ɜ ɤɨɧɟɱɧɭɸ. Ɉɩɢɫɚɧɢɟ 8. ȼɵɱɢɫɥɢɬɶ_ɝɪɚɞɢɟɧɬ ɦɟɬɨɞɚ ɩɪɢɜɟɞɟɧɨ ɧɚ ɪɢɫ 7. ɇɚ ɪɢɫ 6ɜ 9. Ɉɩɬɢɦɢɡɚɰɢɹ_ɲɚɝɚ ɉɭɫɬɨɣ_ɭɤɚɡɚɬɟɥɶ ɒɚɝ ɩɪɢɜɟɞɟɧ ɨɞɢɧ ɲɚɝ ɨɩɬɢɦɢɡɚɰɢɢ ɩɨ ɦɟ10. N=N+1 ɬɨɞɭ 2ParTan. ȼɢɞɧɨ, ɱɬɨ ɩɨɫɥɟ ɲɚɝɚ 11. ȿɫɥɢ N
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8.2.3.3 Ʉɜɚɡɢɧɶɸɬɨɧɨɜɫɤɢɟ ɦɟɬɨɞɵ ɋɭɳɟɫɬɜɭɟɬ ɛɨɥɶɲɨɟ ɫɟɦɟɣɫɬɜɨ ɤɜɚɡɢɧɶɸɬɨɧɨɜɫɤɢɯ ɦɟɬɨɞɨɜ, ɩɨɡɜɨɥɹɸɳɢɯ ɧɚ ɤɚɠɞɨɦ ɲɚɝɟ ɩɪɨɜɨɞɢɬɶ ɦɢɧɢɦɢɡɚɰɢɸ ɜ ɧɚɩɪɚɜɥɟɧɢɢ ɦɢɧɢɦɭɦɚ ɤɜɚɞɪɚɬɢɱɧɨɣ ɮɨɪɦɵ. ɂɞɟɹ ɷɬɢɯ ɦɟɬɨɞɨɜ ɫɨɫɬɨɢɬ ɜ ɬɨɦ, ɱɬɨ ɮɭɧɤɰɢɹ ɨɰɟɧɤɢ ɩɪɢɛɥɢɠɚɟɬɫɹ ɤɜɚɞɪɚɬɢɱɧɨɣ ɮɨɪɦɨɣ. Ɂɧɚɹ ɤɜɚɞɪɚɬɢɱɧɭɸ ɮɨɪɦɭ, ɦɨɠɧɨ ɜɵɱɢɫɥɢɬɶ ɟɟ ɦɢɧɢɦɭɦ ɢ ɩɪɨɜɨɞɢɬɶ ɨɩɬɢɦɢɡɚɰɢɸ ɲɚɝɚ ɜ ɧɚɩɪɚɜɥɟɧɢɢ ɷɬɨɝɨ ɦɢɧɢɦɭɦɚ. Ɉɞɧɢɦ ɢɡ ɧɚɢɛɨɥɟɟ ɱɚɫɬɨ ɢɫɩɨɥɶɡɭɟɦɵɯ ɦɟɬɨɞɨɜ ɢɡ ɫɟɦɟɣɫɬɜɚ ɨɞɧɨɲɚɝɨɜɵɯ ɤɜɚɡɢɧɶɸɬɨɧɨɜɫɤɢɯ ɦɟɬɨɞɨɜ ɹɜɥɹɟɬɫɹ BFGS ɦɟɬɨɞ. ɗɬɨɬ ɦɟɬɨɞ ɯɨɪɨɲɨ ɡɚɪɟɤɨɦɟɧɞɨɜɚɥ ɫɟɛɹ ɩɪɢ ɨɛɭɱɟɧɢɢ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ (ɫɦ. [29]). ɉɨɞɪɨɛɧɨ ɨɡɧɚɤɨɦɢɬɶɫɹ ɫ ɦɟɬɨɞɨɦ BFGS ɢ ɞɪɭɝɢɦɢ ɤɜɚɡɢɧɶɸɬɨɧɨɜɫɤɢɦɢ ɦɟɬɨɞɚɦɢ ɦɨɠɧɨ ɜ ɪɚɛɨɬɟ [48].
8.3 ɋɬɚɧɞɚɪɬ ɩɟɪɜɨɝɨ ɭɪɨɜɧɹ ɤɨɦɩɨɧɟɧɬɚ ɭɱɢɬɟɥɶ ȼ ɷɬɨɦ ɪɚɡɞɟɥɟ ɩɪɢɜɨɞɢɬɫɹ ɫɬɚɧɞɚɪɬ ɹɡɵɤɚ ɨɩɢɫɚɧɢɹ ɤɨɦɩɨɧɟɧɬɚ ɭɱɢɬɟɥɶ. ɉɨɫɤɨɥɶɤɭ ɱɚɫɬɶ ɚɥɝɨɪɢɬɦɨɜ ɨɛɭɱɟɧɢɹ ɠɟɫɬɤɨ ɩɪɢɜɹɡɚɧɚ ɤ ɚɪɯɢɬɟɤɬɭɪɟ ɫɟɬɢ, ɬɨ ɜ ɫɥɟɞɭɸɳɟɦ ɪɚɡɞɟɥɟ ɩɪɟɞɥɨɠɟɧ ɫɩɨɫɨɛ ɨɩɨɡɧɚɧɢɹ «ɫɜɨɢɯ» ɫɟɬɟɣ.
8.3.1 ɋɩɨɫɨɛ ɨɩɨɡɧɚɧɢɹ ɫɟɬɢ ɞɥɹ ɦɟɬɨɞɨɜ, ɩɪɢɜɹɡɚɧɧɵɯ ɤ ɚɪɯɢɬɟɤɬɭɪɟ ɫɟɬɢ Ⱦɥɹ ɨɩɨɡɧɚɧɢɹ ɬɢɩɚ ɫɟɬɢ ɪɟɤɨɦɟɧɞɭɟɬɫɹ ɢɫɩɨɥɶɡɨɜɚɬɶ ɩɟɪɜɵɣ ɩɚɪɚɦɟɬɪ ɫɟɬɢ. Ⱦɥɹ ɷɬɨɝɨ ɚɪɯɢɬɟɤɬɭɪɟ ɫɟɬɢ ɩɪɢɩɢɫɵɜɚɟɬɫɹ ɭɧɢɤɚɥɶɧɵɣ ɧɨɦɟɪ, ɬɢɩɚ Long. ɍɧɢɤɚɥɶɧɨɫɬɶ ɦɨɠɟɬ ɩɨɞɞɟɪɠɢɜɚɬɶɫɹ, ɧɚɩɪɢɦɟɪ, ɡɚ ɫɱɟɬ ɢɫɩɨɥɶɡɨɜɚɧɢɹ ɝɟɧɟɪɚɬɨɪɚ ɫɥɭɱɚɣɧɵɯ ɱɢɫɟɥ. Ʉɪɨɦɟ ɬɨɝɨ, ɩɪɢ ɨɩɢɫɚɧɢɢ ɩɚɪɚɦɟɬɪɨɜ ɫɟɬɢ ɫɥɟɞɭɟɬ ɡɚɞɚɬɶ ɨɬɞɟɥɶɧɵɣ ɬɢɩ ɩɚɪɚɦɟɬɪɨɜ ɞɥɹ ɩɟɪɜɨɝɨ ɩɚɪɚɦɟɬɪɚ ɢ ɭɤɚɡɚɬɶ ɦɢɧɢɦɚɥɶɧɭɸ ɝɪɚɧɢɰɭ ɪɚɜɧɨɣ ɦɚɤɫɢɦɚɥɶɧɨɣ ɢ ɪɚɜɧɨɣ ɧɨɦɟɪɭ ɚɪɯɢɬɟɤɬɭɪɵ ɫɟɬɢ. Ɍɚɤɠɟ ɧɟɨɛɯɨɞɢɦɨ ɭɤɚɡɚɬɶ ɜ ɦɚɫɤɟ ɩɚɪɚɦɟɬɪɨɜ, ɱɬɨ ɷɬɨɬ ɩɚɪɚɦɟɬɪ ɹɜɥɹɟɬɫɹ ɧɟɨɛɭɱɚɟɦɵɦ. ɍɱɢɬɟɥɶ, ɩɪɟɠɞɟ ɱɟɦ ɜɵɩɨɥɧɢɬɶ ɥɸɛɭɸ ɨɩɟɪɚɰɢɸ ɫ ɫɟɬɶɸ, ɱɢɬɚɟɬ ɩɚɪɚɦɟɬɪɵ ɫɟɬɢ, ɢ ɩɪɨɜɟɪɹɟɬ ɩɟɪɜɵɣ ɩɚɪɚɦɟɬɪ ɫɟɬɢ, ɢɧɬɟɪɩɪɟɬɢɪɭɟɦɵɣ ɤɚɤ ɩɟɪɟɦɟɧɧɚɹ ɬɢɩɚ Long, ɧɚ ɫɨɜɩɚɞɟɧɢɟ ɫ ɯɪɚɧɢɦɵɦ ɜ ɭɱɢɬɟɥɟ ɧɨɦɟɪɨɦ ɚɪɯɢɬɟɤɬɭɪɵ. ȼ ɫɥɭɱɚɟ ɧɟɫɨɜɩɚɞɟɧɢɹ ɧɨɦɟɪɚ ɜ ɩɚɪɚɦɟɬɪɚɯ ɫɟɬɢ ɫ ɧɨɦɟɪɨɦ ɜ ɭɱɢɬɟɥɟ, ɭɱɢɬɟɥɶ ɝɟɧɟɪɢɪɭɟɬ ɜɧɭɬɪɟɧɧɸɸ ɨɲɢɛɤɭ 601 – ɧɟɫɨɜɦɟɫɬɢɦɨɫɬɶ ɫɟɬɢ ɢ ɭɱɢɬɟɥɹ. ȿɫɥɢ ɭɱɢɬɟɥɶ ɪɚɛɨɬɚɟɬ ɫ ɫɟɬɹɦɢ ɥɸɛɨɣ ɚɪɯɢɬɟɤɬɭɪɵ, ɬɨ ɩɪɨɰɟɞɭɪɚ ɨɩɨɡɧɚɧɢɹ ɚɪɯɢɬɟɤɬɭɪɵ ɫɟɬɢ ɧɟ ɧɭɠɧɚ.
8.3.2 ɋɩɢɫɨɤ ɫɬɚɧɞɚɪɬɧɵɯ ɮɭɧɤɰɢɣ ȼ ɷɬɨɦ ɪɚɡɞɟɥɟ ɨɩɢɫɚɧɵ ɫɬɚɧɞɚɪɬɧɵɟ ɮɭɧɤɰɢɢ, ɫɩɟɰɢɮɢɱɟɫɤɢɟ ɞɥɹ ɤɨɦɩɨɧɟɧɬɚ ɭɱɢɬɟɥɶ. ɗɬɢ ɮɭɧɤɰɢɢ ɫɨɨɬɜɟɬɫɬɜɭɸɬ ɦɚɤɪɨɫɚɦ, ɢɫɩɨɥɶɡɨɜɚɧɧɵɦ ɜ ɩɟɪɜɨɣ ɱɚɫɬɢ ɝɥɚɜɵ. Ɂɚɝɨɥɨɜɤɢ ɮɭɧɤɰɢɣ ɞɚɧɵ ɧɚ ɹɡɵɤɟ ɨɩɢɫɚɧɢɹ ɭɱɢɬɟɥɹ.
8.3.2.1 ɍɫɬɚɧɨɜɢɬɶ ɨɛɴɟɤɬ ɨɛɭɱɟɧɢɹ (SetInstructionObject) Ɂɚɝɨɥɨɜɨɤ ɮɭɧɤɰɢɢ: Function SetInstructionObject (What : Integer; Net : PString) : Logic; Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ What ɦɨɠɟɬ ɩɪɢɧɢɦɚɬɶ ɫɥɟɞɭɸɳɢɟ ɡɧɚɱɟɧɢɹ (ɩɪɟɞɨɩɪɟɞɟɥɟɧɧɵɟ ɤɨɧɫɬɚɧɬɵ, ɩɪɢɜɟɞɟɧɧɵɟ ɜ ɬɚɛɥ. 11 ɝɥɚɜɵ «Ɉɛɳɢɣ ɫɬɚɧɞɚɪɬ»): Parameters – ɞɥɹ ɨɛɭɱɟɧɢɹ ɩɚɪɚɦɟɬɪɨɜ ɫɟɬɢ; InSignals – ɞɥɹ ɨɛɭɱɟɧɢɹ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ. Net – ɢɦɹ ɧɟɣɪɨɧɧɨɣ ɫɟɬɢ, ɤɨɬɨɪɚɹ ɛɭɞɟɬ ɨɛɭɱɚɬɶɫɹ. ȼɨɡɦɨɠɧɨ ɨɛɭɱɟɧɢɟ ɨɞɧɨɝɨ ɢɡ ɞɜɭɯ ɨɛɴɟɤɬɨɜ – ɩɚɪɚɦɟɬɪɨɜ ɫɟɬɢ ɢɥɢ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ. Ɉɛɴɟɤɬ ɨɛɭɱɟɧɢɹ ɞɨɥɠɟɧ ɛɵɬɶ ɡɚɞɚɧ ɞɨ ɧɚɱɚɥɚ ɫɨɛɫɬɜɟɧɧɨ ɨɛɭɱɟɧɢɹ. ɉɨ ɭɦɨɥɱɚɧɢɸ ɨɛɭɱɚɟɬɫɹ ɩɟɪɜɚɹ ɫɟɬɶ ɜ ɫɩɢɫɤɟ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ɤɨɦɩɨɧɟɧɬɚ ɫɟɬɶ. ɉɪɢ ɧɟɨɛɯɨɞɢɦɨɫɬɢ ɜ ɤɚɱɟɫɬɜɟ ɨɛɴɟɤɬɚ ɨɛɭɱɟɧɢɹ ɦɨɠɟɬ ɛɵɬɶ ɡɚɞɚɧɚ ɱɚɫɬɶ ɫɟɬɢ (ɫɦ. ɪɚɡɞɟɥ «Ɉɩɢɫɚɧɢɟ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ»). ɉɪɢ ɫɨɯɪɚɧɟɧɢɢ ɭɱɢɬɟɥɹ ɜ ɮɚɣɥɟ ɫɟɬɢ ɨɛɴɟɤɬ ɨɛɭɱɟɧɢɹ ɯɪɚɧɢɬɫɹ ɜɦɟɫɬɟ ɫ ɭɱɢɬɟɥɟɦ. Ɏɭɧɤɰɢɹ ɜɨɡɜɪɚɳɚɟɬ ɡɧɚɱɟɧɢɟ ɢɫɬɢɧɚ, ɟɫɥɢ ɟɟ ɜɵɩɨɥɧɟɧɢɟ ɡɚɜɟɪɲɟɧɨ ɭɫɩɟɲɧɨ. ȼ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ (ɧɚɩɪɢɦɟɪ, ɭɤɚɡɚɧɧɚɹ ɫɟɬɶ ɨɬɫɭɬɫɬɜɭɟɬ ɜ ɫɩɢɫɤɟ ɫɟɬɟɣ ɤɨɦɩɨɧɟɧɬɚ ɫɟɬɶ) ɜɨɡɜɪɚɳɚɟɬɫɹ ɡɧɚɱɟɧɢɟ ɥɨɠɶ.
8.3.2.2 ɋɨɡɞɚɧɢɟ ɦɚɫɫɢɜɚ (CreateArray) Ɂɚɝɨɥɨɜɨɤ ɮɭɧɤɰɢɢ: Function CreateArray : PRealArray; Ⱥɪɝɭɦɟɧɬɨɜ ɧɟɬ. Ɏɭɧɤɰɢɹ ɜɨɡɜɪɚɳɚɟɬ ɭɤɚɡɚɬɟɥɶ ɧɚ ɦɚɫɫɢɜ, ɩɪɢɝɨɞɧɵɣ ɞɥɹ ɯɪɚɧɟɧɢɹ ɦɚɫɫɢɜɚ ɨɛɭɱɚɟɦɵɯ ɩɚɪɚɦɟɬɪɨɜ (ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ) ɫɟɬɢ. ȿɫɥɢ ɦɚɫɫɢɜ ɫɨɡɞɚɬɶ ɧɟ ɭɞɚɥɨɫɶ, ɬɨ ɜɨɡɜɪɚɳɚɟɬɫɹ ɩɭɫɬɨɣ ɭɤɚɡɚɬɟɥɶ.
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8.3.2.3 Ɉɫɜɨɛɨɞɢɬɶ ɦɚɫɫɢɜ (EraseArray) Ɂɚɝɨɥɨɜɨɤ ɮɭɧɤɰɢɢ: Function EraseArray( Vec : PRealArray) : Logic; Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ Vec – ɭɤɚɡɚɬɟɥɶ ɧɚ ɦɚɫɫɢɜ. ɉɪɢ ɜɵɡɨɜɟ ɫɨɞɟɪɠɢɬ ɚɞɪɟɫ ɨɫɜɨɛɨɠɞɚɟɦɨɝɨ ɦɚɫɫɢɜɚ. ɉɨɫɥɟ ɜɵɩɨɥɧɟɧɢɹ ɮɭɧɤɰɢɢ ɜ ɚɪɝɭɦɟɧɬɟ Vec ɫɨɞɟɪɠɢɬɫɹ ɩɭɫɬɨɣ ɭɤɚɡɚɬɟɥɶ. ȼ ɫɥɭɱɚɟ ɧɟɜɨɡɦɨɠɧɨɫɬɢ ɨɫɜɨɛɨɠɞɟɧɢɹ ɩɚɦɹɬɢ ɮɭɧɤɰɢɹ ɝɟɧɟɪɢɪɭɟɬ ɜɧɭɬɪɟɧɧɸɸ ɨɲɢɛɤɭ 604 – ɧɟɤɨɪɪɟɤɬɧɚɹ ɪɚɛɨɬɚ ɫ ɩɚɦɹɬɶɸ, ɩɟɪɟɞɚɟɬ ɭɩɪɚɜɥɟɧɢɟ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɜɵɩɨɥɧɟɧɢɟ ɮɭɧɤɰɢɢ ɡɚɜɟɪɲɚɟɬɫɹ, ɜɨɡɜɪɚɳɚɟɬɫɹ ɡɧɚɱɟɧɢɟ ɥɨɠɶ. ȼ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ ɜɨɡɜɪɚɳɚɟɬɫɹ ɡɧɚɱɟɧɢɟ ɢɫɬɢɧɚ.
8.3.2.4 ɋɥɭɱɚɣɧɵɣ ɦɚɫɫɢɜ (RandomArray) Ɂɚɝɨɥɨɜɨɤ ɮɭɧɤɰɢɢ: Function RandomArray(Vec : PRealArray) : Logic; Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ Vec – ɭɤɚɡɚɬɟɥɶ ɧɚ ɦɚɫɫɢɜ. ɉɪɢ ɜɯɨɞɟ ɜ ɦɚɤɪɨɫ ɫɨɞɟɪɠɢɬ ɚɞɪɟɫ ɫɭɳɟɫɬɜɭɸɳɟɝɨ ɦɚɫɫɢɜɚ. ȼ ɯɨɞɟ ɜɵɩɨɥɧɟɧɢɹ ɮɭɧɤɰɢɢ ɞɥɹ ɤɚɠɞɨɝɨ ɷɥɟɦɟɧɬɚ ɦɚɫɫɢɜɚ ɩɚɪɚɦɟɬɪɨɜ ɝɟɧɟɪɢɪɭɟɬɫɹ ɫɥɭɱɚɣɧɨɟ ɡɧɚɱɟɧɢɟ. Ⱦɥɹ ɝɟɧɟɪɚɰɢɢ ɢɫɩɨɥɶɡɭɟɬɫɹ ɝɟɧɟɪɚɬɨɪ ɫɥɭɱɚɣɧɵɯ ɱɢɫɟɥ, ɪɚɜɧɨɦɟɪɧɨ ɪɚɫɩɪɟɞɟɥɟɧɧɵɯ ɧɚ ɨɬɪɟɡɤɟ ɨɬ ɧɭɥɹ ɞɨ ɟɞɢɧɢɰɵ. ɉɨɫɥɟ ɩɨɥɭɱɟɧɢɹ ɫɥɭɱɚɣɧɨɣ ɜɟɥɢɱɢɧɵ a ɨɧɚ ɩɪɟɨɛɪɚɡɭɟɬɫɹ ɩɨ ɮɨɪɦɭɥɟ
a¢ = a(amax - amin ) - amin ɤ ɫɥɭɱɚɣɧɨɣ ɜɟɥɢɱɢɧɟ, ɪɚɫɩɪɟɞɟɥɟɧɧɨɣ ɧɚ ɨɬɪɟɡɤɟ [ amin , amax ] . ȼɟɥɢ-
ɱɢɧɵ amin ɢ amax ɞɥɹ ɩɚɪɚɦɟɬɪɨɜ ɫɟɬɢ ɨɩɪɟɞɟɥɹɸɬɫɹ ɢɯ ɬɢɩɨɦ (ɫɦ. ɪɚɡɞɟɥ «Ɉɩɢɫɚɧɢɟ ɷɥɟɦɟɧɬɨɜ»). Ⱦɥɹ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɩɪɢɧɢɦɚɟɬɫɹ amin = -1 , amax = 1 . ȿɫɥɢ ɨɛɭɱɚɟɦɵɦ ɨɛɴɟɤɬɨɦ ɹɜɥɹɸɬɫɹ ɩɚɪɚɦɟɬɪɵ, ɬɨ ɝɟɧɟɪɚɰɢɹ ɫɥɭɱɚɣɧɨɝɨ ɦɚɫɫɢɜɚ ɩɪɨɢɡɜɨɞɢɬɫɹ ɩɭɬɟɦ ɝɟɧɟɪɚɰɢɢ ɡɚɩɪɨɫɚ RandomDirection ɤɨɦɩɨɧɟɧɬɚ ɫɟɬɶ. ȿɫɥɢ ɩɪɢ ɜɵɩɨɥɧɟɧɢɢ ɮɭɧɤɰɢɢ ɜɨɡɧɢɤɥɚ ɨɲɢɛɤɚ, ɬɨ ɝɟɧɟɪɢɪɭɟɬɫɹ ɜɧɭɬɪɟɧɧɹɹ ɨɲɢɛɤɚ 605 – ɨɲɢɛɤɚ ɩɪɢ ɢɫɩɨɥɧɟɧɢɢ ɜɧɟɲɧɟɝɨ ɡɚɩɪɨɫɚ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɮɭɧɤɰɢɹ ɜɨɡɜɪɚɳɚɟɬ ɡɧɚɱɟɧɢɟ ɥɨɠɶ. ȼ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ ɜɨɡɜɪɚɳɚɟɬɫɹ ɡɧɚɱɟɧɢɟ ɢɫɬɢɧɚ.
8.3.2.5 Ɇɨɞɢɮɢɤɚɰɢɹ ɦɚɫɫɢɜɚ (Modify) Ɂɚɝɨɥɨɜɨɤ ɮɭɧɤɰɢɢ: Function Modify(Direct : PRealArray; OldStep, NewStep : Real) : Logic; Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ Direct – ɭɤɚɡɚɬɟɥɶ ɧɚ ɦɚɫɫɢɜ ɧɚɩɪɚɜɥɟɧɢɹ ɦɨɞɢɮɢɤɚɰɢɢ ɫɟɬɢ. OldStep – ɜɟɫ ɫɬɚɪɨɝɨ ɦɚɫɫɢɜɚ ɩɚɪɚɦɟɬɪɨɜ ɜ ɦɨɞɢɮɢɰɢɪɨɜɚɧɧɨɦ. NewStep – ɜɟɫ ɦɚɫɫɢɜɚ ɧɚɩɪɚɜɥɟɧɢɹ ɦɨɞɢɮɢɤɚɰɢɢ ɜ ɦɨɞɢɮɢɰɢɪɨɜɚɧɧɨɦ ɦɚɫɫɢɜɟ ɩɚɪɚɦɟɬɪɨɜ. ɗɬɚ ɮɭɧɤɰɢɹ ɝɟɧɟɪɢɪɭɟɬ ɡɚɩɪɨɫ ɧɚ ɦɨɞɢɮɢɤɚɰɢɸ ɩɚɪɚɦɟɬɪɨɜ ɫɟɬɢ (ɫɦ. ɪɚɡɞɟɥ «ɉɪɨɜɟɫɬɢ ɨɛɭɱɟɧɢɟ (Modify)» ɝɥɚɜɵ «Ɉɩɢɫɚɧɢɟ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ»). ȼɵɡɨɜ ɡɚɩɪɨɫɚ ɢɦɟɟɬ ɜɢɞ: Modify( Net, OldStep, NewStep, Tipe, Direct ) Ⱥɪɝɭɦɟɧɬɚɦɢ ɡɚɩɪɨɫɚ ɹɜɥɹɸɬɫɹ: Net – ɭɤɚɡɚɬɟɥɶ ɧɚ ɩɭɫɬɭɸ ɫɬɪɨɤɭ (ɢɫɩɨɥɶɡɭɟɬɫɹ ɫɟɬɶ ɩɨ ɭɦɨɥɱɚɧɢɸ). OldStep, NewStep – ɚɪɝɭɦɟɧɬɵ ɮɭɧɤɰɢɢ. Tipe – ɡɧɚɱɟɧɢɟ ɚɪɝɭɦɟɧɬɚ What ɜ ɡɚɩɪɨɫɟ InstructionObject. Direct – ɚɪɝɭɦɟɧɬ ɮɭɧɤɰɢɢ. Ⱥɪɝɭɦɟɧɬ ɮɭɧɤɰɢɢ Direct ɦɨɠɟɬ ɛɵɬɶ ɩɭɫɬɵɦ ɭɤɚɡɚɬɟɥɟɦ. ȼ ɷɬɨɦ ɫɥɭɱɚɟ ɞɥɹ ɦɨɞɢɮɢɤɚɰɢɢ ɢɫɩɨɥɶɡɭɟɬɫɹ ɦɚɫɫɢɜ ɝɪɚɞɢɟɧɬɚ, ɯɪɚɧɹɳɢɣɫɹ ɜɦɟɫɬɟ ɫ ɫɟɬɶɸ. ȼ ɫɥɭɱɚɟ ɜɨɡɧɢɤɧɨɜɟɧɢɹ ɨɲɢɛɤɢ ɜ ɯɨɞɟ ɦɨɞɢɮɢɤɚɰɢɢ ɫɟɬɢ (ɡɚɩɪɨɫ Modify ɜɨɡɜɪɚɳɚɟɬ ɡɧɚɱɟɧɢɟ ɥɨɠɶ) ɝɟɧɟɪɢɪɭɟɬɫɹ ɜɧɭɬɪɟɧɧɹɹ ɨɲɢɛɤɚ 605 – ɨɲɢɛɤɚ ɩɪɢ ɢɫɩɨɥɧɟɧɢɢ ɜɧɟɲɧɟɝɨ ɡɚɩɪɨɫɚ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɮɭɧɤɰɢɹ ɜɨɡɜɪɚɳɚɟɬ ɡɧɚɱɟɧɢɟ ɥɨɠɶ. ȼ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ ɜɨɡɜɪɚɳɚɟɬɫɹ ɡɧɚɱɟɧɢɟ ɢɫɬɢɧɚ.
8.3.2.6 Ɉɩɬɢɦɢɡɚɰɢɹ ɲɚɝɚ (Optimize) Ɂɚɝɨɥɨɜɨɤ ɮɭɧɤɰɢɢ: Function Optimize (Direct : PRealArray; Step : Real) : Real; Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ Direct – ɭɤɚɡɚɬɟɥɶ ɧɚ ɦɚɫɫɢɜ ɧɚɩɪɚɜɥɟɧɢɹ ɦɨɞɢɮɢɤɚɰɢɢ ɫɟɬɢ. Step – ɧɚɱɚɥɶɧɵɣ ɲɚɝ ɜ ɧɚɩɪɚɜɥɟɧɢɢ Direct. Ⱦɟɣɫɬɜɢɹ, ɜɵɩɨɥɧɹɟɦɵɟ ɮɭɧɤɰɢɟɣ Optimize, ɨɩɢɫɚɧɵ ɜ ɪɚɡɞɟɥɟ «ɉɨɞɛɨɪ ɨɩɬɢɦɚɥɶɧɨɝɨ ɲɚɝɚ» ɷɬɨɣ ɝɥɚɜɵ. ȼ ɫɥɭɱɚɟ ɜɨɡɧɢɤɧɨɜɟɧɢɹ ɨɲɢɛɤɢ ɩɪɢ ɜɵɩɨɥɧɟɧɢɢ ɮɭɧɤɰɢɢ ɨɧɚ ɝɟɧɟɪɢɪɭɟɬ ɜɧɭɬɪɟɧɧɸɸ ɨɲɢɛɤɭ 605 – ɨɲɢɛɤɚ ɩɪɢ ɢɫɩɨɥɧɟɧɢɢ ɜɧɟɲɧɟɝɨ ɡɚɩɪɨɫɚ, ɩɟɪɟɞɚɟɬ ɭɩɪɚɜɥɟɧɢɟ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɮɭɧɤɰɢɹ ɜɨɡɜɪɚɳɚɟɬ ɡɧɚɱɟɧɢɟ 0. ȼ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ ɜɨɡɜɪɚɳɚɟɬɫɹ ɡɧɚɱɟɧɢɟ ɨɰɟɧɤɢ ɩɪɢ ɨɩɬɢɦɚɥɶɧɨɦ
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ɲɚɝɟ. ɋɥɟɞɭɟɬ ɨɬɦɟɬɢɬɶ, ɱɬɨ ɩɨɫɥɟ ɡɚɜɟɪɲɟɧɢɹ ɜɵɩɨɥɧɟɧɢɹ ɮɭɧɤɰɢɢ, ɩɚɪɚɦɟɬɪɵ ɫɟɬɢ ɫɨɨɬɜɟɬɫɬɜɭɸɬ ɪɟɡɭɥɶɬɚɬɭ ɜɵɩɨɥɧɟɧɢɹ ɮɭɧɤɰɢɢ Modify(Direct, 1, Step), ɝɞɟ Step – ɡɧɚɱɟɧɢɟ ɨɩɬɢɦɚɥɶɧɨɝɨ ɲɚɝɚ.
8.3.2.7 ɋɨɯɪɚɧɢɬɶ ɦɚɫɫɢɜ (SaveArray) Ɂɚɝɨɥɨɜɨɤ ɮɭɧɤɰɢɢ: Function SaveArray(Vec : PRealArray) : Logic; Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ Vec – ɭɤɚɡɚɬɟɥɶ ɧɚ ɦɚɫɫɢɜ. Ɏɭɧɤɰɢɹ ɝɟɧɟɪɢɪɭɟɬ ɡɚɩɪɨɫ nwGetData. ɉɨɫɥɟ ɜɵɩɨɥɧɟɧɢɹ ɮɭɧɤɰɢɢ ɜ ɦɚɫɫɢɜɟ, ɧɚ ɤɨɬɨɪɵɣ ɭɤɚɡɵɜɚɟɬ ɚɪɝɭɦɟɧɬ Vec, ɫɨɞɟɪɠɢɬɫɹ ɬɟɤɭɳɢɣ ɦɚɫɫɢɜ ɩɚɪɚɦɟɬɪɨɜ. ȼ ɫɥɭɱɚɟ ɜɨɡɧɢɤɧɨɜɟɧɢɹ ɨɲɢɛɤɢ ɜ ɯɨɞɟ ɜɵɩɨɥɧɟɧɢɹ ɮɭɧɤɰɢɢ ɝɟɧɟɪɢɪɭɟɬɫɹ ɜɧɭɬɪɟɧɧɹɹ ɨɲɢɛɤɚ 605 – ɨɲɢɛɤɚ ɩɪɢ ɢɫɩɨɥɧɟɧɢɢ ɜɧɟɲɧɟɝɨ ɡɚɩɪɨɫɚ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɮɭɧɤɰɢɹ ɜɨɡɜɪɚɳɚɟɬ ɡɧɚɱɟɧɢɟ ɥɨɠɶ. ȼ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ ɜɨɡɜɪɚɳɚɟɬɫɹ ɡɧɚɱɟɧɢɟ ɢɫɬɢɧɚ.
8.3.2.8 ɍɫɬɚɧɨɜɢɬɶ ɩɚɪɚɦɟɬɪɵ (SetArray) Ɂɚɝɨɥɨɜɨɤ ɮɭɧɤɰɢɢ: Function SetArray(Vec : PRealArray) : Logic; Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ Vec – ɭɤɚɡɚɬɟɥɶ ɧɚ ɦɚɫɫɢɜ, ɫɨɞɟɪɠɚɳɢɣ ɩɚɪɚɦɟɬɪɵ, ɤɨɬɨɪɵɟ ɧɟɨɛɯɨɞɢɦɨ ɭɫɬɚɧɨɜɢɬɶ. Ɏɭɧɤɰɢɹ ɝɟɧɟɪɢɪɭɟɬ ɡɚɩɪɨɫ nwSetData.ɉɨɫɥɟ ɜɵɩɨɥɧɟɧɢɹ ɮɭɧɤɰɢɢ ɩɚɪɚɦɟɬɪɵ ɫɟɬɢ ɫɨɜɩɚɞɚɸɬ ɫ ɩɚɪɚɦɟɬɪɚɦɢ, ɫɨɞɟɪɠɚɳɢɦɢɫɹ ɜ ɦɚɫɫɢɜɟ, ɧɚ ɤɨɬɨɪɵɣ ɭɤɚɡɵɜɚɟɬ ɚɪɝɭɦɟɧɬ Vec. ȼ ɫɥɭɱɚɟ ɜɨɡɧɢɤɧɨɜɟɧɢɹ ɨɲɢɛɤɢ ɜ ɯɨɞɟ ɜɵɩɨɥɧɟɧɢɹ ɮɭɧɤɰɢɢ ɝɟɧɟɪɢɪɭɟɬɫɹ ɜɧɭɬɪɟɧɧɹɹ ɨɲɢɛɤɚ 605 – ɨɲɢɛɤɚ ɩɪɢ ɢɫɩɨɥɧɟɧɢɢ ɜɧɟɲɧɟɝɨ ɡɚɩɪɨɫɚ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɮɭɧɤɰɢɹ ɜɨɡɜɪɚɳɚɟɬ ɡɧɚɱɟɧɢɟ ɥɨɠɶ. ȼ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ ɜɨɡɜɪɚɳɚɟɬɫɹ ɡɧɚɱɟɧɢɟ ɢɫɬɢɧɚ.
8.3.2.9 ȼɵɱɢɫɥɢɬɶ ɨɰɟɧɤɭ (Estimate) Ɂɚɝɨɥɨɜɨɤ ɮɭɧɤɰɢɢ: Function Estimate(Handle : Integer; All : Logic) : Real; Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ Handle – ɧɨɦɟɪ ɫɟɚɧɫɚ ɡɚɞɚɱɧɢɤɚ. All – ɩɪɢɡɧɚɤ ɨɛɭɱɟɧɢɹ ɩɨ ɜɫɟɦɭ ɨɛɭɱɚɸɳɟɦɭ ɦɧɨɠɟɫɬɜɭ. Ɏɭɧɤɰɢɹ ɝɟɧɟɪɢɪɭɟɬ ɡɚɩɪɨɫ ɤ ɢɫɩɨɥɧɢɬɟɥɸ ɧɚ ɜɵɱɢɫɥɟɧɢɟ ɨɰɟɧɤɢ. ȿɫɥɢ ɚɪɝɭɦɟɧɬ All ɫɨɞɟɪɠɢɬ ɡɧɚɱɟɧɢɟ ɢɫɬɢɧɚ, ɬɨ ɨɛɭɱɟɧɢɟ ɩɪɨɢɡɜɨɞɢɬɫɹ ɩɨ ɜɫɟɦɭ ɨɛɭɱɚɸɳɟɦɭ ɦɧɨɠɟɫɬɜɭ, ɜ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ – ɩɨɡɚɞɚɱɧɨ. ȼ ɫɥɭɱɚɟ ɜɨɡɧɢɤɧɨɜɟɧɢɹ ɨɲɢɛɤɢ ɩɪɢ ɜɵɩɨɥɧɟɧɢɢ ɮɭɧɤɰɢɢ ɨɧ ɝɟɧɟɪɢɪɭɟɬ ɜɧɭɬɪɟɧɧɸɸ ɨɲɢɛɤɭ 605 – ɨɲɢɛɤɚ ɩɪɢ ɢɫɩɨɥɧɟɧɢɢ ɜɧɟɲɧɟɝɨ ɡɚɩɪɨɫɚ, ɩɟɪɟɞɚɟɬ ɭɩɪɚɜɥɟɧɢɟ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɮɭɧɤɰɢɹ ɜɨɡɜɪɚɳɚɟɬ ɡɧɚɱɟɧɢɟ 0. ȼ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ ɜɨɡɜɪɚɳɚɟɬɫɹ ɡɧɚɱɟɧɢɟ ɜɵɱɢɫɥɟɧɧɨɣ ɨɰɟɧɤɢ.
8.3.2.10 ȼɵɱɢɫɥɢɬɶ ɝɪɚɞɢɟɧɬ (CalcGradient) Ɂɚɝɨɥɨɜɨɤ ɮɭɧɤɰɢɢ: Function CalcGradient(Handle : Integer; All : Logic) : Real; Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ Handle – ɧɨɦɟɪ ɫɟɚɧɫɚ ɡɚɞɚɱɧɢɤɚ. All – ɩɪɢɡɧɚɤ ɨɛɭɱɟɧɢɹ ɩɨ ɜɫɟɦɭ ɨɛɭɱɚɸɳɟɦɭ ɦɧɨɠɟɫɬɜɭ. Ɏɭɧɤɰɢɹ ɝɟɧɟɪɢɪɭɟɬ ɡɚɩɪɨɫ ɤ ɢɫɩɨɥɧɢɬɟɥɸ ɧɚ ɜɵɱɢɫɥɟɧɢɟ ɝɪɚɞɢɟɧɬɚ. ȿɫɥɢ ɚɪɝɭɦɟɧɬ All ɫɨɞɟɪɠɢɬ ɡɧɚɱɟɧɢɟ ɢɫɬɢɧɚ, ɬɨ ɨɛɭɱɟɧɢɟ ɩɪɨɢɡɜɨɞɢɬɫɹ ɩɨ ɜɫɟɦɭ ɨɛɭɱɚɸɳɟɦɭ ɦɧɨɠɟɫɬɜɭ, ɜ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ – ɩɨɡɚɞɚɱɧɨ. ȼ ɫɥɭɱɚɟ ɜɨɡɧɢɤɧɨɜɟɧɢɹ ɨɲɢɛɤɢ ɩɪɢ ɜɵɩɨɥɧɟɧɢɢ ɮɭɧɤɰɢɢ ɨɧ ɝɟɧɟɪɢɪɭɟɬ ɜɧɭɬɪɟɧɧɸɸ ɨɲɢɛɤɭ 605 – ɨɲɢɛɤɚ ɩɪɢ ɢɫɩɨɥɧɟɧɢɢ ɜɧɟɲɧɟɝɨ ɡɚɩɪɨɫɚ, ɩɟɪɟɞɚɟɬ ɭɩɪɚɜɥɟɧɢɟ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɮɭɧɤɰɢɹ ɜɨɡɜɪɚɳɚɟɬ ɡɧɚɱɟɧɢɟ 0. ȼ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ ɜɨɡɜɪɚɳɚɟɬɫɹ ɡɧɚɱɟɧɢɟ ɜɵɱɢɫɥɟɧɧɨɣ ɨɰɟɧɤɢ.
8.3.2.11 Ɂɚɩɭɫɬɢɬɶ ɡɚɩɪɨɫ (GenerateQuest) Ɂɚɝɨɥɨɜɨɤ ɮɭɧɤɰɢɢ: Function GenerateQuest(Name : PString; Arguments : PRealArray) : Logic Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ Name – ɭɤɚɡɚɬɟɥɶ ɧɚ ɫɢɦɜɨɥɶɧɭɸ ɫɬɪɨɤɭ, ɫɨɞɟɪɠɚɳɭɸ ɢɦɹ ɡɚɩɪɨɫɚ. Arguments – ɦɚɫɫɢɜ, ɫɨɞɟɪɠɚɳɢɣ ɚɞɪɟɫɚ ɚɪɝɭɦɟɧɬɨɜ ɡɚɩɪɨɫɚ. Ɏɭɧɤɰɢɹ ɝɟɧɟɪɢɪɭɟɬ ɡɚɩɪɨɫ ɤ ɦɚɤɪɨɤɨɦɩɨɧɟɧɬɭ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪ ɧɚ ɢɫɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ, ɢɦɹ ɤɨɬɨɪɨɝɨ ɭɤɚɡɚɧɨ ɜ ɚɪɝɭɦɟɧɬɟ Name, ɫ ɚɪɝɭɦɟɧɬɚɦɢ, ɚɞɪɟɫɚ ɤɨɬɨɪɵɯ ɭɤɚɡɚɧɵ ɜ ɚɪɝɭɦɟɧɬɟ Arguments. Ⱦɟɣɫɬɜɭɸɬ ɫɥɟɞɭɸɳɢɟ ɨɝɪɚɧɢɱɟɧɢɹ. ȼ ɫɬɪɨɤɟ, ɫɨɞɟɪɠɚɳɟɣ ɢɦɹ ɡɚɩɪɨɫɚ ɞɨɥɠɧɨ ɫɨɞɟɪɠɚɬɶɫɹ ɬɨɥɶɤɨ ɨɞɧɨ ɫɥɨɜɨ – ɢɦɹ ɡɚɩɪɨɫɚ. ȼɟɞɭɳɢɟ ɢ ɯɜɨɫɬɨɜɵɟ ɩɪɨɛɟɥɵ ɩɨɞɚɜɥɹɸɬɫɹ. ȼ ɦɚɫɫɢɜɟ Arguments ɞɨɥɠɧɨ
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ɫɨɞɟɪɠɚɬɶɫɹ ɪɨɜɧɨ ɫɬɨɥɶɤɨ ɷɥɟɦɟɧɬɨɜ, ɫɤɨɥɶɤɨ ɚɪɝɭɦɟɧɬɨɜ ɭ ɝɟɧɟɪɢɪɭɟɦɨɝɨ ɡɚɩɪɨɫɚ. ȼ ɦɚɫɫɢɜ Arguments ɜɫɟɝɞɚ ɫɤɥɚɞɵɜɚɸɬɫɹ ɚɞɪɟɫɚ ɚɪɝɭɦɟɧɬɨɜ, ɞɚɠɟ ɟɫɥɢ ɜ ɡɚɩɪɨɫ ɞɚɧɧɵɣ ɚɪɝɭɦɟɧɬ ɩɟɪɟɞɚɟɬɫɹ ɩɨ ɡɧɚɱɟɧɢɸ.
8.3.3 əɡɵɤ ɨɩɢɫɚɧɢɹ ɭɱɢɬɟɥɹ ȼ ɨɬɥɢɱɢɟ ɨɬ ɬɚɤɢɯ ɤɨɦɩɨɧɟɧɬɨɜ ɤɚɤ ɨɰɟɧɤɚ, ɫɟɬɶ ɢ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɨɬɜɟɬɚ, ɭɱɢɬɟɥɶ ɧɟ ɹɜɥɹɟɬɫɹ ɫɨɫɬɚɜɧɵɦ ɨɛɴɟɤɬɨɦ. Ɉɞɧɚɤɨ ɭɱɢɬɟɥɶ ɦɨɠɟɬ ɫɨɫɬɨɹɬɶ ɢɡ ɦɧɨɠɟɫɬɜɚ ɮɭɧɤɰɢɣ, ɜɵɡɵɜɚɸɳɢɯ ɞɪɭɝ ɞɪɭɝɚ. ɋɨɛɫɬɜɟɧɧɨ ɭɱɢɬɟɥɶ – ɷɬɨ ɩɪɨɰɟɞɭɪɚ, ɭɩɪɚɜɥɹɸɳɚɹ ɨɛɭɱɟɧɢɟɦ ɫɟɬɢ. Ʉɥɸɱɟɜɵɟ ɫɥɨɜɚ, ɫɩɟɰɢɮɢɱɟɫɤɢɟ ɞɥɹ ɹɡɵɤɚ ɨɩɢɫɚɧɢɹ ɭɱɢɬɟɥɹ ɩɪɢɜɟɞɟɧɵ ɜ ɬɚɛɥ. 3
Ɍɚɛɥɢɰɚ 3. Ʉɥɸɱɟɜɵɟ ɫɥɨɜɚ ɫɩɟɰɢɮɢɱɟɫɤɢɟ ɞɥɹ ɹɡɵɤɚ ɨɩɢɫɚɧɢɹ ɭɱɢɬɟɥɹ Ʉɥɸɱɟɜɨɟ ɫɥɨɜɨɄɪɚɬɤɨɟ ɨɩɢɫɚɧɢɟ 1. Main ɇɚɱɚɥɨ ɝɥɚɜɧɨɣ ɩɪɨɰɟɞɭɪɵ 2. Instructor Ɂɚɝɨɥɨɜɨɤ ɨɩɢɫɚɧɢɹ ɭɱɢɬɟɥɹ 3. InstrLib Ɂɚɝɨɥɨɜɨɤ ɨɩɢɫɚɧɢɹ ɛɢɛɥɢɨɬɟɤɢ ɮɭɧɤɰɢɣ 4. Used ɉɨɞɤɥɸɱɟɧɢɟ ɛɢɛɥɢɨɬɟɤ ɮɭɧɤɰɢɣ 5. Init ɇɚɱɚɥɨ ɛɥɨɤɚ ɢɧɢɰɢɚɰɢɢ 6. InstrStep ɇɚɱɚɥɨ ɛɥɨɤɚ ɨɞɧɨɝɨ ɲɚɝɚ ɨɛɭɱɟɧɢɹ 7. Close ɇɚɱɚɥɨ ɛɥɨɤɚ ɡɚɜɟɪɲɟɧɢɹ ɨɛɭɱɟɧɢɹ
8.3.3.1 Ȼɢɛɥɢɨɬɟɤɢ ɮɭɧɤɰɢɣ ɭɱɢɬɟɥɹ Ȼɢɛɥɢɨɬɟɤɢ ɮɭɧɤɰɢɣ ɭɱɢɬɟɥɹ ɫɨɞɟɪɠɚɬ ɨɩɢɫɚɧɢɟ ɮɭɧɤɰɢɣ, ɧɟɨɛɯɨɞɢɦɵɯ ɞɥɹ ɪɚɛɨɬɵ ɨɞɧɨɝɨ ɢɥɢ ɧɟɫɤɨɥɶɤɢɯ ɭɱɢɬɟɥɟɣ. ɂɫɩɨɥɶɡɨɜɚɧɢɟ ɛɢɛɥɢɨɬɟɤ ɩɨɡɜɨɥɹɟɬ ɢɡɛɟɠɚɬɶ ɞɭɛɥɢɪɨɜɚɧɢɹ ɮɭɧɤɰɢɣ ɜ ɪɚɡɥɢɱɧɵɯ ɭɱɢɬɟɥɹɯ. Ɉɩɢɫɚɧɢɟ ɛɢɛɥɢɨɬɟɤɢ ɮɭɧɤɰɢɣ ɚɧɚɥɨɝɢɱɧɨ ɨɩɢɫɚɧɢɸ ɭɱɢɬɟɥɹ, ɧɨ ɧɟ ɫɨɞɟɪɠɢɬ ɝɥɚɜɧɨɣ ɩɪɨɰɟɞɭɪɵ.
8.3.3.2 ȻɇɎ ɹɡɵɤɚ ɨɩɢɫɚɧɢɹ ɭɱɢɬɟɥɹ Ɉɛɨɡɧɚɱɟɧɢɹ, ɩɪɢɧɹɬɵɟ ɜ ɞɚɧɧɨɦ ɪɚɫɲɢɪɟɧɢɢ ȻɇɎ ɢ ɨɩɢɫɚɧɢɟ ɪɹɞɚ ɤɨɧɫɬɪɭɤɰɢɣ ɩɪɢɜɟɞɟɧɵ ɜ ɝɥɚɜɟ «Ɉɛɳɢɣ ɫɬɚɧɞɚɪɬ» ɜ ɪɚɡɞɟɥɟ «Ɉɩɢɫɚɧɢɟ ɹɡɵɤɚ ɨɩɢɫɚɧɢɹ ɤɨɦɩɨɧɟɧɬ». <Ɉɩɢɫɚɧɢɟ ɛɢɛɥɢɨɬɟɤɢ> ::= <Ɂɚɝɨɥɨɜɨɤ ɛɢɛɥɢɨɬɟɤɢ> <Ɉɩɢɫɚɧɢɟ ɝɥɨɛɚɥɶɧɵɯ ɩɟɪɟɦɟɧɧɵɯ> <Ɉɩɢɫɚɧɢɟ ɮɭɧɤɰɢɣ> <Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɛɢɛɥɢɨɬɟɤɢ> <Ɂɚɝɨɥɨɜɨɤ ɛɢɛɥɢɨɬɟɤɢ> ::= InstrLib <ɂɦɹ ɛɢɛɥɢɨɬɟɤɢ> [Used <ɋɩɢɫɨɤ ɢɦɟɧ ɛɢɛɥɢɨɬɟɤ>] <ɂɦɹ ɛɢɛɥɢɨɬɟɤɢ> ::= <ɂɞɟɧɬɢɮɢɤɚɬɨɪ> <ɋɩɢɫɨɤ ɢɦɟɧ ɛɢɛɥɢɨɬɟɤ> ::= <ɂɦɹ ɢɫɩɨɥɶɡɭɟɦɨɣ ɛɢɛɥɢɨɬɟɤɢ> [,<ɋɩɢɫɨɤ ɢɦɟɧ ɛɢɛɥɢɨɬɟɤ>] <ɂɦɹ ɢɫɩɨɥɶɡɭɟɦɨɣ ɛɢɛɥɢɨɬɟɤɢ> ::= <ɂɞɟɧɬɢɮɢɤɚɬɨɪ> <Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɛɢɛɥɢɨɬɟɤɢ> ::= End InstrLib <Ɉɩɢɫɚɧɢɟ ɭɱɢɬɟɥɹ> ::= <Ɂɚɝɨɥɨɜɨɤ ɭɱɢɬɟɥɹ> <Ɉɩɢɫɚɧɢɟ ɝɥɨɛɚɥɶɧɵɯ ɩɟɪɟɦɟɧɧɵɯ> <Ɉɩɢɫɚɧɢɟ ɮɭɧɤɰɢɣ> <Ƚɥɚɜɧɚɹ ɩɪɨɰɟɞɭɪɚ> <Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɭɱɢɬɟɥɹ> <Ɂɚɝɨɥɨɜɨɤ ɭɱɢɬɟɥɹ> ::= Instructor <ɂɦɹ ɛɢɛɥɢɨɬɟɤɢ> [Used <ɋɩɢɫɨɤ ɢɦɟɧ ɛɢɛɥɢɨɬɟɤ>] <Ƚɥɚɜɧɚɹ ɩɪɨɰɟɞɭɪɚ> ::= Main <Ɉɩɢɫɚɧɢɟ ɫɬɚɬɢɱɟɫɤɢɯ ɩɟɪɟɦɟɧɧɵɯ> <Ɉɩɢɫɚɧɢɟ ɩɟɪɟɦɟɧɧɵɯ> <Ȼɥɨɤ ɢɧɢɰɢɚɰɢɢ> <Ȼɥɨɤ ɲɚɝɚ ɨɛɭɱɟɧɢɹ> <Ȼɥɨɤ ɡɚɜɟɪɲɟɧɢɹ> <Ȼɥɨɤ ɢɧɢɰɢɚɰɢɢ> ::= Init <Ɍɟɥɨ ɮɭɧɤɰɢɢ> <Ȼɥɨɤ ɲɚɝɚ ɨɛɭɱɟɧɢɹ> ::= InstrStep <ȼɵɪɚɠɟɧɢɟ ɬɢɩɚ Logic> <Ɍɟɥɨ ɮɭɧɤɰɢɢ> <Ȼɥɨɤ ɡɚɜɟɪɲɟɧɢɹ> ::= Close <Ɍɟɥɨ ɮɭɧɤɰɢɢ> <Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɭɱɢɬɟɥɹ> End Instructor
8.3.3.3 Ɉɩɢɫɚɧɢɟ ɹɡɵɤɚ ɨɩɢɫɚɧɢɹ ɭɱɢɬɟɥɹ əɡɵɤ ɨɩɢɫɚɧɢɹ ɭɱɢɬɟɥɹ ɹɜɥɹɟɬɫɹ ɧɚɢɛɨɥɟɟ ɩɪɨɫɬɵɦ ɢɡ ɜɫɟɯ ɹɡɵɤɨɜ ɨɩɢɫɚɧɢɹ ɤɨɦɩɨɧɟɧɬ. Ɏɚɤɬɢɱɟɫɤɢ ɜɫɟ ɫɢɧɬɚɤɫɢɱɟɫɤɢɟ ɤɨɧɫɬɪɭɤɰɢɢ ɷɬɨɝɨ ɹɡɵɤɚ ɨɩɢɫɚɧɵ ɜ ɝɥɚɜɟ «Ɉɛɳɢɣ ɫɬɚɧɞɚɪɬ». ȼ ɬɟɥɟ ɮɭɧɤɰɢɢ, ɹɜɥɹɸɳɟɦɫɹ ɱɚɫɬɶɸ ɝɥɚɜɧɨɣ ɩɪɨɰɟɞɭɪɵ ɧɟɞɨɩɭɫɬɢɦ ɨɩɟɪɚɬɨɪ ɜɨɡɜɪɚɬɚ ɡɧɚɱɟɧɢɹ, ɩɨɫɤɨɥɶɤɭ ɝɥɚɜɧɚɹ ɩɪɨɰɟɞɭɪɚ ɧɟ ɹɜɥɹɟɬɫɹ ɮɭɧɤɰɢɟɣ. Ɍɪɢ ɪɚɡɞɟɥɚ ɝɥɚɜɧɨɣ ɮɭɧɤɰɢɢ – ɛɥɨɤ ɢɧɢɰɢɚɰɢɢ, ɛɥɨɤ ɨɞɧɨɝɨ ɲɚɝɚ ɨɛɭɱɟɧɢɹ ɢ ɛɥɨɤ ɡɚɜɟɪɲɟɧɢɹ ɹɜɥɹɸɬɫɹ ɮɪɚɝɦɟɧɬɚɦɢ ɨɞɧɨɣ ɩɪɨɰɟɞɭɪɵ. ȼɵɞɟɥɟɧɢɟ ɷɬɢɯ ɪɚɡɞɟɥɨɜ ɧɟɨɛɯɨɞɢɦɨ ɞɥɹ ɜɵɩɨɥɧɟɧɢɹ ɡɚɩɪɨɫɚ «ȼɵɩɨɥɧɢɬɶ N ɲɚɝɨɜ ɨɛɭɱɟɧɢɹ». ȼɵɩɨɥɧɟɧɢɟ ɝɥɚɜɧɨɣ ɩɪɨɰɟɞɭɪɵ ɩɪɨɢɫɯɨɞɢɬ ɫɥɟɞɭɸɳɢɦ ɨɛɪɚɡɨɦ. ȼɵɩɨɥɧɹɟɬɫɹ ɛɥɨɤ ɢɧɢɰɢɚɰɢɢ. ȼɵɩɨɥɧɟɧɢɟ ɛɥɨɤɚ ɨɞɧɨɝɨ ɲɚɝɚ ɨɛɭɱɟɧɢɹ ɫɟɬɢ ɩɪɨɢɡɜɨɞɢɬɫɹ ɞɨ ɬɟɯ ɩɨɪ, ɩɨɤɚ ɧɟ ɧɚɫɬɭɩɢɬ ɨɞɧɨ ɢɡ ɫɥɟɞɭɸɳɢɯ ɫɨɛɵɬɢɣ: 1. ɩɪɨɝɪɚɦɦɚ ɜɵɣɞɟɬ ɢɡ ɛɥɨɤɚ ɨɞɧɨɝɨ ɲɚɝɚ ɨɛɭɱɟɧɢɹ ɫɟɬɢ ɩɪɹɦɵɦ ɩɟɪɟɯɨɞɨɦ ɧɚ ɦɟɬɤɭ ɜ ɞɪɭɝɨɦ ɪɚɡɞɟɥɟ; 2. ɧɚɪɭɲɢɬɫɹ ɭɫɥɨɜɢɟ, ɭɤɚɡɚɧɧɨɟ ɜ ɤɨɧɫɬɪɭɤɰɢɢ InstStep; 3. ɤɨɦɩɨɧɟɧɬ ɭɱɢɬɟɥɶ ɩɨɥɭɱɢɬ ɡɚɩɪɨɫ «ɉɪɟɪɜɚɬɶ ɨɛɭɱɟɧɢɟ ɫɟɬɢ»; 4. ɜ ɫɥɭɱɚɟ ɜɵɩɨɥɧɟɧɢɹ ɡɚɩɪɨɫɚ «ȼɵɩɨɥɧɢɬɶ N ɲɚɝɨɜ ɨɛɭɱɟɧɢɹ» ɛɥɨɤ ɨɞɧɨɝɨ ɲɚɝɚ ɨɛɭɱɟɧɢɹ ɫɟɬɢ ɜɵɩɨɥɧɟɧ N ɪɚɡ. Ⱦɚɥɟɟ ɜɵɩɨɥɧɹɟɬɫɹ ɛɥɨɤ ɡɚɜɟɪɲɟɧɢɹ ɨɛɭɱɟɧɢɹ.
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8.3.3.4 ɉɪɢɦɟɪ ɨɩɢɫɚɧɢɹ ɭɱɢɬɟɥɹ ȼ ɞɚɧɧɨɦ ɪɚɡɞɟɥɟ ɩɪɢɜɟɞɟɧɵ ɨɩɢɫɚɧɢɹ ɧɟɤɨɬɨɪɵɯ ɦɟɬɨɞɨɜ ɨɛɭɱɟɧɢɹ, ɨɩɢɫɚɧɧɵɯ ɜ ɪɚɡɞɟɥɟ «Ɉɩɢɫɚɧɢɟ ɚɥɝɨɪɢɬɦɨɜ ɨɛɭɱɟɧɢɹ». ɉɪɢɦɟɪ 1. Instructor RandomFire;{Ɇɟɬɨɞ ɫɥɭɱɚɣɧɨɣ ɫɬɪɟɥɶɛɵ ɫ ɭɦɟɧɶɲɟɧɢɟɦ ɪɚɞɢɭɫɚ} Main {Ɉɛɭɱɟɧɢɟ ɜɟɞɟɬɫɹ ɩɨ ɜɫɟɦɭ ɨɛɭɱɚɸɳɟɦɭ ɦɧɨɠɟɫɬɜɭ} Label Exit, Exit1; Static Integer Try Name "ɑɢɫɥɨ ɩɨɩɵɬɨɤ ɩɪɢ ɨɞɧɨɦ ɪɚɞɢɭɫɟ" Default 5; Real MinRadius Name "Ɇɢɧɢɦɚɥɶɧɵɣ ɪɚɞɢɭɫ, ɩɪɢ ɤɨɬɨɪɨɦ ɩɪɨɞɨɥɠɚɟɬɫɹ ɪɚɛɨɬɚ" Default 0.001; String NetName Name "ɂɦɹ ɫɟɬɢ" Default ""; Integer What Name "ɑɬɨ ɨɛɭɱɚɬɶ" Default Parameters; Color InstColor Name "ɐɜɟɬ ɩɪɢɦɟɪɨɜ ɨɛɭɱɚɸɳɟɝɨ ɦɧɨɠɟɫɬɜɚ" Default HFFFF; {ɉɨ ɭɦɨɥɱɚɧɢɸ} Integer OperColor Name "Ɉɩɟɪɚɰɢɹ ɞɥɹ ɨɬɛɨɪɚ ɰɜɟɬɨɜ" Default CIn; {ɜɫɟ ɩɪɢɦɟɪɵ, ɜ ɰɜɟɬɟ ɤɨ-} Var {ɬɨɪɵɯ ɟɫɬɶ ɯɨɬɶ ɨɞɢɧ ɟɞɢɧɢɱɧɵɣ ɛɢɬ} PRealArray Map, DirectMap; {Ⱦɥɹ ɯɪɚɧɟɧɢɹ ɬɟɤɭɳɟɝɨ ɢ ɫɥɭɱɚɣɧɨɝɨ ɦɚɫɫɢɜɨɜ ɩɚɪɚɦɟɬɪɨɜ} Real Est1, Est2; {Ⱦɥɹ ɯɪɚɧɟɧɢɹ ɬɟɤɭɳɟɣ ɢ ɫɥɭɱɚɣɧɨɣ ɨɰɟɧɤɢ} Real Radius; {Ɍɟɤɭɳɢɣ ɪɚɞɢɭɫ} Integer TryNum, RadiusNum; {ɑɢɫɥɨ ɩɨɩɵɬɨɤ, ɧɨɦɟɪ ɢɫɩɨɥɶɡɨɜɚɧɧɨɝɨ ɪɚɞɢɭɫɚ} Integer Handle; {ɇɨɦɟɪ ɫɟɚɧɫɚ ɡɚɞɚɱɧɢɤɚ} String QName; {ɂɦɹ ɡɚɩɪɨɫɚ} Init Begin If Not SetInstructionObject (What, @NetName) Then GoTo Exit; {Ɂɚɞɚɟɦ ɨɛɴɟɤɬɵ ɨɛɭɱɟɧɢɹ} QName = "InitSession"; {Ɂɚɞɚɟɦ ɢɦɹ ɡɚɩɪɨɫɚ} Map = NewArray(mRealArray, 3); {ɋɨɡɞɚɟɦ ɦɚɫɫɢɜ ɞɥɹ ɚɪɝɭɦɟɧɬɨɜ ɡɚɩɪɨɫɚ} If Map = Null Then GoTo Exit; TPointer(Map^[1]) = @InstColor; {Ɂɚɧɨɫɢɦ ɚɞɪɟɫ ɩɟɪɜɨɝɨ ɚɪɝɭɦɟɧɬɚ} TPointer(Map^[2]) = @OperColor; {Ɂɚɧɨɫɢɦ ɚɞɪɟɫ ɜɬɨɪɨɝɨ ɚɪɝɭɦɟɧɬɚ} TPointer(Map^[3]) = @Handle; {Ɂɚɧɨɫɢɦ ɚɞɪɟɫ ɬɪɟɬɶɟɝɨ ɚɪɝɭɦɟɧɬɚ} If Not GenerateQuest(@QName, Map) Then GoTo Exit;{Ɉɬɤɪɵɜɚɟɦ ɫɟɚɧɫ ɪɚɛɨɬɵ ɫ ɡɚɞɚɱɧɢɤɨɦ} If Not FreeArray(mRealArray, Map) Then GoTo Exit; {Ɉɫɜɨɛɨɠɞɚɟɦ ɦɚɫɫɢɜ ɞɥɹ ɚɪɝɭɦɟɧɬɨɜ} {ɋɨɛɫɬɜɟɧɧɨ ɧɚɱɚɥɨ ɨɛɭɱɟɧɢɹ} Map = CreateArray; {ɋɨɡɞɚɟɦ ɜɫɩɨɦɨɝɚɬɟɥɶɧɵɟ ɦɚɫɫɢɜɵ} DirectMap= CreateArray; If Map = Null Then GoTo Exit; If DirectMap= Null Then GoTo Exit; Est1 = Estimate(Handle, True); If Error <> 0 Then GoTo Exit; RadiusNum = 1; {Ɉɛɪɚɛɚɬɵɜɚɟɦ ɩɟɪɜɵɣ ɪɚɞɢɭɫ} Radius = 1 / RadiusNum; {ȼɵɱɢɫɥɹɟɦ ɩɟɪɜɵɣ ɪɚɞɢɭɫ} If Not SaveArray(Map) Then GoTo Exit; {ɋɨɯɪɚɧɹɟɦ ɧɚɱɚɥɶɧɵɣ ɦɚɫɫɢɜ ɩɚɪɚɦɟɬɪɨɜ} End InstrStep Radius > MinRadius {Ɉɛɪɚɛɨɬɤɚ ɫ ɨɞɧɢɦ ɪɚɞɢɭɫɨɦ – ɨɞɢɧ ɲɚɝ ɨɛɭɱɟɧɢɹ} Begin TryNum = 0; While TryNum < Try Do Begin If Not SetArray(Map) Then GoTo Exit; {ɍɫɬɚɧɚɜɥɢɜɚɟɦ ɥɭɱɲɢɣ ɦɚɫɫɢɜ ɩɚɪɚɦɟɬɪɨɜ} If Not RandomArray(DirectMap) Then GoTo Exit; {Ƚɟɧɟɪɢɪɭɟɬɫɹ ɧɨɜɵɣ ɦɚɫɫɢɜ ɩɚɪɚɦɟɬɪɨɜ} If Not Modify(DirectMap, 1, Radius) Then GoTo Exit; {Ɇɨɞɢɮɢɰɢɪɭɟɦ ɦɚɫɫɢɜ ɩɚɪɚɦɟɬɪɨɜ} Est2 = Estimate(Handle, True); If Error <> 0 Then GoTo Exit; If Est1>Est2 Then Begin If Not SaveArray(Map) Then GoTo Exit; {ɋɨɯɪɚɧɹɟɦ ɥɭɱɲɢɣ ɦɚɫɫɢɜ ɩɚɪɚɦɟɬɪɨɜ} Est1 = Est2; TryNum = 0; End Else TryNum = TryNum + 1; {ɍɜɟɥɢɱɢɜɚɟɦ ɫɱɟɬɱɢɤ ɨɬɤɚɡɨɜ} End
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RadiusNum = RadiusNum + 1; {Ɉɛɪɚɛɚɬɵɜɚɟɦ ɫɥɟɞɭɸɳɢɣ ɪɚɞɢɭɫ} Radius = 1 / RadiusNum; {ȼɵɱɢɫɥɹɟɦ ɫɥɟɞɭɸɳɢɣ ɪɚɞɢɭɫ} End Close Begin Exit: If Not SetArray(Map) Then; {ȼɨɫɫɬɚɧɚɜɥɢɜɚɟɦ ɥɭɱɲɢɣ ɦɚɫɫɢɜ ɩɚɪɚɦɟɬɪɨɜ} If Not EraseArray(Ɇɚɪ1) Then; {Ɉɫɜɨɛɨɠɞɚɟɦ ɜɫɩɨɦɨɝɚɬɟɥɶɧɵɟ ɦɚɫɫɢɜɵ} If Not EraseArray(Ɇɚɪ2) Then; QName = "CloseSession"; {Ɂɚɞɚɟɦ ɢɦɹ ɡɚɩɪɨɫɚ} Map = NewArray(mRealArray, 1); {ɋɨɡɞɚɟɦ ɦɚɫɫɢɜ ɞɥɹ ɚɪɝɭɦɟɧɬɨɜ ɡɚɩɪɨɫɚ} If Map = Null Then GoTo Exit1; TPointer(Map^[1]) = @Handle; {Ɂɚɧɨɫɢɦ ɚɞɪɟɫ ɟɞɢɧɫɬɜɟɧɧɨɝɨ ɚɪɝɭɦɟɧɬɚ} If Not GenerateQuest(@QName, Map) Then;{Ɉɬɤɪɵɜɚɟɦ ɫɟɚɧɫ ɪɚɛɨɬɵ ɫ ɡɚɞɚɱɧɢɤɨɦ} If Not FreeArray(mRealArray, Map) Then; {Ɉɫɜɨɛɨɠɞɚɟɦ ɦɚɫɫɢɜ ɞɥɹ ɚɪɝɭɦɟɧɬɨɜ} Exit1: End End Instructor ɉɪɢɦɟɪ 2. Ȼɢɛɥɢɨɬɟɤɚ ɮɭɧɤɰɢɣ InstrLib Library1; {Ȼɢɛɥɢɨɬɟɤɚ ɫɨɞɟɪɠɢɬ ɮɭɧɤɰɢɢ ɞɥɹ ɫɥɟɞɭɸɳɟɝɨ ɭɱɢɬɟɥɹ} Function SDM( Handle : Integer; Step : Real) : Real; {Ɇɟɬɨɞ ɧɚɢɫɤɨɪɟɣɲɟɝɨ ɫɩɭɫɤɚ} Label Exit, Endd; Var Real Est; Begin Est = CalcGradient(Handle, True); If Error <> 0 Then GoTo Exit; Est = Optimize(Null, Step); {ȼɵɡɵɜɚɟɦ ɮɭɧɤɰɢɸ ɩɨɞɛɨɪɚ ɨɩɬɢɦɚɥɶɧɨɝɨ ɲɚɝɚ} If Error <> 0 Then GoTo Exit; SDM = Est; GoTo Endd; Exit: SDM = 0; Endd: End Function RDM( Handle : Integer; Step : Real) : Real; {Ɇɟɬɨɞ ɫɥɭɱɚɣɧɨɝɨ ɩɨɢɫɤɚ} Label Exit, Endd; Var Real Est; PRealArray : Direction; Begin Direction = CreateArray; {ɋɨɡɞɚɟɦ ɜɫɩɨɦɨɝɚɬɟɥɶɧɵɣ ɦɚɫɫɢɜ} If Direction = Null Then GoTo Exit; If Not RandomArray(Direction) Then GoTo Exit; {Ƚɟɧɟɪɢɪɭɟɬɫɹ ɧɨɜɵɣ ɦɚɫɫɢɜ ɩɚɪɚɦɟɬɪɨɜ} If Error <> 0 Then GoTo Exit; Est = Optimize(Direction, Step); {ȼɵɡɵɜɚɟɦ ɮɭɧɤɰɢɸ ɩɨɞɛɨɪɚ ɨɩɬɢɦɚɥɶɧɨɝɨ ɲɚɝɚ} If Error <> 0 Then GoTo Exit; RDM = Est; GoTo Endd; Exit: RDM = 0; Endd: End End InstrLib ɉɪɢɦɟɪ 3. Ⱥɧɬɢɨɜɪɚɠɧɚɹ ɩɪɨɰɟɞɭɪɚ ɨɛɭɱɟɧɢɹ. Instructor kParTan Used Library1; { Ⱥɧɬɢɨɜɪɚɠɧɚɹ ɩɪɨɰɟɞɭɪɚ ɨɛɭɱɟɧɢɹ kParTan } Main {Ɉɛɭɱɟɧɢɟ ɜɟɞɟɬɫɹ ɩɨ ɜɫɟɦɭ ɨɛɭɱɚɸɳɟɦɭ ɦɧɨɠɟɫɬɜɭ} Label Exit, Exit1;
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Static Color InstColor Name "ɐɜɟɬ ɩɪɢɦɟɪɨɜ ɨɛɭɱɚɸɳɟɝɨ ɦɧɨɠɟɫɬɜɚ" Default HFFFF; {ɉɨ ɭɦɨɥɱɚɧɢɸ} Integer OperColor Name "Ɉɩɟɪɚɰɢɹ ɞɥɹ ɨɬɛɨɪɚ ɰɜɟɬɨɜ" Default CIn; {ɜɫɟ ɩɪɢɦɟɪɵ, ɜ ɰɜɟɬɟ ɤɨ-} String NetName Name "ɂɦɹ ɫɟɬɢ" Default ""; {ɬɨɪɵɯ ɟɫɬɶ ɯɨɬɶ ɨɞɢɧ ɟɞɢɧɢɱɧɵɣ ɛɢɬ} Integer What Name "ɑɬɨ ɨɛɭɱɚɬɶ" Default Parameters; Integer k Name "ɑɢɫɥɨ ɲɚɝɨɜ ɦɟɠɞɭ ParTan ɲɚɝɚɦɢ" Default 2; {ɉɨ ɭɦɨɥɱɚɧɢɸ kParTan} Real Accuracy Name "Ɍɪɟɛɭɟɦɵɣ ɦɢɧɢɦɭɦ ɨɰɟɧɤɢ" Default 0.00001; Logic Direction Name "ɋɥɭɱɚɣɧɨɟ ɧɚɩɪɚɜɥɟɧɢɟ ɢɥɢ ɚɧɬɢɝɪɚɞɢɟɧɬ" Default True; {ȿɫɥɢ ɢɫɬɢɧɚ,} Var {ɬɨ ɚɧɬɢɝɪɚɞɢɟɧɬ} Integer Handle; {ɇɨɦɟɪ ɫɟɚɧɫɚ ɡɚɞɚɱɧɢɤɚ} String QName; {ɂɦɹ ɡɚɩɪɨɫɚ} PRealArray Map1, DirectMap; {Ⱦɥɹ ɬɟɤɭɳɟɝɨ ɦɚɫɫɢɜɚ ɩɚɪɚɦɟɬɪɨɜ ɢ ParTan ɧɚɩɪɚɜɥɟɧɢɹ} Real Step, ParTanStep; {Ⱦɥɢɧɵ ɲɚɝɨɜ ɞɥɹ ɨɩɬɢɦɢɡɚɰɢɢ ɲɚɝɚ} Real Est1, Est2; {Ⱦɥɹ ɯɪɚɧɟɧɢɹ ɬɟɤɭɳɟɣ ɢ ɫɥɭɱɚɣɧɨɣ ɨɰɟɧɤɢ} Long I; Init Begin If Not SetInstructionObject (What, @NetName) Then GoTo Exit; {Ɂɚɞɚɟɦ ɨɛɴɟɤɬɵ ɨɛɭɱɟɧɢɹ} QName = "InitSession"; {Ɂɚɞɚɟɦ ɢɦɹ ɡɚɩɪɨɫɚ} Map1 = NewArray(mRealArray, 3); {ɋɨɡɞɚɟɦ ɦɚɫɫɢɜ ɞɥɹ ɚɪɝɭɦɟɧɬɨɜ ɡɚɩɪɨɫɚ} If Map = Null Then GoTo Exit; TPointer(Map^[1]) = @InstColor; {Ɂɚɧɨɫɢɦ ɚɞɪɟɫ ɩɟɪɜɨɝɨ ɚɪɝɭɦɟɧɬɚ} TPointer(Map^[2]) = @OperColor; {Ɂɚɧɨɫɢɦ ɚɞɪɟɫ ɜɬɨɪɨɝɨ ɚɪɝɭɦɟɧɬɚ} TPointer(Map^[3]) = @Handle; {Ɂɚɧɨɫɢɦ ɚɞɪɟɫ ɬɪɟɬɶɟɝɨ ɚɪɝɭɦɟɧɬɚ} If Not GenerateQuMap(@QName, Map) Then GoTo Exit;{Ɉɬɤɪɵɜɚɟɦ ɫɟɚɧɫ ɪɚɛɨɬɵ ɫ ɡɚɞɚɱɧɢɤɨɦ} If Not FreeArray(mRealArray, Map) Then GoTo Exit; {Ɉɫɜɨɛɨɠɞɚɟɦ ɦɚɫɫɢɜ ɞɥɹ ɚɪɝɭɦɟɧɬɨɜ} {ɋɨɛɫɬɜɟɧɧɨ ɧɚɱɚɥɨ ɨɛɭɱɟɧɢɹ} Map = CreateArray; {ɋɨɡɞɚɟɦ ɜɫɩɨɦɨɝɚɬɟɥɶɧɵɟ ɦɚɫɫɢɜɵ} DirectMap= CreateArray; If Map = Null Then GoTo Exit; If DirectMap = Null Then GoTo Exit; Est1 = Accuracy*10; {Ɂɚɞɚɟɦ ɨɰɟɧɤɭ, ɧɟ ɭɞɨɜɥɟɬɜɨɪɹɸɳɭɸ ɬɪɟɛɨɜɚɧɢɸ ɬɨɱɧɨɫɬɢ} Step = 0.005; {Ɂɚɞɚɟɦ ɧɚɱɚɥɶɧɨɟ ɡɧɚɱɟɧɢɟ ɲɚɝɭ} End InstrStep Est > Accuracy Begin If Not SaveArray(Map1) Then GoTo Exit; {ɋɨɯɪɚɧɹɟɦ ɧɚɱɚɥɶɧɵɣ ɦɚɫɫɢɜ ɩɚɪɚɦɟɬɪɨɜ} For I = 1 To k Do Begin {ȼɵɩɨɥɧɹɟɦ k ɦɟɠɩɚɪɬɚɧɧɵɯ ɲɚɝɨɜ} If Direct Then Est = SDM(Handle, Step) Else Est = RDM(Handle, Step); If Error <> 0 Then GoTo Exit; End; If Not SaveArray(DirectMap) Then GoTo Exit; {ɋɨɯɪɚɧɹɟɦ ɤɨɧɟɱɧɵɣ ɦɚɫɫɢɜ ɩɚɪɚɦɟɬɪɨɜ} For I = 1 To TLong(Map^[0]) Do DirectMap^[I] = DirectMap^[I] - Map^[I]; {ȼɵɱɢɫɥɹɟɦ ɧɚɩɪɚɜɥɟɧɢɟ ParTan ɲɚɝɚ} ParTanStep = 1; {Ɂɚɞɚɟɦ ɧɚɱɚɥɶɧɨɟ ɡɧɚɱɟɧɢɟ ParTan ɲɚɝɭ} Est = Optimize(DirectMap, ParTanStep); {ȼɵɡɵɜɚɟɦ ɮɭɧɤɰɢɸ ɩɨɞɛɨɪɚ ɨɩɬɢɦɚɥɶɧɨɝɨ ɲɚɝɚ} If Error <> 0 Then GoTo Exit; End Close Begin Exit: If Not EraseArray(Ɇɚɪ) Then; {Ɉɫɜɨɛɨɠɞɚɟɦ ɜɫɩɨɦɨɝɚɬɟɥɶɧɵɟ ɦɚɫɫɢɜɵ} If Not EraseArray(DirectMap) Then; QName = "CloseSession"; {Ɂɚɞɚɟɦ ɢɦɹ ɡɚɩɪɨɫɚ} Map = NewArray(mRealArray, 1); {ɋɨɡɞɚɟɦ ɦɚɫɫɢɜ ɞɥɹ ɚɪɝɭɦɟɧɬɨɜ ɡɚɩɪɨɫɚ} If Map = Null Then GoTo Exit1; TPointer(Map^[1]) = @Handle; {Ɂɚɧɨɫɢɦ ɚɞɪɟɫ ɟɞɢɧɫɬɜɟɧɧɨɝɨ ɚɪɝɭɦɟɧɬɚ} If Not GenerateQuest(@QName, Map) Then;{Ɉɬɤɪɵɜɚɟɦ ɫɟɚɧɫ ɪɚɛɨɬɵ ɫ ɡɚɞɚɱɧɢɤɨɦ} If Not FreeArray(mRealArray, Map) Then; {Ɉɫɜɨɛɨɠɞɚɟɦ ɦɚɫɫɢɜ ɞɥɹ ɚɪɝɭɦɟɧɬɨɜ}
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Exit1: End End Instructor
8.4 ɋɬɚɧɞɚɪɬ ɜɬɨɪɨɝɨ ɭɪɨɜɧɹ ɤɨɦɩɨɧɟɧɬɚ ɭɱɢɬɟɥɶ Ʉɨɦɩɨɧɟɧɬ ɭɱɢɬɟɥɶ ɨɞɧɨɜɪɟɦɟɧɧɨ ɪɚɛɨɬɚɟɬ ɬɨɥɶɤɨ ɫ ɨɞɧɢɦ ɭɱɢɬɟɥɟɦ. Ɂɚɩɪɨɫɵ ɤ ɤɨɦɩɨɧɟɧɬɭ ɭɱɢɬɟɥɶ ɦɨɠɧɨ ɪɚɡɛɢɬɶ ɧɚ ɫɥɟɞɭɸɳɢɟ ɝɪɭɩɩɵ. 1. Ɉɛɭɱɟɧɢɟ ɫɟɬɢ. 2. ɑɬɟɧɢɟ/ɡɚɩɢɫɶ ɭɱɢɬɟɥɹ. 3. ɂɧɢɰɢɚɰɢɹ ɪɟɞɚɤɬɨɪɚ ɭɱɢɬɟɥɹ. 4. Ɋɚɛɨɬɚ ɫ ɩɚɪɚɦɟɬɪɚɦɢ ɭɱɢɬɟɥɹ.
8.4.1 Ɉɛɭɱɟɧɢɟ ɫɟɬɢ Ʉ ɞɚɧɧɨɣ ɝɪɭɩɩɟ ɨɬɧɨɫɹɬɫɹ ɬɪɢ ɡɚɩɪɨɫɚ – ɨɛɭɱɢɬɶ ɫɟɬɶ (InstructNet), ɩɪɨɜɟɫɬɢ N ɲɚɝɨɜ ɨɛɭɱɟɧɢɹ (NInstructSteps) ɢ ɩɪɟɪɜɚɬɶ ɨɛɭɱɟɧɢɟ (CloseInstruction).
8.4.1.1 Ɉɛɭɱɢɬɶ ɫɟɬɶ (InstructNet) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function InstructNet : Logic; C: Logic InstructNet() Ⱥɪɝɭɦɟɧɬɨɜ ɧɟɬ.
1. 2. 3. 4.
5.
ɇɚɡɧɚɱɟɧɢɟ – ɩɪɨɢɡɜɨɞɢɬ ɨɛɭɱɟɧɢɟ ɫɟɬɢ. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. ȿɫɥɢ Error <> 0, ɬɨ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. ȿɫɥɢ ɜ ɦɨɦɟɧɬ ɩɨɥɭɱɟɧɢɹ ɡɚɩɪɨɫɚ ɭɱɢɬɟɥɶ ɧɟ ɡɚɝɪɭɠɟɧ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 601 – ɧɟɜɟɪɧɨɟ ɢɦɹ ɤɨɦɩɨɧɟɧɬɚ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. ȼɵɩɨɥɧɹɟɬɫɹ ɝɥɚɜɧɚɹ ɩɪɨɰɟɞɭɪɚ ɡɚɝɪɭɠɟɧɧɨɝɨ ɭɱɢɬɟɥɹ. ȿɫɥɢ ɜɨ ɜɪɟɦɹ ɜɵɩɨɥɧɟɧɢɹ ɡɚɩɪɨɫɚ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ, ɚ ɡɧɚɱɟɧɢɟ ɩɟɪɟɦɟɧɧɨɣ Error ɪɚɜɧɨ ɧɭɥɸ, ɬɨ ɝɟɧɟɪɢɪɭɟɬɫɹ ɜɧɭɬɪɟɧɧɹɹ ɨɲɢɛɤɚ 605 – ɨɲɢɛɤɚ ɢɫɩɨɥɧɟɧɢɹ ɭɱɢɬɟɥɹ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. ȿɫɥɢ ɜɨ ɜɪɟɦɹ ɜɵɩɨɥɧɟɧɢɹ ɡɚɩɪɨɫɚ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ, ɚ ɡɧɚɱɟɧɢɟ ɩɟɪɟɦɟɧɧɨɣ Error ɧɟ ɪɚɜɧɨ ɧɭɥɸ, ɬɨ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ.
8.4.1.2 ɉɪɨɜɟɫɬɢ N ɲɚɝɨɜ ɨɛɭɱɟɧɢɹ (NInstructSteps) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function NInstructNet( N : Integer ) : Logic; C: Logic NInstructNet(Integer N) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɚ: N – ɱɢɫɥɨ ɜɵɩɨɥɧɟɧɢɣ ɛɥɨɤɚ ɨɞɧɨɝɨ ɲɚɝɚ ɨɛɭɱɟɧɢɹ ɫɟɬɢ.
1. 2. 3. 4.
5.
ɇɚɡɧɚɱɟɧɢɟ – ɩɪɨɢɡɜɨɞɢɬ ɨɛɭɱɟɧɢɟ ɫɟɬɢ. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. ȿɫɥɢ Error <> 0, ɬɨ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. ȿɫɥɢ ɜ ɦɨɦɟɧɬ ɩɨɥɭɱɟɧɢɹ ɡɚɩɪɨɫɚ ɭɱɢɬɟɥɶ ɧɟ ɡɚɝɪɭɠɟɧ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 601 – ɧɟɜɟɪɧɨɟ ɢɦɹ ɤɨɦɩɨɧɟɧɬɚ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. ȼɵɩɨɥɧɹɟɬɫɹ ɛɥɨɤ ɢɧɢɰɢɚɰɢɢ ɝɥɚɜɧɨɣ ɩɪɨɰɟɞɭɪɵ ɡɚɝɪɭɠɟɧɧɨɝɨ ɭɱɢɬɟɥɹ, N ɪɚɡ ɜɵɩɨɥɧɹɟɬɫɹ ɛɥɨɤ ɨɞɧɨɝɨ ɲɚɝɚ ɨɛɭɱɟɧɢɹ, ɜɵɩɨɥɧɹɟɬɫɹ ɛɥɨɤ ɡɚɜɟɪɲɟɧɢɹ ɨɛɭɱɟɧɢɹ. ȿɫɥɢ ɜɨ ɜɪɟɦɹ ɜɵɩɨɥɧɟɧɢɹ ɡɚɩɪɨɫɚ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ, ɚ ɡɧɚɱɟɧɢɟ ɩɟɪɟɦɟɧɧɨɣ Error ɪɚɜɧɨ ɧɭɥɸ, ɬɨ ɝɟɧɟɪɢɪɭɟɬɫɹ ɜɧɭɬɪɟɧɧɹɹ ɨɲɢɛɤɚ 605 – ɨɲɢɛɤɚ ɢɫɩɨɥɧɟɧɢɹ ɭɱɢɬɟɥɹ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. ȿɫɥɢ ɜɨ ɜɪɟɦɹ ɜɵɩɨɥɧɟɧɢɹ ɡɚɩɪɨɫɚ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ, ɚ ɡɧɚɱɟɧɢɟ ɩɟɪɟɦɟɧɧɨɣ Error ɧɟ ɪɚɜɧɨ ɧɭɥɸ, ɬɨ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ.
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8.4.1.3 ɉɪɟɪɜɚɬɶ ɨɛɭɱɟɧɢɟ (CloseInstruction) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function CloseInstruction: Logic; C: Logic CloseInstruction() Ⱥɪɝɭɦɟɧɬɨɜ ɧɟɬ.
1. 2. 3.
4. 5. 6.
7.
ɇɚɡɧɚɱɟɧɢɟ – ɩɪɟɪɵɜɚɟɬ ɨɛɭɱɟɧɢɟ ɫɟɬɢ. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. ȿɫɥɢ Error <> 0, ɬɨ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. ȿɫɥɢ ɜ ɦɨɦɟɧɬ ɩɨɥɭɱɟɧɢɹ ɡɚɩɪɨɫɚ ɭɱɢɬɟɥɶ ɧɟ ɡɚɝɪɭɠɟɧ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 601 – ɧɟɜɟɪɧɨɟ ɢɦɹ ɤɨɦɩɨɧɟɧɬɚ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. ȿɫɥɢ ɜ ɦɨɦɟɧɬ ɩɨɥɭɱɟɧɢɹ ɡɚɩɪɨɫɚ ɧɟ ɜɵɩɨɥɧɹɟɬɫɹ ɧɢ ɨɞɢɧ ɢɡ ɡɚɩɪɨɫɨɜ ɨɛɭɱɢɬɶ ɫɟɬɶ (InstructNet) ɢɥɢ ɩɪɨɜɟɫɬɢ N ɲɚɝɨɜ ɨɛɭɱɟɧɢɹ (NInstructSteps), ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 606 – ɧɟɜɟɪɧɨɟ ɢɫɩɨɥɶɡɨɜɚɧɢɟ ɡɚɩɪɨɫɚ ɧɚ ɩɪɟɪɵɜɚɧɢɟ ɨɛɭɱɟɧɢɹ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. Ɂɚɜɟɪɲɚɟɬɫɹ ɜɵɩɨɥɧɟɧɢɟ ɬɟɤɭɳɟɝɨ ɲɚɝɚ ɨɛɭɱɟɧɢɹ ɫɟɬɢ. ȼɵɩɨɥɧɹɟɬɫɹ ɛɥɨɤ ɡɚɜɟɪɲɟɧɢɹ ɨɛɭɱɟɧɢɹ ɫɟɬɢ. ȿɫɥɢ ɜɨ ɜɪɟɦɹ ɜɵɩɨɥɧɟɧɢɹ ɡɚɩɪɨɫɚ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ, ɚ ɡɧɚɱɟɧɢɟ ɩɟɪɟɦɟɧɧɨɣ Error ɪɚɜɧɨ ɧɭɥɸ, ɬɨ ɝɟɧɟɪɢɪɭɟɬɫɹ ɜɧɭɬɪɟɧɧɹɹ ɨɲɢɛɤɚ 605 – ɨɲɢɛɤɚ ɢɫɩɨɥɧɟɧɢɹ ɭɱɢɬɟɥɹ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. ȿɫɥɢ ɜɨ ɜɪɟɦɹ ɜɵɩɨɥɧɟɧɢɹ ɡɚɩɪɨɫɚ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ, ɚ ɡɧɚɱɟɧɢɟ ɩɟɪɟɦɟɧɧɨɣ Error ɧɟ ɪɚɜɧɨ ɧɭɥɸ, ɬɨ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ.
8.4.2 ɑɬɟɧɢɟ/ɡɚɩɢɫɶ ɭɱɢɬɟɥɹ ȼ ɞɚɧɧɨɦ ɪɚɡɞɟɥɟ ɨɩɢɫɚɧɵ ɡɚɩɪɨɫɵ, ɩɨɡɜɨɥɹɸɳɢɟ ɡɚɝɪɭɡɢɬɶ ɭɱɢɬɟɥɹ ɫ ɞɢɫɤɚ ɢɥɢ ɢɡ ɩɚɦɹɬɢ, ɜɵɝɪɭɡɢɬɶ ɭɱɢɬɟɥɹ ɢ ɫɨɯɪɚɧɢɬɶ ɬɟɤɭɳɟɝɨ ɭɱɢɬɟɥɹ ɧɚ ɞɢɫɤɟ ɢɥɢ ɜ ɩɚɦɹɬɢ.
8.4.2.1 ɉɪɨɱɢɬɚɬɶ ɭɱɢɬɟɥɹ (inAdd) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function inAdd( CompName : PString ) : Logic; C: Logic inAdd(PString CompName) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɚ: CompName – ɭɤɚɡɚɬɟɥɶ ɧɚ ɫɬɪɨɤɭ ɫɢɦɜɨɥɨɜ, ɫɨɞɟɪɠɚɳɭɸ ɢɦɹ ɮɚɣɥɚ ɤɨɦɩɨɧɟɧɬɚ ɢɥɢ ɚɞɪɟɫ ɨɩɢɫɚɧɢɹ ɤɨɦɩɨɧɟɧɬɚ. ɇɚɡɧɚɱɟɧɢɟ – ɱɢɬɚɟɬ ɭɱɢɬɟɥɹ ɫ ɞɢɫɤɚ ɢɥɢ ɢɡ ɩɚɦɹɬɢ. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. ȿɫɥɢ ɜ ɤɚɱɟɫɬɜɟ ɚɪɝɭɦɟɧɬɚ CompName ɞɚɧɚ ɫɬɪɨɤɚ, ɩɟɪɜɵɟ ɱɟɬɵɪɟ ɫɢɦɜɨɥɚ ɤɨɬɨɪɨɣ ɫɨɫɬɚɜɥɹɸɬ ɫɥɨɜɨ File, ɬɨ ɨɫɬɚɥɶɧɚɹ ɱɚɫɬɶ ɫɬɪɨɤɢ ɫɨɞɟɪɠɢɬ ɢɦɹ ɤɨɦɩɨɧɟɧɬɚ ɢ ɩɨɫɥɟ ɩɪɨɛɟɥɚ ɢɦɹ ɮɚɣɥɚ, ɫɨɞɟɪɠɚɳɟɝɨ ɤɨɦɩɨɧɟɧɬɭ. ȼ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ ɫɱɢɬɚɟɬɫɹ, ɱɬɨ ɚɪɝɭɦɟɧɬ CompName ɫɨɞɟɪɠɢɬ ɭɤɚɡɚɬɟɥɶ ɧɚ ɨɛɥɚɫɬɶ ɩɚɦɹɬɢ, ɫɨɞɟɪɠɚɳɭɸ ɨɩɢɫɚɧɢɟ ɤɨɦɩɨɧɟɧɬɚ ɜ ɮɨɪɦɚɬɟ ɞɥɹ ɡɚɩɢɫɢ ɧɚ ɞɢɫɤ. ȿɫɥɢ ɨɩɢɫɚɧɢɟ ɧɟ ɜɦɟɳɚɟɬɫɹ ɜ ɨɞɧɭ ɨɛɥɚɫɬɶ ɩɚɦɹɬɢ, ɬɨ ɞɨɩɭɫɤɚɟɬɫɹ ɜɤɥɸɱɟɧɢɟ ɜ ɬɟɤɫɬ ɨɩɢɫɚɧɢɹ ɤɨɦɩɨɧɟɧɬɚ ɤɥɸɱɟɜɨɝɨ ɫɥɨɜɚ Continue, ɡɚ ɤɨɬɨɪɵɦ ɫɥɟɞɭɟɬ ɱɟɬɵɪɟ ɛɚɣɬɚ, ɫɨɞɟɪɠɚɳɢɟ ɚɞɪɟɫ ɫɥɟɞɭɸɳɟɣ ɨɛɥɚɫɬɢ ɩɚɦɹɬɢ. 2. ȿɫɥɢ ɜ ɞɚɧɧɵɣ ɦɨɦɟɧɬ ɡɚɝɪɭɠɟɧ ɞɪɭɝɨɣ ɭɱɢɬɟɥɶ, ɬɨ ɜɵɩɨɥɧɹɟɬɫɹ ɡɚɩɪɨɫ inDelete. ɍɱɢɬɟɥɶ ɫɱɢɬɵɜɚɟɬɫɹ ɢɡ ɮɚɣɥɚ ɢɥɢ ɢɡ ɩɚɦɹɬɢ. 3. ȿɫɥɢ ɫɱɢɬɵɜɚɧɢɟ ɡɚɜɟɪɲɚɟɬɫɹ ɩɨ ɨɲɢɛɤɟ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 602 – ɨɲɢɛɤɚ ɫɱɢɬɵɜɚɧɢɹ ɭɱɢɬɟɥɹ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ.
1.
8.4.2.2 ɍɞɚɥɟɧɢɟ ɭɱɢɬɟɥɹ (inDelete) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function inDelete : Logic;
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C: Logic inDelete() Ⱥɪɝɭɦɟɧɬɨɜ ɧɟɬ. ɇɚɡɧɚɱɟɧɢɟ – ɭɞɚɥɹɟɬ ɡɚɝɪɭɠɟɧɧɨɝɨ ɜ ɩɚɦɹɬɶ ɭɱɢɬɟɥɹ. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. 1. ȿɫɥɢ ɫɩɢɫɨɤ ɜ ɦɨɦɟɧɬ ɩɨɥɭɱɟɧɢɹ ɡɚɩɪɨɫɚ ɭɱɢɬɟɥɶ ɧɟ ɡɚɝɪɭɠɟɧ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 601 – ɧɟɜɟɪɧɨɟ ɢɦɹ ɭɱɢɬɟɥɹ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ.
8.4.2.3 Ɂɚɩɢɫɶ ɤɨɦɩɨɧɟɧɬɚ (inWrite) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function inWrite(Var FileName : PString) : Logic; C: Logic inWrite(PString* FileName) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: CompName – ɭɤɚɡɚɬɟɥɶ ɧɚ ɫɬɪɨɤɭ ɫɢɦɜɨɥɨɜ, ɫɨɞɟɪɠɚɳɭɸ ɢɦɹ ɤɨɦɩɨɧɟɧɬɚ. FileName – ɢɦɹ ɮɚɣɥɚ ɢɥɢ ɚɞɪɟɫ ɩɚɦɹɬɢ, ɤɭɞɚ ɧɚɞɨ ɡɚɩɢɫɚɬɶ ɤɨɦɩɨɧɟɧɬ. ɇɚɡɧɚɱɟɧɢɟ – ɫɨɯɪɚɧɹɟɬ ɭɱɢɬɟɥɹ ɜ ɮɚɣɥɟ ɢɥɢ ɜ ɩɚɦɹɬɢ. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. 1. ȿɫɥɢ ɜ ɦɨɦɟɧɬ ɩɨɥɭɱɟɧɢɹ ɡɚɩɪɨɫɚ ɭɱɢɬɟɥɶ ɧɟ ɡɚɝɪɭɠɟɧ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 601 – ɧɟɜɟɪɧɨɟ ɢɦɹ ɤɨɦɩɨɧɟɧɬɚ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 2. ȿɫɥɢ ɜ ɤɚɱɟɫɬɜɟ ɚɪɝɭɦɟɧɬɚ FileName ɞɚɧɚ ɫɬɪɨɤɚ, ɩɟɪɜɵɟ ɱɟɬɵɪɟ ɫɢɦɜɨɥɚ ɤɨɬɨɪɨɣ ɫɨɫɬɚɜɥɹɸɬ ɫɥɨɜɨ File, ɬɨ ɨɫɬɚɥɶɧɚɹ ɱɚɫɬɶ ɫɬɪɨɤɢ ɫɨɞɟɪɠɢɬ ɢɦɹ ɮɚɣɥɚ ɞɥɹ ɡɚɩɢɫɢ ɤɨɦɩɨɧɟɧɬɚ. ȼ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ FileName ɞɨɥɠɟɧ ɫɨɞɟɪɠɚɬɶ ɩɭɫɬɨɣ ɭɤɚɡɚɬɟɥɶ. ȼ ɷɬɨɦ ɫɥɭɱɚɟ ɡɚɩɪɨɫ ɜɟɪɧɟɬ ɜ ɧɟɦ ɭɤɚɡɚɬɟɥɶ ɧɚ ɨɛɥɚɫɬɶ ɩɚɦɹɬɢ, ɤɭɞɚ ɛɭɞɟɬ ɩɨɦɟɳɟɧɨ ɨɩɢɫɚɧɢɟ ɤɨɦɩɨɧɟɧɬɚ ɜ ɮɨɪɦɚɬɟ ɞɥɹ ɡɚɩɢɫɢ ɧɚ ɞɢɫɤ. ȿɫɥɢ ɨɩɢɫɚɧɢɟ ɧɟ ɜɦɟɳɚɟɬɫɹ ɜ ɨɞɧɭ ɨɛɥɚɫɬɶ ɩɚɦɹɬɢ, ɬɨ ɜ ɬɟɤɫɬ ɛɭɞɟɬ ɜɤɥɸɱɟɧɨ ɤɥɸɱɟɜɨɟ ɫɥɨɜɨ Continue, ɡɚ ɤɨɬɨɪɵɦ ɫɥɟɞɭɟɬ ɱɟɬɵɪɟ ɛɚɣɬɚ, ɫɨɞɟɪɠɚɳɢɟ ɚɞɪɟɫ ɫɥɟɞɭɸɳɟɣ ɨɛɥɚɫɬɢ ɩɚɦɹɬɢ. 3. ȿɫɥɢ ɜɨ ɜɪɟɦɹ ɫɨɯɪɚɧɟɧɢɹ ɤɨɦɩɨɧɟɧɬɚ ɜɨɡɧɢɤɧɟɬ ɨɲɢɛɤɚ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 603 – ɨɲɢɛɤɚ ɫɨɯɪɚɧɟɧɢɹ ɤɨɦɩɨɧɟɧɬɚ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ.
8.4.3 ɂɧɢɰɢɚɰɢɹ ɪɟɞɚɤɬɨɪɚ ɭɱɢɬɟɥɹ Ʉ ɷɬɨɣ ɝɪɭɩɩɟ ɡɚɩɪɨɫɨɜ ɨɬɧɨɫɢɬɫɹ ɡɚɩɪɨɫ, ɤɨɬɨɪɵɣ ɢɧɢɰɢɢɪɭɟɬ ɪɚɛɨɬɭ ɧɟ ɪɚɫɫɦɚɬɪɢɜɚɟɦɨɝɨ ɜ ɞɚɧɧɨɣ ɪɚɛɨɬɟ ɤɨɦɩɨɧɟɧɬɚ – ɪɟɞɚɤɬɨɪɚ ɭɱɢɬɟɥɹ.
8.4.3.1 Ɋɟɞɚɤɬɢɪɨɜɚɬɶ ɤɨɦɩɨɧɟɧɬ (inEdit) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Procedure inEdit(CompName : PString); C: void inEdit(PString CompName) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɚ: CompName – ɭɤɚɡɚɬɟɥɶ ɧɚ ɫɬɪɨɤɭ ɫɢɦɜɨɥɨɜ – ɢɦɹ ɮɚɣɥɚ ɢɥɢ ɚɞɪɟɫ ɩɚɦɹɬɢ, ɫɨɞɟɪɠɚɳɢɟ ɨɩɢɫɚɧɢɟ ɭɱɢɬɟɥɹ. ȿɫɥɢ ɜ ɤɚɱɟɫɬɜɟ ɚɪɝɭɦɟɧɬɚ CompName ɞɚɧɚ ɫɬɪɨɤɚ, ɩɟɪɜɵɟ ɱɟɬɵɪɟ ɫɢɦɜɨɥɚ ɤɨɬɨɪɨɣ ɫɨɫɬɚɜɥɹɸɬ ɫɥɨɜɨ File, ɬɨ ɨɫɬɚɥɶɧɚɹ ɱɚɫɬɶ ɫɬɪɨɤɢ ɫɨɞɟɪɠɢɬ ɢɦɹ ɭɱɢɬɟɥɹ ɢ ɩɨɫɥɟ ɩɪɨɛɟɥɚ ɢɦɹ ɮɚɣɥɚ, ɫɨɞɟɪɠɚɳɟɝɨ ɨɩɢɫɚɧɢɟ ɭɱɢɬɟɥɹ. ȼ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ ɫɱɢɬɚɟɬɫɹ, ɱɬɨ ɚɪɝɭɦɟɧɬ CompName ɫɨɞɟɪɠɢɬ ɭɤɚɡɚɬɟɥɶ ɧɚ ɨɛɥɚɫɬɶ ɩɚɦɹɬɢ, ɫɨɞɟɪɠɚɳɭɸ ɨɩɢɫɚɧɢɟ ɭɱɢɬɟɥɹ ɜ ɮɨɪɦɚɬɟ ɞɥɹ ɡɚɩɢɫɢ ɧɚ ɞɢɫɤ. ȿɫɥɢ ɨɩɢɫɚɧɢɟ ɧɟ ɜɦɟɳɚɟɬɫɹ ɜ ɨɞɧɭ ɨɛɥɚɫɬɶ ɩɚɦɹɬɢ, ɬɨ ɞɨɩɭɫɤɚɟɬɫɹ ɜɤɥɸɱɟɧɢɟ ɜ ɬɟɤɫɬ ɨɩɢɫɚɧɢɹ ɤɥɸɱɟɜɨɝɨ ɫɥɨɜɚ Continue, ɡɚ ɤɨɬɨɪɵɦ ɫɥɟɞɭɟɬ ɱɟɬɵɪɟ ɛɚɣɬɚ, ɫɨɞɟɪɠɚɳɢɟ ɚɞɪɟɫ ɫɥɟɞɭɸɳɟɣ ɨɛɥɚɫɬɢ ɩɚɦɹɬɢ. ȿɫɥɢ ɜ ɤɚɱɟɫɬɜɟ ɚɪɝɭɦɟɧɬɚ CompName ɩɟɪɟɞɚɧ ɩɭɫɬɨɣ ɭɤɚɡɚɬɟɥɶ ɢɥɢ ɭɤɚɡɚɬɟɥɶ ɧɚ ɩɭɫɬɭɸ ɫɬɪɨɤɭ, ɬɨ ɪɟɞɚɤɬɨɪ ɫɨɡɞɚɟɬ ɧɨɜɨɝɨ ɭɱɢɬɟɥɹ.
8.4.4 Ɋɚɛɨɬɚ ɫ ɩɚɪɚɦɟɬɪɚɦɢ ɭɱɢɬɟɥɹ ȼ ɞɚɧɧɨɦ ɪɚɡɞɟɥɟ ɨɩɢɫɚɧɵ ɡɚɩɪɨɫɵ, ɩɨɡɜɨɥɹɸɳɢɟ ɢɡɦɟɧɹɬɶ ɩɚɪɚɦɟɬɪɵ ɭɱɢɬɟɥɹ.
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8.4.4.1 ɉɨɥɭɱɢɬɶ ɩɚɪɚɦɟɬɪɵ (inGetData) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function inGetData(Var Param : PRealArray ) : Logic; C: Logic inGetData(PRealArray* Param) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɚ: Param – ɚɞɪɟɫ ɦɚɫɫɢɜɚ ɩɚɪɚɦɟɬɪɨɜ. ɇɚɡɧɚɱɟɧɢɟ – ɜɨɡɜɪɚɳɚɟɬ ɜɟɤɬɨɪ ɩɚɪɚɦɟɬɪɨɜ ɭɱɢɬɟɥɹ. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. 1. ȿɫɥɢ Error <> 0, ɬɨ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 2. ȿɫɥɢ ɜ ɦɨɦɟɧɬ ɩɨɥɭɱɟɧɢɹ ɡɚɩɪɨɫɚ ɭɱɢɬɟɥɶ ɧɟ ɡɚɝɪɭɠɟɧ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 601 – ɧɟɜɟɪɧɨɟ ɢɦɹ ɤɨɦɩɨɧɟɧɬɚ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 3. ȼ ɦɚɫɫɢɜ, ɚɞɪɟɫ ɤɨɬɨɪɨɝɨ ɩɟɪɟɞɚɧ ɜ ɚɪɝɭɦɟɧɬɟ Param, ɡɚɧɨɫɹɬɫɹ ɡɧɚɱɟɧɢɹ ɩɚɪɚɦɟɬɪɨɜ. ɉɚɪɚɦɟɬɪɵ ɡɚɧɨɫɹɬɫɹ ɜ ɦɚɫɫɢɜ ɜ ɩɨɪɹɞɤɟ ɨɩɢɫɚɧɢɹ ɜ ɪɚɡɞɟɥɟ ɨɩɢɫɚɧɢɹ ɫɬɚɬɢɱɟɫɤɢɯ ɩɟɪɟɦɟɧɧɵɯ.
8.4.4.2 ɉɨɥɭɱɢɬɶ ɢɦɟɧɚ ɩɚɪɚɦɟɬɪɨɜ (inGetName) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function inGetName(Var Param : PRealArray ) : Logic; C: Logic inGetName(PRealArray* Param) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɚ: Param – ɚɞɪɟɫ ɦɚɫɫɢɜɚ ɭɤɚɡɚɬɟɥɟɣ ɧɚ ɧɚɡɜɚɧɢɹ ɩɚɪɚɦɟɬɪɨɜ. ɇɚɡɧɚɱɟɧɢɟ – ɜɨɡɜɪɚɳɚɟɬ ɜɟɤɬɨɪ ɭɤɚɡɚɬɟɥɟɣ ɧɚ ɧɚɡɜɚɧɢɹ ɩɚɪɚɦɟɬɪɨɜ ɭɱɢɬɟɥɹ. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. 1. ȿɫɥɢ Error <> 0, ɬɨ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 2. ȿɫɥɢ ɜ ɦɨɦɟɧɬ ɩɨɥɭɱɟɧɢɹ ɡɚɩɪɨɫɚ ɭɱɢɬɟɥɶ ɧɟ ɡɚɝɪɭɠɟɧ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 601 – ɧɟɜɟɪɧɨɟ ɢɦɹ ɤɨɦɩɨɧɟɧɬɚ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 3. ȼ ɦɚɫɫɢɜ, ɚɞɪɟɫ ɤɨɬɨɪɨɝɨ ɩɟɪɟɞɚɧ ɜ ɚɪɝɭɦɟɧɬɟ Param, ɡɚɧɨɫɹɬɫɹ ɚɞɪɟɫɚ ɫɢɦɜɨɥɶɧɵɯ ɫɬɪɨɤ, ɫɨɞɟɪɠɚɳɢɯ ɧɚɡɜɚɧɢɹ ɩɚɪɚɦɟɬɪɨɜ.
8.4.4.3 ɍɫɬɚɧɨɜɢɬɶ ɩɚɪɚɦɟɬɪɵ (inSetData) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function inSetData(Param : PRealArray ) : Logic; C: Logic inSetData(PRealArray Param) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: Param – ɚɞɪɟɫ ɦɚɫɫɢɜɚ ɩɚɪɚɦɟɬɪɨɜ. ɇɚɡɧɚɱɟɧɢɟ – ɡɚɦɟɧɹɟɬ ɡɧɚɱɟɧɢɹ ɩɚɪɚɦɟɬɪɨɜ ɭɱɢɬɟɥɹ ɧɚ ɡɧɚɱɟɧɢɹ, ɩɟɪɟɞɚɧɧɵɟ, ɜ ɚɪɝɭɦɟɧɬɟ Param. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. ȿɫɥɢ Error <> 0, ɬɨ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. ȿɫɥɢ ɜ ɦɨɦɟɧɬ ɩɨɥɭɱɟɧɢɹ ɡɚɩɪɨɫɚ ɭɱɢɬɟɥɶ ɧɟ ɡɚɝɪɭɠɟɧ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 601 – ɧɟɜɟɪɧɨɟ ɢɦɹ ɤɨɦɩɨɧɟɧɬɚ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 3. ɉɚɪɚɦɟɬɪɵ, ɡɧɚɱɟɧɢɹ ɤɨɬɨɪɵɯ ɯɪɚɧɹɬɫɹ ɜ ɦɚɫɫɢɜɟ, ɚɞɪɟɫ ɤɨɬɨɪɨɝɨ ɩɟɪɟɞɚɧ ɜ ɚɪɝɭɦɟɧɬɟ Param, ɩɟɪɟɞɚɸɬɫɹ ɭɱɢɬɟɥɸ.
1. 2.
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8.4.5 Ɉɛɪɚɛɨɬɤɚ ɨɲɢɛɨɤ ȼ ɬɚɛɥ. 4 ɩɪɢɜɟɞɟɧ ɩɨɥɧɵɣ ɫɩɢɫɨɤ ɨɲɢɛɨɤ, ɤɨɬɨɪɵɟ ɦɨɝɭɬ ɜɨɡɧɢɤɚɬɶ ɩɪɢ ɜɵɩɨɥɧɟɧɢɢ ɡɚɩɪɨɫɨɜ ɤɨɦɩɨɧɟɧɬɨɦ ɭɱɢɬɟɥɶ, ɢ ɞɟɣɫɬɜɢɹ ɫɬɚɧɞɚɪɬɧɨɝɨ ɨɛɪɚɛɨɬɱɢɤɚ ɨɲɢɛɨɤ. Ɍɚɛɥɢɰɚ 4. Ɉɲɢɛɤɢ ɤɨɦɩɨɧɟɧɬɚ ɭɱɢɬɟɥɶ ɢ ɞɟɣɫɬɜɢɹ ɫɬɚɧɞɚɪɬɧɨɝɨ ɨɛɪɚɛɨɬɱɢɤɚ ɨɲɢɛɨɤ. ʋɇɚ ɡɜɚɧɢɟ ɨɲɢɛɤɢɋɬɚɧɞɚɪɬɧɚɹ ɨɛɪɚɛɨɬɤɚ 601 ɇɟɫɨɜɦɟɫɬɢɦɨɫɬɶ ɫɟɬɢ ɢ ɭɱɢɬɟɥɹɁɚɧɟɫɟɧɢɟ ɧɨɦɟɪɚ ɜ Error 602 Ɉɲɢɛɤɚ ɫɱɢɬɵɜɚɧɢɹ ɭɱɢɬɟɥɹɁɚɧɟɫɟɧɢɟ ɧɨɦɟɪɚ ɜ Error 603 Ɉɲɢɛɤɚ ɫɨɯɪɚɧɟɧɢɹ ɭɱɢɬɟɥɹɁɚɧɟɫɟɧɢɟ ɧɨɦɟɪɚ ɜ Error 604 ɇɟɤɨɪɪɟɤɬɧɚɹ ɪɚɛɨɬɚ ɫ ɩɚɦɹɬɶɸɁɚɧɟɫɟɧɢɟ ɧɨɦɟɪɚ ɜ Error 605 Ɉɲɢɛɤɚ ɢɫɩɨɥɧɟɧɢɹ ɭɱɢɬɟɥɹɁɚɧɟɫɟɧɢɟ ɧɨɦɟɪɚ ɜ Error 606 ɇɟɜɟɪɧɨɟ ɢɫɩɨɥɶɡɨɜɚɧɢɟ ɡɚɩɪɨɫɚ ɧɚ ɩɪɟɪɵɜɚɧɢɟ ɨɛɭɱɟɧɢɹɁɚɧɟɫɟɧɢɟ ɧɨɦɟɪɚ ɜ Error
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Ʉɨɧɬɪɚɫɬɟɪ Ʉɨɦɩɨɧɟɧɬ ɤɨɧɬɪɚɫɬɟɪ ɩɪɟɞɧɚɡɧɚɱɟɧ ɞɥɹ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɹ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ. ɉɟɪɜɵɟ ɪɚɛɨɬɵ, ɩɨɫɜɹɳɟɧɧɵɟ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɸ (ɫɤɟɥɟɬɨɧɢɡɚɰɢɢ) ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ɩɨɹɜɢɥɢɫɶ ɜ ɧɚɱɚɥɟ ɞɟɜɹɧɨɫɬɵɯ ɝɨɞɨɜ [64. ]. Ɉɞɧɚɤɨ, ɡɚɞɚɱɚ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɹ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ɧɟ ɹɜɥɹɥɚɫɶ ɰɟɧɬɪɚɥɶɧɨɣ, ɩɨɫɤɨɥɶɤɭ ɭɩɪɨɳɟɧɢɟ ɫɟɬɟɣ ɦɨɠɟɬ ɩɪɢɧɟɫɬɢ ɪɟɚɥɶɧɭɸ ɩɨɥɶɡɭ ɬɨɥɶɤɨ ɩɪɢ ɪɟɚɥɢɡɚɰɢɢ ɨɛɭɱɟɧɧɨɣ ɧɟɣɪɨɧɧɨɣ ɫɟɬɢ ɜ ɜɢɞɟ ɷɥɟɤɬɪɨɧɧɨɝɨ (ɨɩɬɨɷɥɟɤɬɪɨɧɧɨɝɨ) ɭɫɬɪɨɣɫɬɜɚ. Ɍɨɥɶɤɨ ɜ ɪɚɛɨɬɟ Ⱥ.ɇ. Ƚɨɪɛɚɧɹ ɢ ȿ.Ɇ. Ɇɢɪɤɟɫɚ «Ʌɨɝɢɱɟɫɤɢ ɩɪɨɡɪɚɱɧɵɟ ɧɟɣɪɨɧɧɵɟ ɫɟɬɢ» [82] (ɛɨɥɟɟ ɩɨɥɧɵɣ ɜɚɪɢɚɧɬ ɪɚɛɨɬɵ ɫɦ. [75]), ɨɩɭɛɥɢɤɨɜɚɧɧɨɣ ɜ 1995 ɝɨɞɭ ɡɚɞɚɱɟ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɹ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ɛɵɥ ɩɪɢɞɚɧ ɫɚɦɨɫɬɨɹɬɟɥɶɧɵɣ ɫɦɵɫɥ – ɜɩɟɪɜɵɟ ɩɨɹɜɢɥɚɫɶ ɪɟɚɥɶɧɚɹ ɜɨɡɦɨɠɧɨɫɬɶ ɩɨɥɭɱɚɬɶ ɧɨɜɵɟ ɹɜɧɵɟ ɡɧɚɧɢɹ ɢɡ ɞɚɧɧɵɯ. ȼ ɫɜɹɡɢ ɫ ɬɟɦ, ɱɬɨ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɟ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ɧɟ ɹɜɥɹɟɬɫɹ ɞɨɫɬɚɬɨɱɧɨ ɪɚɡɜɢɬɨɣ ɜɟɬɜɶɸ ɧɟɣɪɨɢɧɮɨɪɦɚɬɢɤɢ, ɫɬɚɧɞɚɪɬ, ɩɪɢɜɟɞɟɧɧɵɣ ɜ ɞɚɧɧɨɣ ɝɥɚɜɟ, ɹɜɥɹɟɬɫɹ ɨɱɟɧɶ ɨɛɳɢɦ.
Ɂɚɞɚɱɢ ɞɥɹ ɤɨɧɬɪɚɫɬɟɪɚ ɂɡ ɚɧɚɥɢɡɚ ɥɢɬɟɪɚɬɭɪɵ ɢ ɨɩɵɬɚ ɪɚɛɨɬɵ ɝɪɭɩɩɵ ɇɟɣɪɨɄɨɦɩ ɦɨɠɧɨ ɫɮɨɪɦɭɥɢɪɨɜɚɬɶ ɫɥɟɞɭɸɳɢɟ ɡɚɞɚɱɢ, ɪɟɲɚɟɦɵɟ ɫ ɩɨɦɨɳɶɸ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɹ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ. 1. ɍɩɪɨɳɟɧɢɟ ɚɪɯɢɬɟɤɬɭɪɵ ɧɟɣɪɨɧɧɨɣ ɫɟɬɢ. 2. ɍɦɟɧɶɲɟɧɢɟ ɱɢɫɥɚ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ. 3. ɋɜɟɞɟɧɢɟ ɩɚɪɚɦɟɬɪɨɜ ɧɟɣɪɨɧɧɨɣ ɫɟɬɢ ɤ ɧɟɛɨɥɶɲɨɦɭ ɧɚɛɨɪɭ ɜɵɞɟɥɟɧɧɵɯ ɡɧɚɱɟɧɢɣ. 4. ɋɧɢɠɟɧɢɟ ɬɪɟɛɨɜɚɧɢɣ ɤ ɬɨɱɧɨɫɬɢ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ. 5. ɉɨɥɭɱɟɧɢɟ ɹɜɧɵɯ ɡɧɚɧɢɣ ɢɡ ɞɚɧɧɵɯ. Ⱦɚɥɟɟ ɜ ɷɬɨɦ ɪɚɡɞɟɥɟ ɜɫɟ ɩɟɪɟɱɢɫɥɟɧɧɵɟ ɜɵɲɟ ɡɚɞɚɱɢ ɪɚɫɫɦɨɬɪɟɧɵ ɛɨɥɟɟ ɩɨɞɪɨɛɧɨ.
ɍɩɪɨɳɟɧɢɟ ɚɪɯɢɬɟɤɬɭɪɵ ɧɟɣɪɨɧɧɨɣ ɫɟɬɢ ɋɬɪɟɦɥɟɧɢɟ ɤ ɭɩɪɨɳɟɧɢɸ ɚɪɯɢɬɟɤɬɭɪɵ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ɜɨɡɧɢɤɥɨ ɢɡ ɩɨɩɵɬɤɢ ɨɬɜɟɬɢɬɶ ɧɚ ɫɥɟɞɭɸɳɢɟ ɜɨɩɪɨɫ: «ɋɤɨɥɶɤɨ ɧɟɣɪɨɧɨɜ ɧɭɠɧɨ ɢɫɩɨɥɶɡɨɜɚɬɶ ɢ ɤɚɤ ɨɧɢ ɞɨɥɠɧɵ ɛɵɬɶ ɫɜɹɡɚɧɵ ɞɪɭɝ ɫ ɞɪɭɝɨɦ?» ɉɪɢ ɨɬɜɟɬɟ ɧɚ ɷɬɨɬ ɜɨɩɪɨɫ ɫɭɳɟɫɬɜɭɟɬ ɞɜɟ ɩɪɨɬɢɜɨɩɨɥɨɠɧɵɟ ɬɨɱɤɢ ɡɪɟɧɢɹ. Ɉɞɧɚ ɢɡ ɧɢɯ ɭɬɜɟɪɠɞɚɟɬ, ɱɬɨ ɱɟɦ ɛɨɥɶɲɟ ɧɟɣɪɨɧɨɜ ɢɫɩɨɥɶɡɨɜɚɬɶ, ɬɟɦ ɛɨɥɟɟ ɧɚɞɟɠɧɚɹ ɫɟɬɶ ɩɨɥɭɱɢɬɫɹ. ɋɬɨɪɨɧɧɢɤɢ ɷɬɨɣ ɩɨɡɢɰɢɢ ɫɫɵɥɚɸɬɫɹ ɧɚ ɩɪɢɦɟɪ ɱɟɥɨɜɟɱɟɫɤɨɝɨ ɦɨɡɝɚ. Ⱦɟɣɫɬɜɢɬɟɥɶɧɨ, ɱɟɦ ɛɨɥɶɲɟ ɧɟɣɪɨɧɨɜ, ɬɟɦ ɛɨɥɶɲɟ ɱɢɫɥɨ ɫɜɹɡɟɣ ɦɟɠɞɭ ɧɢɦɢ, ɢ ɬɟɦ ɛɨɥɟɟ ɫɥɨɠɧɵɟ ɡɚɞɚɱɢ ɫɩɨɫɨɛɧɚ ɪɟɲɢɬɶ ɧɟɣɪɨɧɧɚɹ ɫɟɬɶ. Ʉɪɨɦɟ ɬɨɝɨ, ɟɫɥɢ ɢɫɩɨɥɶɡɨɜɚɬɶ ɡɚɜɟɞɨɦɨ ɛɨɥɶɲɟɟ ɱɢɫɥɨ ɧɟɣɪɨɧɨɜ, ɱɟɦ ɧɟɨɛɯɨɞɢɦɨ ɞɥɹ ɪɟɲɟɧɢɹ ɡɚɞɚɱɢ, ɬɨ ɧɟɣɪɨɧɧɚɹ ɫɟɬɶ ɬɨɱɧɨ ɨɛɭɱɢɬɫɹ. ȿɫɥɢ ɠɟ ɧɚɱɢɧɚɬɶ ɫ ɧɟɛɨɥɶɲɨɝɨ ɱɢɫɥɚ ɧɟɣɪɨɧɨɜ, ɬɨ ɫɟɬɶ ɦɨɠɟɬ ɨɤɚɡɚɬɶɫɹ ɧɟɫɩɨɫɨɛɧɨɣ ɨɛɭɱɢɬɶɫɹ ɪɟɲɟɧɢɸ ɡɚɞɚɱɢ, ɢ ɜɟɫɶ ɩɪɨɰɟɫɫ ɩɪɢɞɟɬɫɹ ɩɨɜɬɨɪɹɬɶ ɫɧɚɱɚɥɚ ɫ ɛɨɥɶɲɢɦ ɱɢɫɥɨɦ ɧɟɣɪɨɧɨɜ. ɗɬɚ ɬɨɱɤɚ ɡɪɟɧɢɹ (ɱɟɦ ɛɨɥɶɲɟ – ɬɟɦ ɥɭɱɲɟ) ɩɨɩɭɥɹɪɧɚ ɫɪɟɞɢ ɪɚɡɪɚɛɨɬɱɢɤɨɜ ɧɟɣɪɨɫɟɬɟɜɨɝɨ ɩɪɨɝɪɚɦɦɧɨɝɨ ɨɛɟɫɩɟɱɟɧɢɹ. Ɍɚɤ, ɦɧɨɝɢɟ ɢɡ ɧɢɯ ɤɚɤ ɨɞɧɨ ɢɡ ɨɫɧɨɜɧɵɯ ɞɨɫɬɨɢɧɫɬɜ ɫɜɨɢɯ ɩɪɨɝɪɚɦɦ ɧɚɡɵɜɚɸɬ ɜɨɡɦɨɠɧɨɫɬɶ ɢɫɩɨɥɶɡɨɜɚX 1 2 3 4 ɧɢɹ ɥɸɛɨɝɨ ɱɢɫɥɚ ɧɟɣɪɨɧɨɜ. ȼɬɨɪɚɹ ɬɨɱɤɚ ɡɪɟɧɢɹ ɨɩɢɪɚɟɬɫɹ ɧɚ ɬɚɤɨɟ «ɷɦɩɢɪɢɱɟF( X ) 5 4 6 3 ɫɤɨɟ» ɩɪɚɜɢɥɨ: ɱɟɦ ɛɨɥɶɲɟ ɩɨɞɝɨɧɨɱɧɵɯ ɩɚɪɚɦɟɬɪɨɜ, ɬɟɦ ɯɭɠɟ ɚɩɩɪɨɤɫɢɦɚɰɢɹ ɮɭɧɤɰɢɢ ɜ ɬɟɯ ɨɛɥɚɫɬɹɯ, ɝɞɟ ɟɟ ɡɧɚɱɟɧɢɹ ɚ) ɉɨɥɢɧɨɦ 3-ɟɣ ɫɬɟɩɟɧɢ 20 ɛɵɥɢ ɡɚɪɚɧɟɟ ɧɟɢɡɜɟɫɬɧɵ. ɋ ɦɚɬɟɦɚɬɢɱɟɫɤɨɣ ɬɨɱɤɢ ɡɪɟɧɢɹ ɡɚɞɚɱɢ ɨɛɭɱɟɧɢɹ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ɫɜɨɞɹɬɫɹ ɤ ɩɪɨɞɨɥɠɟɧɢɸ 10 ɮɭɧɤɰɢɢ ɡɚɞɚɧɧɨɣ ɜ ɤɨɧɟɱɧɨɦ ɱɢɫɥɟ ɬɨɱɟɤ ɧɚ ɜɫɸ ɨɛɥɚɫɬɶ 0 ɨɩɪɟɞɟɥɟɧɢɹ. ɉɪɢ ɬɚɤɨɦ ɩɨɞɯɨɞɟ ɜɯɨɞɧɵɟ ɞɚɧɧɵɟ ɫɟɬɢ ɫɱɢ1 2 3 4 5 -10 0 ɬɚɸɬɫɹ ɚɪɝɭɦɟɧɬɚɦɢ ɮɭɧɤɰɢɢ, ɚ ɨɬɜɟɬ ɫɟɬɢ – ɡɧɚɱɟɧɢɟɦ -20 ɮɭɧɤɰɢɢ. ɇɚ ɪɢɫ. 1 ɩɪɢɜɟɞɟɧ ɩɪɢɦɟɪ ɚɩɩɪɨɤɫɢɦɚɰɢɢ ɬɚɛɥɢɱɧɨɣ ɮɭɧɤɰɢɢ ɩɨɥɢɧɨɦɚɦɢ 3-ɣ (ɪɢɫ. 1.ɚ) ɢ 7-ɣ (ɪɢɫ. 1.ɛ) ɫɬɟɩɟɧɟɣ. Ɉɱɟɜɢɞɧɨ, ɱɬɨ ɚɩɩɪɨɤɫɢɦɚɰɢɹ, ɩɨɥɭɱɟɧɧɚɹ ɫ ɩɨɦɨɛ) ɉɨɥɢɧɨɦ 7-ɨɣ ɫɬɟɩɟɧɢ ɳɶɸ ɩɨɥɢɧɨɦɚ 3-ɟɣ ɫɬɟɩɟɧɢ ɛɨɥɶɲɟ ɫɨɨɬɜɟɬɫɬɜɭɟɬ ɜɧɭɬɪɟɧ30 ɧɟɦɭ ɩɪɟɞɫɬɚɜɥɟɧɢɸ ɨ «ɩɪɚɜɢɥɶɧɨɣ» ɚɩɩɪɨɤɫɢɦɚɰɢɢ. ɇɟ20 ɫɦɨɬɪɹ ɧɚ ɫɜɨɸ ɩɪɨɫɬɨɬɭ, ɷɬɨɬ ɩɪɢɦɟɪ ɞɨɫɬɚɬɨɱɧɨ ɧɚɝɥɹɞɧɨ ɞɟɦɨɧɫɬɪɢɪɭɟɬ ɫɭɬɶ ɩɪɨɛɥɟɦɵ. 10 ȼɬɨɪɨɣ ɩɨɞɯɨɞ ɨɩɪɟɞɟɥɹɟɬ ɧɭɠɧɨɟ ɱɢɫɥɨ ɧɟɣɪɨɧɨɜ 0 ɤɚɤ ɦɢɧɢɦɚɥɶɧɨ ɧɟɨɛɯɨɞɢɦɨɟ. Ɉɫɧɨɜɧɵɦ ɧɟɞɨɫɬɚɬɤɨɦ ɹɜɥɹ0 1 2 3 4 5 -10 ɟɬɫɹ ɬɨ, ɱɬɨ ɷɬɨ, ɦɢɧɢɦɚɥɶɧɨ ɧɟɨɛɯɨɞɢɦɨɟ ɱɢɫɥɨ, ɡɚɪɚɧɟɟ ɧɟɢɡɜɟɫɬɧɨ, ɚ ɩɪɨɰɟɞɭɪɚ ɟɝɨ ɨɩɪɟɞɟɥɟɧɢɹ ɩɭɬɟɦ ɩɨɫɬɟɩɟɧɧɨɊɢɫ. 1. Ⱥɩɩɪɨɤɫɢɦɚɰɢɹ ɬɚɛɥɢɱɧɨɣ ɝɨ ɧɚɪɚɳɢɜɚɧɢɹ ɱɢɫɥɚ ɧɟɣɪɨɧɨɜ ɜɟɫɶɦɚ ɬɪɭɞɨɟɦɤɚ. Ɉɩɢɪɚɹɫɶ ɮɭɧɤɰɢɢ
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ɧɚ ɨɩɵɬ ɪɚɛɨɬɵ ɝɪɭɩɩɵ ɇɟɣɪɨɄɨɦɩ ɜ ɨɛɥɚɫɬɢ ɦɟɞɢɰɢɧɫɤɨɣ ɞɢɚɝɧɨɫɬɢɤɢ [[18, 49 – 52, 72, 90, 91, 160, 161, 165, 182 – 187, 190 – 208, 255, 295 – 298, 316, 317, 341 – 345, 351, 361]], ɤɨɫɦɢɱɟɫɤɨɣ ɧɚɜɢɝɚɰɢɢ ɢ ɩɫɢɯɨɥɨɝɢɢ ɦɨɠɧɨ ɨɬɦɟɬɢɬɶ, ɱɬɨ ɜɨ ɜɫɟɯ ɷɬɢɯ ɡɚɞɚɱɚɯ ɧɢ ɪɚɡɭ ɧɟ ɩɨɬɪɟɛɨɜɚɥɨɫɶ ɛɨɥɟɟ ɧɟɫɤɨɥɶɤɢɯ ɞɟɫɹɬɤɨɜ ɧɟɣɪɨɧɨɜ. ɉɨɞɜɨɞɹ ɢɬɨɝ ɚɧɚɥɢɡɭ ɞɜɭɯ ɤɪɚɣɧɢɯ ɩɨɡɢɰɢɣ, ɦɨɠɧɨ ɫɤɚɡɚɬɶ ɫɥɟɞɭɸɳɟɟ: ɫɟɬɶ ɫ ɦɢɧɢɦɚɥɶɧɵɦ ɱɢɫɥɨɦ ɧɟɣɪɨɧɨɜ ɞɨɥɠɧɚ ɥɭɱɲɟ («ɩɪɚɜɢɥɶɧɟɟ», ɛɨɥɟɟ ɝɥɚɞɤɨ) ɚɩɩɪɨɤɫɢɦɢɪɨɜɚɬɶ ɮɭɧɤɰɢɸ, ɧɨ ɜɵɹɫɧɟɧɢɟ ɷɬɨɝɨ ɦɢɧɢɦɚɥɶɧɨɝɨ ɱɢɫɥɚ ɧɟɣɪɨɧɨɜ ɬɪɟɛɭɟɬ ɛɨɥɶɲɢɯ ɢɧɬɟɥɥɟɤɬɭɚɥɶɧɵɯ ɡɚɬɪɚɬ ɢ ɷɤɫɩɟɪɢɦɟɧɬɨɜ ɩɨ ɨɛɭɱɟɧɢɸ ɫɟɬɟɣ. ȿɫɥɢ ɱɢɫɥɨ ɧɟɣɪɨɧɨɜ ɢɡɛɵɬɨɱɧɨ, ɬɨ ɦɨɠɧɨ ɩɨɥɭɱɢɬɶ ɪɟɡɭɥɶɬɚɬ ɫ ɩɟɪɜɨɣ ɩɨɩɵɬɤɢ, ɧɨ ɫɭɳɟɫɬɜɭɟɬ ɪɢɫɤ ɩɨɫɬɪɨɢɬɶ «ɩɥɨɯɭɸ» ɚɩɩɪɨɤɫɢɦɚɰɢɸ. ɂɫɬɢɧɚ, ɤɚɤ ɜɫɟɝɞɚ ɛɵɜɚɟɬ ɜ ɬɚɤɢɯ ɫɥɭɱɚɹɯ, ɥɟɠɢɬ ɩɨɫɟɪɟɞɢɧɟ: ɧɭɠɧɨ ɜɵɛɢɪɚɬɶ ɱɢɫɥɨ ɧɟɣɪɨɧɨɜ ɛɨɥɶɲɢɦ, ɱɟɦ ɧɟɨɛɯɨɞɢɦɨ, ɧɨ ɧɟ ɧɚɦɧɨɝɨ. ɗɬɨ ɦɨɠɧɨ ɨɫɭɳɟɫɬɜɢɬɶ ɩɭɬɟɦ ɭɞɜɨɟɧɢɹ ɱɢɫɥɚ ɧɟɣɪɨɧɨɜ ɜ ɫɟɬɢ ɩɨɫɥɟ ɤɚɠɞɨɣ ɧɟɭɞɚɱɧɨɣ ɩɨɩɵɬɤɢ ɨɛɭɱɟɧɢɹ. ɇɚɢɛɨɥɟɟ ɧɚɞɟɠɧɵɦ ɫɩɨɫɨɛɨɦ ɨɰɟɧɤɢ ɦɢɧɢɦɚɥɶɧɨɝɨ ɱɢɫɥɚ ɧɟɣɪɨɧɨɜ ɹɜɥɹɟɬɫɹ ɢɫɩɨɥɶɡɨɜɚɧɢɟ ɩɪɨɰɟɞɭɪɵ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɹ. Ʉɪɨɦɟ ɬɨɝɨ, ɩɪɨɰɟɞɭɪɚ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɹ ɩɨɡɜɨɥɹɟɬ ɨɬɜɟɬɢɬɶ ɢ ɧɚ ɜɬɨɪɨɣ ɜɨɩɪɨɫ: ɤɚɤɨɜɚ ɞɨɥɠɧɚ ɛɵɬɶ ɫɬɪɭɤɬɭɪɚ ɫɟɬɢ.
ɍɦɟɧɶɲɟɧɢɟ ɱɢɫɥɚ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɉɪɢ ɩɨɫɬɚɧɨɜɤɟ ɡɚɞɚɱɢ ɞɥɹ ɧɟɣɪɨɧɧɨɣ ɫɟɬɢ ɧɟ ɜɫɟɝɞɚ ɭɞɚɟɬɫɹ ɬɨɱɧɨ ɨɩɪɟɞɟɥɢɬɶ ɫɤɨɥɶɤɨ ɢ ɤɚɤɢɯ ɜɯɨɞɧɵɯ ɞɚɧɧɵɯ ɧɭɠɧɨ ɩɨɞɚɜɚɬɶ ɧɚ ɜɯɨɞ. ȼ ɫɥɭɱɚɟ ɧɟɞɨɫɬɚɬɤɚ ɞɚɧɧɵɯ ɫɟɬɶ ɧɟ ɫɦɨɠɟɬ ɨɛɭɱɢɬɶɫɹ ɪɟɲɟɧɢɸ ɡɚɞɚɱɢ. Ɉɞɧɚɤɨ ɝɨɪɚɡɞɨ ɱɚɳɟ ɧɚ ɜɯɨɞ ɫɟɬɢ ɩɨɞɚɟɬɫɹ ɢɡɛɵɬɨɱɧɵɣ ɧɚɛɨɪ ɜɯɨɞɧɵɯ ɩɚɪɚɦɟɬɪɨɜ. ɇɚɩɪɢɦɟɪ, ɩɪɢ ɨɛɭɱɟɧɢɢ ɫɟɬɢ ɩɨɫɬɚɧɨɜɤɟ ɞɢɚɝɧɨɡɚ ɜ ɡɚɞɚɱɚɯ ɦɟɞɢɰɢɧɫɤɨɣ ɞɢɚɝɧɨɫɬɢɤɢ ɧɚ ɜɯɨɞ ɫɟɬɢ ɩɨɞɚɸɬɫɹ ɜɫɟ ɞɚɧɧɵɟ, ɧɟɨɛɯɨɞɢɦɵɟ ɞɥɹ ɩɨɫɬɚɧɨɜɤɢ ɞɢɚɝɧɨɡɚ ɜ ɫɨɨɬɜɟɬɫɬɜɢɢ ɫ ɫɭɳɟɫɬɜɭɸɳɢɦɢ ɦɟɬɨɞɢɤɚɦɢ. ɋɥɟɞɭɟɬ ɭɱɟɫɬɶ, ɱɬɨ ɫɬɚɧɞɚɪɬɧɵɟ ɦɟɬɨɞɢɤɢ ɩɨɫɬɚɧɨɜɤɢ ɞɢɚɝɧɨɡɨɜ ɪɚɡɪɚɛɚɬɵɜɚɸɬɫɹ ɞɥɹ ɢɫɩɨɥɶɡɨɜɚɧɢɹ ɧɚ ɛɨɥɶɲɨɣ ɬɟɪɪɢɬɨɪɢɢ (ɧɚɩɪɢɦɟɪ, ɧɚ ɬɟɪɪɢɬɨɪɢɢ Ɋɨɫɫɢɢ). Ʉɚɤ ɩɪɚɜɢɥɨ, ɩɪɢ ɞɢɚɝɧɨɫɬɢɤɟ ɡɚɛɨɥɟɜɚɧɢɣ ɧɚɫɟɥɟɧɢɹ ɤɚɤɨɝɨ-ɧɢɛɭɞɶ ɧɟɛɨɥɶɲɨɝɨ ɪɟɝɢɨɧɚ (ɧɚɩɪɢɦɟɪ ɝɨɪɨɞɚ) ɦɨɠɧɨ ɨɛɨɣɬɢɫɶ ɦɟɧɶɲɢɦ ɧɚɛɨɪɨɦ ɢɫɯɨɞɧɵɯ ɞɚɧɧɵɯ. ɉɪɢɱɟɦ ɷɬɨɬ ɭɫɟɱɟɧɧɵɣ ɧɚɛɨɪ ɛɭɞɟɬ ɜɚɪɶɢɪɨɜɚɬɶɫɹ ɨɬ ɨɞɧɨɝɨ ɦɚɥɨɝɨ ɪɟɝɢɨɧɚ ɤ ɞɪɭɝɨɦɭ. Ɍɪɟɛɭɟɬɫɹ ɨɩɪɟɞɟɥɢɬɶ, ɤɚɤɢɟ ɞɚɧɧɵɟ ɧɟɨɛɯɨɞɢɦɵ ɞɥɹ ɪɟɲɟɧɢɹ ɤɨɧɤɪɟɬɧɨɣ ɡɚɞɚɱɢ, ɩɨɫɬɚɜɥɟɧɧɨɣ ɞɥɹ ɧɟɣɪɨɧɧɨɣ ɫɟɬɢ. Ʉɪɨɦɟ ɬɨɝɨ, ɜ ɯɨɞɟ ɪɟɲɟɧɢɹ ɷɬɨɣ ɡɚɞɚɱɢ ɨɩɪɟɞɟɥɹɸɬɫɹ ɡɧɚɱɢɦɨɫɬɢ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ. ɋɥɟɞɭɟɬ ɡɚɦɟɬɢɬɶ, ɱɬɨ ɭɦɟɧɢɟ ɨɩɪɟɞɟɥɹɬɶ ɡɧɚɱɢɦɨɫɬɶ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɩɪɟɞɫɬɚɜɥɹɟɬ ɫɚɦɨɫɬɨɹɬɟɥɶɧɭɸ ɰɟɧɧɨɫɬɶ.
ɋɜɟɞɟɧɢɟ ɩɚɪɚɦɟɬɪɨɜ ɧɟɣɪɨɧɧɨɣ ɫɟɬɢ ɤ ɜɵɞɟɥɟɧɧɵɦ ɡɧɚɱɟɧɢɹɦ ɉɪɢ ɨɛɭɱɟɧɢɢ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ɧɚ ɭɧɢɜɟɪɫɚɥɶɧɵɯ ɤɨɦɩɶɸɬɟɪɚɯ ɩɚɪɚɦɟɬɪɵ ɫɟɬɢ ɹɜɥɹɸɬɫɹ ɞɟɣɫɬɜɢɬɟɥɶɧɵɦɢ ɱɢɫɥɚɦɢ ɢɡ ɡɚɞɚɧɧɨɝɨ ɞɢɚɩɚɡɨɧɚ. ɉɪɢ ɚɩɩɚɪɚɬɧɨɣ ɪɟɚɥɢɡɚɰɢɢ ɧɟɣɪɨɧɧɨɣ ɫɟɬɢ ɧɟ ɜɫɟɝɞɚ ɜɨɡɦɨɠɧɨ ɪɟɚɥɢɡɨɜɚɬɶ ɜɟɫɚ ɫɜɹɡɟɣ ɫ ɜɵɫɨɤɨɣ ɬɨɱɧɨɫɬɶɸ (ɜ ɤɨɦɩɶɸɬɟɪɧɨɦ ɩɪɟɞɫɬɚɜɥɟɧɢɢ ɞɟɣɫɬɜɢɬɟɥɶɧɵɯ ɱɢɫɟɥ ɯɪɚɧɹɬɫɹ ɩɟɪɜɵɟ 6-7 ɰɢɮɪ ɦɚɧɬɢɫɫɵ). Ɉɩɵɬ ɩɨɤɚɡɵɜɚɟɬ, ɱɬɨ ɜ ɨɛɭɱɟɧɧɨɣ ɫɟɬɢ ɜɟɫɚ ɦɧɨɝɢɯ ɫɢɧɚɩɫɨɜ ɦɨɠɧɨ ɢɡɦɟɧɹɬɶ ɜ ɞɨɜɨɥɶɧɨ ɲɢɪɨɤɨɦ ɞɢɚɩɚɡɨɧɟ (ɞɨ ɩɨɥɭɲɢɪɢɧɵ ɢɧɬɟɪɜɚɥɚ ɢɡɦɟɧɟɧɢɹ ɜɟɫɚ) ɧɟ ɢɡɦɟɧɹɹ ɤɚɱɟɫɬɜɨ ɪɟɲɟɧɢɹ ɫɟɬɶɸ ɩɨɫɬɚɜɥɟɧɧɨɣ ɩɟɪɟɞ ɧɟɣ ɡɚɞɚɱɢ. ɂɫɯɨɞɹ ɢɡ ɷɬɨɝɨ, ɩɨɥɟɡɧɨ ɭɦɟɬɶ ɪɟɲɚɬɶ ɡɚɞɚɱɭ ɡɚɦɟɧɵ ɡɧɚɱɟɧɢɣ ɩɚɪɚɦɟɬɪɨɜ ɫɟɬɢ ɧɚ ɡɧɚɱɟɧɢɹ ɢɡ ɡɚɞɚɧɧɨɝɨ ɧɚɛɨɪɚ.
ɋɧɢɠɟɧɢɟ ɬɪɟɛɨɜɚɧɢɣ ɤ ɬɨɱɧɨɫɬɢ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɉɪɢ ɨɛɪɚɛɨɬɤɟ ɷɤɫɩɟɪɢɦɟɧɬɚɥɶɧɵɯ ɞɚɧɧɵɯ ɩɨɥɟɡɧɨ ɡɧɚɬɶ, ɱɬɨ ɢɡɦɟɪɟɧɢɟ ɫ ɜɵɫɨɤɨɣ ɬɨɱɧɨɫɬɶɸ, ɤɚɤ ɩɪɚɜɢɥɨ, ɞɨɪɨɠɟ ɢɡɦɟɪɟɧɢɹ ɫ ɧɢɡɤɨɣ ɬɨɱɧɨɫɬɶɸ. ɉɪɢɱɟɦ ɞɨɫɬɚɬɨɱɧɨ ɱɚɫɬɨ ɩɨɥɭɱɟɧɢɟ ɨɱɟɪɟɞɧɨɣ ɡɧɚɱɚɳɟɣ ɰɢɮɪɵ ɢɡɦɟɪɹɟɦɨɝɨ ɩɚɪɚɦɟɬɪɚ ɫɬɨɢɬ ɧɚ ɧɟɫɤɨɥɶɤɨ ɩɨɪɹɞɤɨɜ ɞɨɪɨɠɟ. ȼ ɫɜɹɡɢ ɫ ɷɬɢɦ ɡɚɞɚɱɚ ɫɧɢɠɟɧɢɹ ɬɪɟɛɨɜɚɧɢɣ ɤ ɬɨɱɧɨɫɬɢ ɢɡɦɟɪɟɧɢɹ ɜɯɨɞɧɵɯ ɩɚɪɚɦɟɬɪɨɜ ɫɟɬɢ ɩɪɢɨɛɪɟɬɚɟɬ ɫɦɵɫɥ.
ɉɨɥɭɱɟɧɢɟ ɹɜɧɵɯ ɡɧɚɧɢɣ ɢɡ ɞɚɧɧɵɯ Ɉɞɧɨɣ ɢɡ ɝɥɚɜɧɵɯ ɡɚɝɚɞɨɤ ɦɵɲɥɟɧɢɹ ɹɜɥɹɟɬɫɹ ɬɨ, ɤɚɤ ɢɡ ɫɨɜɨɤɭɩɧɨɫɬɢ ɞɚɧɧɵɯ ɨɛ ɨɛɴɟɤɬɟ, ɩɨɹɜɥɹɟɬɫɹ ɡɧɚɧɢɟ ɨ ɧɟɦ. Ⱦɨ ɧɟɞɚɜɧɟɝɨ ɜɪɟɦɟɧɢ ɧɚɢɛɨɥɶɲɢɦ ɞɨɫɬɢɠɟɧɢɟɦ ɜ ɨɛɥɚɫɬɢ ɢɫɤɭɫɫɬɜɟɧɧɨɝɨ ɢɧɬɟɥɥɟɤɬɚ ɹɜɥɹɥɨɫɶ ɥɢɛɨ ɜɨɫɩɪɨɢɡɜɟɞɟɧɢɟ ɥɨɝɢɤɢ ɱɟɥɨɜɟɤɚ-ɷɤɫɩɟɪɬɚ (ɤɥɚɫɫɢɱɟɫɤɢɟ ɷɤɫɩɟɪɬɧɵɟ ɫɢɫɬɟɦɵ), ɥɢɛɨ ɩɨɫɬɪɨɟɧɢɟ ɪɟɝɪɟɫɫɢɨɧɧɵɯ ɡɚɜɢɫɢɦɨɫɬɟɣ ɢ ɨɩɪɟɞɟɥɟɧɢɟ ɫɬɟɩɟɧɢ ɡɚɜɢɫɢɦɨɫɬɢ ɨɞɧɢɯ ɩɚɪɚɦɟɬɪɨɜ ɨɬ ɞɪɭɝɢɯ. ɋ ɞɪɭɝɨɣ ɫɬɨɪɨɧɵ, ɨɞɧɢɦ ɢɡ ɨɫɧɨɜɧɵɯ ɧɟɞɨɫɬɚɬɤɨɜ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ, ɫ ɬɨɱɤɢ ɡɪɟɧɢɹ ɦɧɨɝɢɯ ɩɨɥɶɡɨɜɚɬɟɥɟɣ, ɹɜɥɹɟɬɫɹ ɬɨ, ɱɬɨ ɧɟɣɪɨɧɧɚɹ ɫɟɬɶ ɪɟɲɚɟɬ ɡɚɞɚɱɭ, ɧɨ ɧɟ ɦɨɠɟɬ ɪɚɫɫɤɚɡɚɬɶ ɤɚɤ. ɂɧɵɦɢ ɫɥɨɜɚɦɢ ɢɡ ɨɛɭɱɟɧɧɨɣ ɧɟɣɪɨɧɧɨɣ ɫɟɬɢ ɧɟɥɶɡɹ ɢɡɜɥɟɱɶ ɚɥɝɨɪɢɬɦ ɪɟɲɟɧɢɹ ɡɚɞɚɱɢ. Ɍɚɤɢɦ ɨɛɪɚɡɨɦ ɧɟɣɪɨɧɧɵɟ ɫɟɬɢ ɩɨɡɜɨɥɹɸɬ ɩɨɥɭɱɚɬɶ ɧɟɹɜɧɵɟ ɡɧɚɧɢɹ ɢɡ ɞɚɧɧɵɯ. ȼ ɞɨɦɚɲɧɟɦ ɡɚɞɚɧɢɢ I ȼɫɟɫɨɸɡɧɨɣ ɨɥɢɦɩɢɚɞɵ ɩɨ ɧɟɣɪɨɤɨɦɩɶɸɬɢɧɝɭ, ɩɪɨɯɨɞɢɜɲɟɣ ɜ ɦɚɟ 1991 ɝɨɞɚ ɜ ɝɨɪɨɞɟ Ɉɦɫɤɟ, ɜ ɢɫɫɥɟɞɨɜɚɬɟɥɶɫɤɨɣ ɡɚɞɚɱɟ ɭɱɚɫɬɧɢɤɚɦ ɛɵɥɨ ɩɪɟɞɥɨɠɟɧɨ ɨɩɪɟɞɟɥɢɬɶ, ɤɚɤ ɧɟɣɪɨɧɧɚɹ ɫɟɬɶ ɪɟɲɚɟɬ ɡɚɞɚɱɭ ɪɚɫɩɨɡɧɚɜɚɧɢɹ ɩɹɬɢ ɩɟɪɜɵɯ ɛɭɤɜ ɥɚɬɢɧɫɤɨɝɨ ɚɥɮɚɜɢɬɚ (ɩɨɥɧɵɣ ɬɟɤɫɬ ɡɚɞɚɧɢɹ ɢ
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ɧɚɢɛɨɥɟɟ ɢɧɬɟɪɟɫɧɵɟ ɜɚɪɢɚɧɬɵ ɪɟɲɟɧɢɹ ɩɪɢɜɟɞɟɧɵ ɜ [47]). ɗɬɨ ɛɵɥɚ ɩɟɪɜɚɹ ɩɨɩɵɬɤɚ ɢɡɜɥɟɱɟɧɢɹ ɚɥɝɨɪɢɬɦɚ ɪɟɲɟɧɢɹ ɡɚɞɚɱɢ ɢɡ ɨɛɭɱɟɧɧɨɣ ɧɟɣɪɨɧɧɨɣ ɫɟɬɢ. ȼ 1995 ɝɨɞɭ ɛɵɥɚ ɫɮɨɪɦɭɥɢɪɨɜɚɧɚ ɢɞɟɹ ɥɨɝɢɱɟɫɤɢ ɩɪɨɡɪɚɱɧɵɯ ɫɟɬɟɣ, ɬɨ ɟɫɬɶ ɫɟɬɟɣ ɧɚ ɨɫɧɨɜɟ ɫɬɪɭɤɬɭɪɵ ɤɨɬɨɪɵɯ ɦɨɠɧɨ ɩɨɫɬɪɨɢɬɶ ɜɟɪɛɚɥɶɧɨɟ ɨɩɢɫɚɧɢɟ ɚɥɝɨɪɢɬɦɚ ɩɨɥɭɱɟɧɢɹ ɨɬɜɟɬɚ. ɗɬɨ ɞɨɫɬɢɝɚɟɬɫɹ ɩɪɢ ɩɨɦɨɳɢ ɫɩɟɰɢɚɥɶɧɵɦ ɨɛɪɚɡɨɦ ɩɨɫɬɪɨɟɧɧɨɣ ɩɪɨɰɟɞɭɪɵ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɹ.
ɉɨɫɬɪɨɟɧɢɟ ɥɨɝɢɱɟɫɤɢ ɩɪɨɡɪɚɱɧɵɯ ɫɟɬɟɣ Ɂɚɞɚɞɢɦɫɹ ɤɥɚɫɫɨɦ ɚ) ɜ) ɛ) ɫɟɬɟɣ, ɤɨɬɨɪɵɟ ɛɭɞɟɦ ɫɱɢɈ Ɉ ɬɚɬɶ ɥɨɝɢɱɟɫɤɢ ɩɪɨɡɪɚɱɧɵɦɢ 5 ɉɈ 4 3 3 (ɬɨ ɟɫɬɶ ɬɚɤɢɦɢ, ɤɨɬɨɪɵɟ + ɪɟɲɚɸɬ ɡɚɞɚɱɭ ɩɨɧɹɬɧɵɦ + + 3 ɞɥɹ ɧɚɫ ɫɩɨɫɨɛɨɦ, ɞɥɹ ɤɨɬɨ+ + ɪɨɝɨ ɥɟɝɤɨ ɫɮɨɪɦɭɥɢɪɨɜɚɬɶ 1 2 1 2 1 2 ɫɥɨɜɟɫɧɨɟ ɨɩɢɫɚɧɢɹ ɜ ɜɢɞɟ + + + + + + ɹɜɧɨɝɨ ɚɥɝɨɪɢɬɦɚ). ɇɚɩɪɢ+ + - + - + + + + ɦɟɪ ɩɨɬɪɟɛɭɟɦ, ɱɬɨɛɵ ɜɫɟ 3 4 9 6 8 4 8 6 3 4 9 4 8 3 6 9 ɧɟɣɪɨɧɵ ɢɦɟɥɢ ɧɟ ɛɨɥɟɟ ɝ) ɞ) ɟ) ɬɪɟɯ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ. 4 ɉ 3 Ɉ ɉ Ɂɚɞɚɞɢɦɫɹ ɧɟɣɪɨɧ1 + + ɧɨɣ ɫɟɬɶɸ ɭ ɤɨɬɨɪɨɣ ɜɫɟ 1 2 1 2 3 ɜɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɩɨɞɚɸɬɫɹ + + + + + + + + + + + + ɧɚ ɜɫɟ ɧɟɣɪɨɧɵ ɜɯɨɞɧɨɝɨ + + ɫɥɨɹ, ɚ ɜɫɟ ɧɟɣɪɨɧɵ ɤɚɠɞɨɝɨ 3 8 4 5 7 9 3 4 6 9 8 3 7 4 4 8 9 4 6 9 ɫɥɟɞɭɸɳɟɝɨ ɫɥɨɹ ɩɪɢɧɢɦɚɸɬ ɡ) ɉ 5 ɠ) 5 ɉ 6 Ɉ ɜɵɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɜɫɟɯ ɧɟɣɪɨɧɨɜ ɩɪɟɞɵɞɭɳɟɝɨ ɫɥɨɹ. + - + + Ɉɛɭɱɢɦ ɫɟɬɶ ɛɟɡɨɲɢɛɨɱɧɨɦɭ 1 2 3 3 1 2 3 4 ɪɟɲɟɧɢɸ ɡɚɞɚɱɢ. + + + ɉɨɫɥɟ ɷɬɨɝɨ ɛɭɞɟɦ + + + + + + + + + -- ɩɪɨɢɡɜɨɞɢɬɶ ɤɨɧɬɪɚɫɬɢɪɨɜɚ2 7 6 7 3 4 2 5 8 3 4 6 9 8 4 5 3 7 ɧɢɟ ɜ ɧɟɫɤɨɥɶɤɨ ɷɬɚɩɨɜ. ɇɚ Ɋɢɫ. 2. ɇɚɛɨɪ ɦɢɧɢɦɚɥɶɧɵɯ ɫɟɬɟɣ ɞɥɹ ɪɟɲɟɧɢɹ ɡɚɞɚɱɢ ɨ ɩɪɟɞɫɤɚɡɚɧɢɢ ɩɟɪɜɨɦ ɷɬɚɩɟ ɛɭɞɟɦ ɭɞɚɥɹɬɶ ɪɟɡɭɥɶɬɚɬɨɜ ɜɵɛɨɪɨɜ ɩɪɟɡɢɞɟɧɬɚ ɋɒȺ. ȼ ɪɢɫɭɧɤɟ ɢɫɩɨɥɶɡɨɜɚɧɵ ɫɥɟɬɨɥɶɤɨ ɜɯɨɞɧɵɟ ɫɜɹɡɢ ɧɟɣɞɭɸɳɢɟ ɨɛɨɡɧɚɱɟɧɢɹ: ɛɭɤɜɵ «ɉ» ɢ «Ɉ» – ɨɛɨɡɧɚɱɚɸɬ ɜɢɞ ɨɬɜɟɬɚ, ɜɵɞɚɪɨɧɨɜ ɜɯɨɞɧɨɝɨ ɫɥɨɹ. ȿɫɥɢ ɜɚɟɦɵɣ ɧɟɣɪɨɧɨɦ: «ɉ» – ɩɨɥɨɠɢɬɟɥɶɧɵɣ ɫɢɝɧɚɥ ɨɡɧɚɱɚɟɬ ɩɨɛɟɞɭ ɩɪɚɩɨɫɥɟ ɷɬɨɝɨ ɭ ɧɟɤɨɬɨɪɵɯ ɜɹɳɟɣ ɩɚɪɬɢɢ, ɚ ɨɬɪɢɰɚɬɟɥɶɧɵɣ – ɨɩɩɨɡɢɰɢɨɧɧɨɣ; «Ɉ» – ɩɨɥɨɠɢɬɟɥɶɧɟɣɪɨɧɨɜ ɨɫɬɚɥɨɫɶ ɛɨɥɶɲɟ ɧɵɣ ɫɢɝɧɚɥ ɨɡɧɚɱɚɟɬ ɩɨɛɟɞɭ ɨɩɩɨɡɢɰɢɨɧɧɨɣ ɩɚɪɬɢɢ, ɚ ɨɬɪɢɰɚɬɟɥɶɧɵɣ – ɬɪɟɯ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ, ɬɨ ɩɪɚɜɹɳɟɣ; ɭɜɟɥɢɱɢɦ ɱɢɫɥɨ ɜɯɨɞɧɵɯ 1 ɇɟɣɪɨɧ; 1 ȼɯɨɞɧɨɣ ɫɢɝɧɚɥ; ɋɜɹɡɶ ɦɟɠɞɭ ɷɥɟɦɟɧɬɚɦɢ ɫɟɬɢ; ɧɟɣɪɨɧɨɜ. Ɂɚɬɟɦ ɚɧɚɥɨɝɢɱɧɭɸ ɩɪɨɰɟɞɭɪɭ ɜɵɩɨɥɧɢɦ ɉɨɞɚɱɚ ɩɨɫɬɨɹɧɧɨɝɨ ɫɢɝɧɚɥɚ. “+”,”-” - ɡɧɚɤ ɫɜɹɡɢ; ɩɨɨɱɟɪɟɞɧɨ ɞɥɹ ɜɫɟɯ ɨɫɬɚɥɶɧɵɯ ɫɥɨɟɜ. ɉɨɫɥɟ ɡɚɜɟɪɲɟɧɢɹ ɨɩɢɫɚɧɧɨɣ ɩɪɨɰɟɞɭɪɵ ɛɭɞɟɬ ɩɨɥɭɱɟɧɚ ɥɨɝɢɱɟɫɤɢ ɩɪɨɡɪɚɱɧɚɹ ɫɟɬɶ. Ɇɨɠɧɨ ɩɪɨɢɡɜɟɫɬɢ ɞɨɩɨɥɧɢɬɟɥɶɧɨɟ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɟ ɫɟɬɢ, ɱɬɨɛɵ ɩɨɥɭɱɢɬɶ ɦɢɧɢɦɚɥɶɧɭɸ ɫɟɬɶ. ɇɚ ɪɢɫ. 2 ɩɪɢɜɟɞɟɧɵ ɜɨɫɟɦɶ ɦɢɧɢɦɚɥɶɧɵɯ ɫɟɬɟɣ. ȿɫɥɢ ɩɨɞ ɥɨɝɢɱɟɫɤɢ ɩɪɨɡɪɚɱɧɵɦɢ ɫɟɬɹɦɢ ɩɨɧɢɦɚɬɶ ɫɟɬɢ, ɭ ɤɨɬɨɪɵɯ ɤɚɠɞɵɣ ɧɟɣɪɨɧ ɢɦɟɟɬ ɧɟ ɛɨɥɟɟ ɬɪɟɯ ɜɯɨɞɨɜ, ɬɨ ɜɫɟ ɫɟɬɢ ɤɪɨɦɟ ɩɹɬɨɣ ɢ ɫɟɞɶɦɨɣ ɹɜɥɹɸɬɫɹ ɥɨɝɢɱɟɫɤɢ ɩɪɨɡɪɚɱɧɵɦɢ. ɉɹɬɚɹ ɢ ɫɟɞɶɦɚɹ ɫɟɬɢ ɞɟɦɨɧɫɬɪɢɪɭɸɬ ɬɨɬ ɮɚɤɬ, ɱɬɨ ɦɢɧɢɦɚɥɶɧɨɫɬɶ ɫɟɬɢ ɧɟ ɜɥɟɱɟɬ ɡɚ ɫɨɛɨɣ ɥɨɝɢɱɟɫɤɨɣ ɩɪɨɡɪɚɱɧɨɫɬɢ.
ɉɨɥɭɱɟɧɢɟ ɹɜɧɵɯ ɡɧɚɧɢɣ ɉɨɫɥɟ ɩɨɥɭɱɟɧɢɹ ɥɨɝɢɱɟɫɤɢ ɩɪɨɡɪɚɱɧɨɣ ɧɟɣɪɨɧɧɨɣ ɫɟɬɢ ɧɚɫɬɭɩɚɟɬ ɷɬɚɩ ɩɨɫɬɪɨɟɧɢɹ ɜɟɪɛɚɥɶɧɨɝɨ ɨɩɢɫɚɧɢɹ. ɉɪɢɧɰɢɩ ɩɨɫɬɪɨɟɧɢɹ ɜɟɪɛɚɥɶɧɨɝɨ ɨɩɢɫɚɧɢɹ ɞɨɫɬɚɬɨɱɧɨ ɩɪɨɫɬ. ɂɫɩɨɥɶɡɭɟɦɚɹ ɬɟɪɦɢɧɨɥɨɝɢɹ ɡɚɢɦɫɬɜɨɜɚɧɚ ɢɡ ɦɟɞɢɰɢɧɵ. ȼɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɛɭɞɟɦ ɧɚɡɵɜɚɬɶ ɫɢɦɩɬɨɦɚɦɢ. ȼɵɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɧɟɣɪɨɧɨɜ ɩɟɪɜɨɝɨ ɫɥɨɹ – ɫɢɧɞɪɨɦɚɦɢ ɩɟɪɜɨɝɨ ɭɪɨɜɧɹ. Ɉɱɟɜɢɞɧɨ, ɱɬɨ ɫɢɧɞɪɨɦɵ ɩɟɪɜɨɝɨ ɭɪɨɜɧɹ ɫɬɪɨɹɬɫɹ ɢɡ ɫɢɦɩɬɨɦɨɜ. ȼɵɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɧɟɣɪɨɧɨɜ k-ɨ ɫɥɨɹ ɛɭɞɟɦ ɧɚɡɵɜɚɬɶ ɫɢɧɞɪɨɦɚɦɢ k-ɨ ɭɪɨɜɧɹ. ɋɢɧɞɪɨɦɵ k-ɨ ɩɟɪɜɨɝɨ ɭɪɨɜɧɹ ɫɬɪɨɹɬɫɹ ɢɡ ɫɢɦɩɬɨɦɨɜ ɢ ɫɢɧɞɪɨɦɨɜ ɛɨɥɟɟ ɧɢɡɤɢɯ ɭɪɨɜɧɟɣ. ɋɢɧɞɪɨɦ ɩɨɫɥɟɞɧɟɝɨ ɭɪɨɜɧɹ ɹɜɥɹɟɬɫɹ ɨɬɜɟɬɨɦ. ȼ ɤɚɱɟɫɬɜɟ ɩɪɢɦɟɪɚ ɩɪɢɜɟɞɟɦ ɢɧɬɟɪɩɪɟɬɚɰɢɸ ɚɥɝɨɪɢɬɦɚ ɪɚɫɫɭɠɞɟɧɢɣ, ɩɨɥɭɱɟɧɧɨɝɨ ɩɨ ɜɬɨɪɨɣ ɫɟɬɢ ɩɪɢɜɟɞɟɧɧɨɣ ɧɚ ɪɢɫ. 2. ɉɨɫɬɚɧɨɜɤɚ ɡɚɞɚɱɢ: ɩɨ ɨɬɜɟɬɚɦ ɧɚ 12 ɜɨɩɪɨɫɨɜ ɧɟɨɛɯɨɞɢɦɨ ɩɪɟɞɫɤɚɡɚɬɶ ɩɨ-
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ɛɟɞɭ ɩɪɚɜɹɳɟɣ ɢɥɢ ɨɩɩɨɡɢɰɢɨɧɧɨɣ ɩɚɪɬɢɢ ɧɚ ɜɵɛɨɪɚɯ ɉɪɟɡɢɞɟɧɬɚ ɋɒȺ. ɇɢɠɟ ɩɪɢɜɟɞɟɧ ɫɩɢɫɨɤ ɜɨɩɪɨɫɨɜ. 1. ɉɪɚɜɹɳɚɹ ɩɚɪɬɢɹ ɛɵɥɚ ɭ ɜɥɚɫɬɢ ɛɨɥɟɟ ɨɞɧɨɝɨ ɫɪɨɤɚ? 2. ɉɪɚɜɹɳɚɹ ɩɚɪɬɢɹ ɩɨɥɭɱɢɥɚ ɛɨɥɶɲɟ 50% ɝɨɥɨɫɨɜ ɧɚ ɩɪɨɲɥɵɯ ɜɵɛɨɪɚɯ? 3. ȼ ɝɨɞ ɜɵɛɨɪɨɜ ɛɵɥɚ ɚɤɬɢɜɧɚ ɬɪɟɬɶɹ ɩɚɪɬɢɹ? 4. Ȼɵɥɚ ɫɟɪɶɟɡɧɚɹ ɤɨɧɤɭɪɟɧɰɢɹ ɩɪɢ ɜɵɞɜɢɠɟɧɢɢ ɨɬ ɩɪɚɜɹɳɟɣ ɩɚɪɬɢɢ? 5. Ʉɚɧɞɢɞɚɬ ɨɬ ɩɪɚɜɹɳɟɣ ɩɚɪɬɢɢ ɛɵɥ ɩɪɟɡɢɞɟɧɬɨɦ ɜ ɝɨɞ ɜɵɛɨɪɨɜ? 6. Ƚɨɞ ɜɵɛɨɪɨɜ ɛɵɥ ɜɪɟɦɟɧɟɦ ɫɩɚɞɚ ɢɥɢ ɞɟɩɪɟɫɫɢɢ? 7. Ȼɵɥ ɥɢ ɪɨɫɬ ɫɪɟɞɧɟɝɨ ɧɚɰɢɨɧɚɥɶɧɨɝɨ ɜɚɥɨɜɨɝɨ ɩɪɨɞɭɤɬɚ ɧɚ ɞɭɲɭ ɧɚɫɟɥɟɧɢɹ ɛɨɥɶɲɟ 2.1%? 8. ɉɪɨɢɡɜɟɥ ɥɢ ɩɪɚɜɹɳɢɣ ɩɪɟɡɢɞɟɧɬ ɫɭɳɟɫɬɜɟɧɧɵɟ ɢɡɦɟɧɟɧɢɹ ɜ ɩɨɥɢɬɢɤɟ? 9. ȼɨ ɜɪɟɦɹ ɩɪɚɜɥɟɧɢɹ ɛɵɥɢ ɫɭɳɟɫɬɜɟɧɧɵɟ ɫɨɰɢɚɥɶɧɵɟ ɜɨɥɧɟɧɢɹ? 10. Ⱥɞɦɢɧɢɫɬɪɚɰɢɹ ɩɪɚɜɹɳɟɣ ɩɚɪɬɢɢ ɜɢɧɨɜɧɚ ɜ ɫɟɪɶɟɡɧɨɣ ɨɲɢɛɤɟ ɢɥɢ ɫɤɚɧɞɚɥɟ? 11. Ʉɚɧɞɢɞɚɬ ɨɬ ɩɪɚɜɹɳɟɣ ɩɚɪɬɢɢ – ɧɚɰɢɨɧɚɥɶɧɵɣ ɝɟɪɨɣ? 12. Ʉɚɧɞɢɞɚɬ ɨɬ ɨɩɩɨɡɢɰɢɨɧɧɨɣ ɩɚɪɬɢɢ – ɧɚɰɢɨɧɚɥɶɧɵɣ ɝɟɪɨɣ? ɋɢɧɞɪɨɦ1_ɍɪɨɜɧɹ1 ɪɚɜɟɧ 1, ɟɫɥɢ ɜɵɪɚɠɟɧɢɟ ɋɢɦɩɬɨɦ4 Ɉɬɜɟɬɵ ɧɚ ɜɨɩɪɨɫɵ ɨɩɢɫɵɜɚɸɬ ɫɢ+ ɋɢɦɩɬɨɦ6 – ɋɢɦɩɬɨɦ 8 ɛɨɥɶɲɟ ɥɢɛɨ ɪɚɜɧɨ ɧɭɥɸ, ɢ ɬɭɚɰɢɸ ɧɚ ɦɨɦɟɧɬ, ɩɪɟɞɲɟɫɬɜɭɸɳɢɣ ɜɵɛɨ–1 – ɜ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ. ɪɚɦ. Ɉɬɜɟɬɵ ɤɨɞɢɪɨɜɚɥɢɫɶ ɫɥɟɞɭɸɳɢɦ ɋɢɧɞɪɨɦ2_ɍɪɨɜɧɹ1 ɪɚɜɟɧ 1, ɟɫɥɢ ɜɵɪɚɠɟɧɢɟ ɋɢɦɩɬɨɦ3 ɨɛɪɚɡɨɦ: «ɞɚ» – ɟɞɢɧɢɰɚ, «ɧɟɬ» – ɦɢɧɭɫ + ɋɢɦɩɬɨɦ4 + ɋɢɦɩɬɨɦ9 ɛɨɥɶɲɟ ɥɢɛɨ ɪɚɜɧɨ ɧɭɥɸ, ɢ ɟɞɢɧɢɰɚ. Ɉɬɪɢɰɚɬɟɥɶɧɵɣ ɫɢɝɧɚɥ ɧɚ ɜɵɯɨɞɟ –1 – ɜ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ. ɫɟɬɢ ɢɧɬɟɪɩɪɟɬɢɪɭɟɬɫɹ ɤɚɤ ɩɪɟɞɫɤɚɡɚɧɢɟ ɋɢɧɞɪɨɦ1_ɍɪɨɜɧɹ2 ɪɚɜɟɧ 1, ɟɫɥɢ ɜɵɪɚɠɟɧɢɟ ɋɢɧɩɨɛɟɞɵ ɩɪɚɜɹɳɟɣ ɩɚɪɬɢɢ. ȼ ɩɪɨɬɢɜɧɨɦ ɞɪɨɦ1_ɍɪɨɜɧɹ1 + ɋɢɧɞɪɨɦ2_ɍɪɨɜɧɹ1 ɛɨɥɶɲɟ ɥɢɛɨ ɫɥɭɱɚɟ, ɨɬɜɟɬɨɦ ɫɱɢɬɚɟɬɫɹ ɩɨɛɟɞɚ ɨɩɩɨɡɢɪɚɜɧɨ ɧɭɥɸ, ɢ –1 – ɜ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ. ɰɢɨɧɧɨɣ ɩɚɪɬɢɢ. ȼɫɟ ɧɟɣɪɨɧɵ ɪɟɚɥɢɡɨɜɵɊɢɫ. 3. Ⱥɜɬɨɦɚɬɢɱɟɫɤɢ ɩɨɫɬɪɨɟɧɧɨɟ ɜɟɪɛɚɥɶɧɨɟ ɨɩɢɫɚɧɢɟ ɜɚɥɢ ɩɨɪɨɝɨɜɭɸ ɮɭɧɤɰɢɸ, ɪɚɜɧɭɸ 1, ɟɫɥɢ ɚɥɝɟɛɪɚɢɱɟɫɤɚɹ ɫɭɦɦɚ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɋɢɧɞɪɨɦ1_ɍɪɨɜɧɹ1 ɪɚɜɟɧ 1, ɟɫɥɢ ɜɵɪɚɠɟɧɢɟ «Ȼɵɥɚ ɧɟɣɪɨɧɚ ɛɨɥɶɲɟ ɥɢɛɨ ɪɚɜɧɚ 0, ɢ -1 ɩɪɢ ɫɟɪɶɟɡɧɚɹ ɤɨɧɤɭɪɟɧɰɢɹ ɩɪɢ ɜɵɞɜɢɠɟɧɢɢ ɨɬ ɩɪɚɜɹɳɟɣ ɫɭɦɦɟ ɦɟɧɶɲɟɣ 0. ɩɚɪɬɢɢ?» + «Ƚɨɞ ɜɵɛɨɪɨɜ ɛɵɥ ɜɪɟɦɟɧɟɦ ɫɩɚɞɚ ɢɥɢ ɉɪɨɜɟɞɟɦ ɩɨɷɬɚɩɧɨ ɩɨɫɬɪɨɟɧɢɟ ɞɟɩɪɟɫɫɢɢ?» + «ɉɪɚɜɹɳɢɣ ɩɪɟɡɢɞɟɧɬ ɧɟ ɩɪɨɢɡɜɟɥ ɫɭɜɟɪɛɚɥɶɧɨɝɨ ɨɩɢɫɚɧɢɹ ɜɬɨɪɨɣ ɫɟɬɢ, ɩɪɢɜɟɳɟɫɬɜɟɧɧɵɯ ɢɡɦɟɧɟɧɢɣ ɜ ɩɨɥɢɬɢɤɟ?» ɛɨɥɶɲɟ ɥɢɛɨ ɞɟɧɧɨɣ ɧɚ ɪɢɫ. 2. ɉɨɫɥɟ ɚɜɬɨɦɚɬɢɱɟɫɤɨɝɨ ɪɚɜɧɨ ɧɭɥɸ, ɢ –1 – ɜ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ. ɩɨɫɬɪɨɟɧɢɹ ɜɟɪɛɚɥɶɧɨɝɨ ɨɩɢɫɚɧɢɹ ɩɨɥɭɱɢɦ ɋɢɧɞɪɨɦ2_ɍɪɨɜɧɹ1 ɪɚɜɟɧ 1, ɟɫɥɢ ɜɵɪɚɠɟɧɢɟ «ȼ ɝɨɞ ɬɟɤɫɬ, ɩɪɢɜɟɞɟɧɧɵɣ ɧɚ ɪɢɫ. 3. Ɂɚɦɟɧɢɦ ɜɫɟ ɜɵɛɨɪɨɜ ɛɵɥɚ ɚɤɬɢɜɧɚ ɬɪɟɬɶɹ ɩɚɪɬɢɹ?» + «Ȼɵɥɚ ɫɟɪɶɫɢɦɩɬɨɦɵ ɧɚ ɬɟɤɫɬɵ ɫɨɨɬɜɟɬɫɬɜɭɸɳɢɯ ɟɡɧɚɹ ɤɨɧɤɭɪɟɧɰɢɹ ɩɪɢ ɜɵɞɜɢɠɟɧɢɢ ɨɬ ɩɪɚɜɹɳɟɣ ɩɚɪɜɨɩɪɨɫɨɜ. Ɂɚɦɟɧɢɦ ɮɨɪɦɭɥɢɪɨɜɤɭ ɜɨɫɶɦɨɝɨ ɬɢɢ?» + «ȼɨ ɜɪɟɦɹ ɩɪɚɜɥɟɧɢɹ ɛɵɥɢ ɫɭɳɟɫɬɜɟɧɧɵɟ ɫɨɜɨɩɪɨɫɚ ɧɚ ɨɛɪɚɬɧɭɸ. ɉɨɞɫɬɚɜɢɦ ɜɦɟɫɬɨ ɰɢɚɥɶɧɵɟ ɜɨɥɧɟɧɢɹ?» ɛɨɥɶɲɟ ɥɢɛɨ ɪɚɜɧɨ ɧɭɥɸ, ɢ –1 – ɋɢɧɞɪɨɦ1_ɍɪɨɜɧɹ2 ɧɚɡɜɚɧɢɟ ɨɬɜɟɬɚ ɫɟɬɢ ɜ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ. ɩɪɢ ɜɵɯɨɞɧɨɦ ɫɢɝɧɚɥɟ 1. Ɍɟɤɫɬ, ɩɨɥɭɱɟɧɈɩɩɨɡɢɰɢɨɧɧɚɹ ɩɚɪɬɢɹ ɩɨɛɟɞɢɬ, ɟɫɥɢ ɜɵɪɚɠɟɧɢɟ ɋɢɧɧɵɣ ɜ ɪɟɡɭɥɶɬɚɬɟ ɷɬɢɯ ɩɪɟɨɛɪɚɡɨɜɚɧɢɣ ɩɪɢɞɪɨɦ1_ɍɪɨɜɧɹ1 + ɋɢɧɞɪɨɦ2_ɍɪɨɜɧɹ1 ɛɨɥɶɲɟ ɥɢɛɨ ɜɟɞɟɧ ɧɚ ɪɢɫ. 4. ɪɚɜɧɨ ɧɭɥɸ. Ɂɚɦɟɬɢɦ, ɱɬɨ ɜɫɟ ɬɪɢ ɜɨɩɪɨɫɚ, ɨɬɊɢɫ. 4. ȼɟɪɛɚɥɶɧɨɟ ɨɩɢɫɚɧɢɟ ɩɨɫɥɟ ɷɥɟɦɟɧɬɚɪɧɵɯ ɩɪɟɨɛɜɟɬɵ ɧɚ ɤɨɬɨɪɵɟ ɮɨɪɦɢɪɭɸɬ ɋɢɧɪɚɡɨɜɚɧɢɣ ɞɪɨɦ1_ɍɪɨɜɧɹ1, ɨɬɧɨɫɹɬɫɹ ɤ ɨɰɟɧɤɟ ɤɚɱɟɫɬɜɚ ɩɪɚɜɥɟɧɢɹ ɞɟɣɫɬɜɭɸɳɟɝɨ ɩɪɟɡɢɞɟɧɬɚ. ɉɪɚɜɥɟɧɢɟ ɩɥɨɯɨɟ, ɟɫɥɢ ɜɟɪɧɵ ɯɨɬɹ ɛɵ ɞɜɚ ɢɡ ɫɥɟɞɭɸɉɨɫɤɨɥɶɤɭ ɩɨɥɨɠɢɬɟɥɶɧɵɣ ɨɬɜɟɬ ɧɚ ɥɸɛɨɣ ɳɢɯ ɜɵɫɤɚɡɵɜɚɧɢɣ: «Ȼɵɥɚ ɫɟɪɶɟɡɧɚɹ ɤɨɧɤɭɪɟɧɰɢɹ ɢɡ ɷɬɢɯ ɜɨɩɪɨɫɨɜ ɯɚɪɚɤɬɟɪɢɡɭɟɬ ɧɟɞɨɫɬɚɬɤɢ ɩɪɢ ɜɵɞɜɢɠɟɧɢɢ ɨɬ ɩɪɚɜɹɳɟɣ ɩɚɪɬɢɢ», «Ƚɨɞ ɜɵɛɨɪɨɜ ɩɪɚɜɥɟɧɢɹ, ɬɨ ɷɬɨɬ ɫɢɧɞɪɨɦ ɦɨɠɧɨ ɧɚɡɜɚɬɶ ɛɵɥ ɜɪɟɦɟɧɟɦ ɫɩɚɞɚ ɢɥɢ ɞɟɩɪɟɫɫɢɢ», «ɉɪɚɜɹɳɢɣ ɩɪɟɫɢɧɞɪɨɦɨɦ ɩɥɨɯɨɣ ɩɨɥɢɬɢɤɢ. Ⱥɧɚɥɨɝɢɱɧɨ, ɡɢɞɟɧɬ ɧɟ ɩɪɨɢɡɜɟɥ ɫɭɳɟɫɬɜɟɧɧɵɯ ɢɡɦɟɧɟɧɢɣ ɜ ɩɨɥɢɬɪɢ ɜɨɩɪɨɫɚ, ɨɬɜɟɬɵ ɧɚ ɤɨɬɨɪɵɟ ɮɨɪɦɢɪɭɸɬ ɬɢɤɟ». ɋɢɧɞɪɨɦ2_ɍɪɨɜɧɹ1, ɨɬɧɨɫɹɬɫɹ ɤ ɯɚɪɚɤɬɟɋɢɬɭɚɰɢɹ ɩɨɥɢɬɢɱɟɫɤɢ ɧɟɫɬɚɛɢɥɶɧɚ, ɟɫɥɢ ɜɟɪɧɵ ɯɨɬɹ ɛɵ ɪɢɫɬɢɤɟ ɩɨɥɢɬɢɱɟɫɤɨɣ ɫɬɚɛɢɥɶɧɨɫɬɢ. ɗɬɨɬ ɞɜɚ ɢɡ ɫɥɟɞɭɸɳɢɯ ɜɵɫɤɚɡɵɜɚɧɢɣ: «ȼ ɝɨɞ ɜɵɛɨɪɨɜ ɫɢɧɞɪɨɦ ɧɚɡɨɜɟɦ ɫɢɧɞɪɨɦɨɦ ɩɨɥɢɬɢɱɟɫɤɨɣ ɛɵɥɚ ɚɤɬɢɜɧɚ ɬɪɟɬɶɹ ɩɚɪɬɢɹ», «Ȼɵɥɚ ɫɟɪɶɟɡɧɚɹ ɤɨɧɤɭɧɟɫɬɚɛɢɥɶɧɨɫɬɢ. ɪɟɧɰɢɹ ɩɪɢ ɜɵɞɜɢɠɟɧɢɢ ɨɬ ɩɪɚɜɹɳɟɣ ɩɚɪɬɢɢ», «ȼɨ Ɍɨɬ ɮɚɤɬ, ɱɬɨ ɨɛɚ ɫɢɧɞɪɨɦɚ ɩɟɪɜɨɜɪɟɦɹ ɩɪɚɜɥɟɧɢɹ ɛɵɥɢ ɫɭɳɟɫɬɜɟɧɧɵɟ ɫɨɰɢɚɥɶɧɵɟ ɝɨ ɭɪɨɜɧɹ ɩɪɢɧɢɦɚɸɬ ɡɧɚɱɟɧɢɟ 1, ɟɫɥɢ ɜɨɥɧɟɧɢɹ». ɢɫɬɢɧɧɵ ɨɬɜɟɬɵ ɯɨɬɹ ɛɵ ɧɚ ɞɜɚ ɢɡ ɬɪɟɯ Ɉɩɩɨɡɢɰɢɨɧɧɚɹ ɩɚɪɬɢɹ ɩɨɛɟɞɢɬ, ɟɫɥɢ ɩɪɚɜɥɟɧɢɟ ɩɥɨɯɨɟ ɜɨɩɪɨɫɨɜ, ɩɨɡɜɨɥɹɟɬ ɢɡɛɚɜɢɬɶɫɹ ɨɬ ɦɚɬɟɦɚɢɥɢ ɫɢɬɭɚɰɢɹ ɩɨɥɢɬɢɱɟɫɤɢ ɧɟɫɬɚɛɢɥɶɧɚ. ɬɢɱɟɫɤɢɯ ɞɟɣɫɬɜɢɣ ɫ ɨɬɜɟɬɚɦɢ ɧɚ ɜɨɩɪɨɫɵ. Ɋɢɫ. 5. Ɉɤɨɧɱɚɬɟɥɶɧɵɣ ɜɚɪɢɚɧɬ ɜɟɪɛɚɥɶɧɨɝɨ ɨɩɢɫɚɧɢɹ Ɉɤɨɧɱɚɬɟɥɶɧɵɣ ɨɬɜɟɬ ɦɨɠɟɬ ɛɵɬɶ ɢɫɬɢɧ-
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ɧɵɦ ɬɨɥɶɤɨ ɟɫɥɢ ɨɛɚ ɫɢɧɞɪɨɦɚ ɢɦɟɸɬ ɡɧɚɱɟɧɢɟ –1. ɂɫɩɨɥɶɡɭɹ ɩɪɢɜɟɞɟɧɧɵɟ ɫɨɨɛɪɚɠɟɧɢɹ, ɩɨɥɭɱɚɟɦ ɨɤɨɧɱɚɬɟɥɶɧɵɣ ɬɟɤɫɬ ɪɟɲɟɧɢɹ ɡɚɞɚɱɢ ɨ ɩɪɟɞɫɤɚɡɚɧɢɢ ɪɟɡɭɥɶɬɚɬɨɜ ɜɵɛɨɪɨɜ ɩɪɟɡɢɞɟɧɬɚ ɋɒȺ, ɩɪɢɜɟɞɟɧɧɵɣ ɧɚ ɪɢɫ. 5. Ɍɚɤɢɦ ɨɛɪɚɡɨɦ, ɢɫɩɨɥɶɡɨɜɚɜ ɢɞɟɸ ɥɨɝɢɱɟɫɤɢ ɩɪɨɡɪɚɱɧɵɯ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ɢ ɦɢɧɢɦɚɥɶɧɵɟ ɢɧɬɟɥɥɟɤɬɭɚɥɶɧɵɟ ɡɚɬɪɚɬɵ ɧɚ ɷɬɚɩɟ ɞɨɜɨɞɤɢ ɜɟɪɛɚɥɶɧɨɝɨ ɨɩɢɫɚɧɢɹ, ɛɵɥ ɩɨɥɭɱɟɧ ɬɟɤɫɬ ɪɟɲɟɧɢɹ ɡɚɞɚɱɢ. ɉɪɢɱɟɦ ɩɪɨɰɟɞɭɪɚ ɩɨɥɭɱɟɧɢɹ ɥɨɝɢɱɟɫɤɢ ɩɪɨɡɪɚɱɧɵɯ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ɫɚɦɚ ɨɬɨɛɪɚɥɚ ɡɧɚɱɢɦɵɟ ɩɪɢɡɧɚɤɢ, ɫɚɦɚ ɩɪɢɜɟɥɚ ɫɟɬɶ ɤ ɧɭɠɧɨɦɭ ɜɢɞɭ. Ⱦɚɥɟɟ ɷɥɟɦɟɧɬɚɪɧɚɹ ɩɪɨɝɪɚɦɦɚ ɩɨɫɬɪɨɢɥɚ ɩɨ ɫɬɪɭɤɬɭɪɟ ɫɟɬɢ ɜɟɪɛɚɥɶɧɨɟ ɨɩɢɫɚɧɢɟ. ɇɚ ɪɢɫ. 2 ɩɪɢɜɟɞɟɧɵ ɫɬɪɭɤɬɭɪɵ ɲɟɫɬɢ ɥɨɝɢɱɟɫɤɢ ɩɪɨɡɪɚɱɧɵɯ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ, ɪɟɲɚɸɳɢɯ ɡɚɞɚɱɭ ɨ ɩɪɟɞɫɤɚɡɚɧɢɢ ɪɟɡɭɥɶɬɚɬɨɜ ɜɵɛɨɪɨɜ ɩɪɟɡɢɞɟɧɬɚ ɋɒȺ [299 – 301]. ȼɫɟ ɫɟɬɢ, ɩɪɢɜɟɞɟɧɧɵɟ ɧɚ ɷɬɨɦ ɪɢɫɭɧɤɟ ɦɢɧɢɦɚɥɶɧɵ ɜ ɬɨɦ ɫɦɵɫɥɟ, ɱɬɨ ɢɡ ɧɢɯ ɧɟɥɶɡɹ ɭɞɚɥɢɬɶ ɧɢ ɨɞɧɨɣ ɫɜɹɡɢ ɬɚɤ, ɱɬɨɛɵ ɫɟɬɶ ɦɨɝɥɚ ɨɛɭɱɢɬɶɫɹ ɩɪɚɜɢɥɶɧɨ ɪɟɲɚɬɶ ɡɚɞɚɱɭ. ɉɨ ɱɢɫɥɭ ɧɟɣɪɨɧɨɜ ɦɢɧɢɦɚɥɶɧɚ ɩɹɬɚɹ ɫɟɬɶ. Ɂɚɦɟɬɢɦ, ɱɬɨ ɜɫɟ ɩɨɩɵɬɤɢ ɚɜɬɨɪɨɜ ɨɛɭɱɢɬɶ ɧɟɣɪɨɧɧɵɟ ɫɟɬɢ ɫɨ ɫɬɪɭɤɬɭɪɚɦɢ, ɢɡɨɛɪɚɠɟɧɧɵɦɢ ɧɚ ɪɢɫ. 2, ɢ ɫɥɭɱɚɣɧɨ ɫɝɟɧɟɪɢɪɨɜɚɧɧɵɦɢ ɧɚɱɚɥɶɧɵɦɢ ɜɟɫɚɦɢ ɫɜɹɡɟɣ ɡɚɤɨɧɱɢɥɢɫɶ ɩɪɨɜɚɥɨɦ. ȼɫɟ ɫɟɬɢ, ɩɪɢɜɟɞɟɧɧɵɟ ɧɚ ɪɢɫ. 2, ɛɵɥɢ ɩɨɥɭɱɟɧɵ ɢɡ ɫɭɳɟɫɬɜɟɧɧɨ ɛɨɥɶɲɢɯ ɫɟɬɟɣ ɫ ɩɨɦɨɳɶɸ ɩɪɨɰɟɞɭɪɵ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɹ. ɋɟɬɢ 1, 2, 3 ɢ 4 ɛɵɥɢ ɩɨɥɭɱɟɧɵ ɢɡ ɬɪɟɯɫɥɨɣɧɵɯ ɫɟɬɟɣ ɫ ɞɟɫɹɬɶɸ ɧɟɣɪɨɧɚɦɢ ɜɨ ɜɯɨɞɧɨɦ ɢ ɫɤɪɵɬɨɦ ɫɥɨɹɯ. ɋɟɬɢ 5, 6, 7 ɢ 8 ɛɵɥɢ ɩɨɥɭɱɟɧɵ ɢɡ ɞɜɭɯɫɥɨɣɧɵɯ ɫɟɬɟɣ ɫ ɞɟɫɹɬɶɸ ɧɟɣɪɨɧɚɦɢ ɜɨ ɜɯɨɞɧɨɦ ɫɥɨɟ. Ʌɟɝɤɨ ɡɚɦɟɬɢɬɶ, ɱɬɨ ɜ ɫɟɬɹɯ 2, 3, 4 ɢ 5 ɢɡɦɟɧɢɥɨɫɶ ɧɟ ɬɨɥɶɤɨ ɱɢɫɥɨ ɧɟɣɪɨɧɨɜ ɜ ɫɥɨɹɯ, ɧɨ ɢ ɱɢɫɥɨ ɫɥɨɟɜ. Ʉɪɨɦɟ ɬɨɝɨ, ɩɨɱɬɢ ɜɫɟ ɜɟɫɚ ɫɜɹɡɟɣ ɜɨ ɜɫɟɯ ɜɨɫɶɦɢ ɫɟɬɹɯ ɪɚɜɧɵ ɥɢɛɨ 1, ɥɢɛɨ -1.
ɉɪɨɰɟɞɭɪɚ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɹ ɋɭɳɟɫɬɜɭɟɬ ɞɜɚ ɬɢɩɚ ɩɪɨɰɟɞɭɪɵ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɹ – ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɟ ɩɨ ɡɧɚɱɢɦɨɫɬɢ ɩɚɪɚɦɟɬɪɨɜ ɢ ɧɟ ɭɯɭɞɲɚɸɳɟɟ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɟ. ȼ ɞɚɧɧɨɦ ɪɚɡɞɟɥɟ ɨɩɢɫɚɧɵ ɨɛɚ ɬɢɩɚ ɩɪɨɰɟɞɭɪɵ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɹ.
Ʉɨɧɬɪɚɫɬɢɪɨɜɚɧɢɟ ɧɚ ɨɫɧɨɜɟ ɩɨɤɚɡɚɬɟɥɟɣ ɡɧɚɱɢɦɨɫɬɢ ɋ ɩɨɦɨɳɶɸ ɷɬɨɣ ɩɪɨɰɟɞɭɪɵ ɦɨɠɧɨ ɤɨɧɬɪɚɫɬɢɪɨɜɚɬɶ, ɤɚɤ ɜɯɨɞɧɵɟ ɫɢɝɧɚɥɵ, ɬɚɤ ɢ ɩɚɪɚɦɟɬɪɵ ɫɟɬɢ. Ⱦɚɥɟɟ ɜ ɞɚɧɧɨɦ ɪɚɡɞɟɥɟ ɛɭɞɟɦ ɩɪɟɞɩɨɥɚɝɚɬɶ, ɱɬɨ ɤɨɧɬɪɚɫɬɢɪɭɸɬɫɹ ɩɚɪɚɦɟɬɪɵ ɫɟɬɢ. ɉɪɢ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɢ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɩɪɨɰɟɞɭɪɚ ɨɫɬɚɟɬɫɹ ɬɨɣ ɠɟ, ɧɨ ɜɦɟɫɬɨ ɩɨɤɚɡɚɬɟɥɟɣ ɡɧɚɱɢɦɨɫɬɢ ɩɚɪɚɦɟɬɪɨɜ ɫɟɬɢ ɢɫɩɨɥɶɡɭɸɬɫɹ ɩɨɤɚɡɚɬɟɥɢ ɡɧɚɱɢɦɨɫɬɢ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ. Ɉɛɨɡɧɚɱɢɦ ɱɟɪɟɡ c p – ɩɨɤɚɡɚɬɟɥɶ ɡɧɚɱɢɦɨɫɬɢ
p-ɨ ɩɚɪɚɦɟɬɪɚ; ɱɟɪɟɡ w 0p – ɬɟɤɭɳɟɟ ɡɧɚɱɟɧɢɟ p-ɨ ɩɚɪɚɦɟɬɪɚ; ɱɟɪɟɡ w*p – ɛɥɢɠɚɣɲɟɟ ɜɵɞɟɥɟɧɧɨɟ ɡɧɚɱɟɧɢɟ ɞɥɹ p-ɨ ɩɚɪɚɦɟɬɪɚ. ɂɫɩɨɥɶɡɭɹ ɜɜɟɞɟɧɧɵɟ ɨɛɨɡɧɚɱɟɧɢɹ ɩɪɨɰɟɞɭɪɭ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɹ ɦɨɠɧɨ ɡɚɩɢɫɚɬɶ ɫɥɟɞɭɸɳɢɦ ɨɛɪɚɡɨɦ: 1. ȼɵɱɢɫɥɹɟɦ ɩɨɤɚɡɚɬɟɥɢ ɡɧɚɱɢɦɨɫɬɢ. 2. ɇɚɯɨɞɢɦ ɦɢɧɢɦɚɥɶɧɵɣ ɫɪɟɞɢ ɩɨɤɚɡɚɬɟɥɟɣ ɡɧɚɱɢɦɨɫɬɢ – c * . p 3.
Ɂɚɦɟɧɢɦ ɫɨɨɬɜɟɬɫɬɜɭɸɳɢɣ ɷɬɨɦɭ ɩɨɤɚɡɚɬɟɥɸ ɡɧɚɱɢɦɨɫɬɢ ɩɚɪɚɦɟɬɪ w
0 ɧɚ p*
w*p * , ɢ ɢɫɤɥɸɱɚɟɦ
ɟɝɨ ɢɡ ɩɪɨɰɟɞɭɪɵ ɨɛɭɱɟɧɢɹ. ɉɪɟɞɴɹɜɢɦ ɫɟɬɢ ɜɫɟ ɩɪɢɦɟɪɵ ɨɛɭɱɚɸɳɟɝɨ ɦɧɨɠɟɫɬɜɚ. ȿɫɥɢ ɫɟɬɶ ɧɟ ɞɨɩɭɫɬɢɥɚ ɧɢ ɨɞɧɨɣ ɨɲɢɛɤɢ, ɬɨ ɩɟɪɟɯɨɞɢɦ ɤɨ ɜɬɨɪɨɦɭ ɲɚɝɭ ɩɪɨɰɟɞɭɪɵ. 5. ɉɵɬɚɟɦɫɹ ɨɛɭɱɢɬɶ ɩɨɥɭɱɟɧɧɭɸ ɫɟɬɶ. ȿɫɥɢ ɫɟɬɶ ɨɛɭɱɢɥɚɫɶ ɛɟɡɨɲɢɛɨɱɧɨɦɭ ɪɟɲɟɧɢɸ ɡɚɞɚɱɢ, ɬɨ ɩɟɪɟɯɨɞɢɦ ɤ ɩɟɪɜɨɦɭ ɲɚɝɭ ɩɪɨɰɟɞɭɪɵ, ɜ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ ɩɟɪɟɯɨɞɢɦ ɤ ɲɟɫɬɨɦɭ ɲɚɝɭ. 6. ȼɨɫɫɬɚɧɚɜɥɢɜɚɟɦ ɫɟɬɶ ɜ ɫɨɫɬɨɹɧɢɟ ɞɨ ɩɨɫɥɟɞɧɟɝɨ ɜɵɩɨɥɧɟɧɢɹ ɬɪɟɬɶɟɝɨ ɲɚɝɚ. ȿɫɥɢ ɜ ɯɨɞɟ ɜɵɩɨɥɧɟɧɢɹ ɲɚɝɨɜ ɫɨ ɜɬɨɪɨɝɨ ɩɨ ɩɹɬɵɣ ɛɵɥ ɨɬɤɨɧɬɪɚɫɬɢɪɨɜɚɧ ɯɨɬɹ ɛɵ ɨɞɢɧ ɩɚɪɚɦɟɬɪ, (ɱɢɫɥɨ ɨɛɭɱɚɟɦɵɯ ɩɚɪɚɦɟɬɪɨɜ ɢɡɦɟɧɢɥɨɫɶ), ɬɨ ɩɟɪɟɯɨɞɢɦ ɤ ɩɟɪɜɨɦɭ ɲɚɝɭ. ȿɫɥɢ ɧɢ ɨɞɢɧ ɩɚɪɚɦɟɬɪ ɧɟ ɛɵɥ ɨɬɤɨɧɬɪɚɫɬɢɪɨɜɚɧ, ɬɨ ɩɨɥɭɱɟɧɚ ɦɢɧɢɦɚɥɶɧɚɹ ɫɟɬɶ. ȼɨɡɦɨɠɧɨ ɢɫɩɨɥɶɡɨɜɚɧɢɟ ɪɚɡɥɢɱɧɵɯ ɨɛɨɛɳɟɧɢɣ ɷɬɨɣ ɩɪɨɰɟɞɭɪɵ. ɇɚɩɪɢɦɟɪ, ɤɨɧɬɪɚɫɬɢɪɨɜɚɬɶ ɡɚ ɨɞɢɧ ɲɚɝ ɩɪɨɰɟɞɭɪɵ ɧɟ ɨɞɢɧ ɩɚɪɚɦɟɬɪ, ɚ ɡɚɞɚɧɧɨɟ ɩɨɥɶɡɨɜɚɬɟɥɟɦ ɱɢɫɥɨ ɩɚɪɚɦɟɬɪɨɜ. ɇɚɢɛɨɥɟɟ ɪɚɞɢɤɚɥɶɧɚɹ ɩɪɨɰɟɞɭɪɚ ɫɨɫɬɨɢɬ ɜ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɢ ɩɨɥɨɜɢɧɵ ɩɚɪɚɦɟɬɪɨɜ ɫɜɹɡɟɣ. ȿɫɥɢ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɟ ɩɨɥɨɜɢɧɵ ɩɚɪɚɦɟɬɪɨɜ ɧɟ ɭɞɚɟɬɫɹ, ɬɨ ɩɵɬɚɟɦɫɹ ɤɨɧɬɪɚɫɬɢɪɨɜɚɬɶ ɱɟɬɜɟɪɬɶ ɢ ɬ.ɞ. Ⱦɪɭɝɢɟ ɜɚɪɢɚɧɬɵ ɨɛɨɛɳɟɧɢɹ ɩɪɨɰɟɞɭɪɵ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɹ ɛɭɞɭɬ ɨɩɢɫɚɧɵ ɩɪɢ ɨɩɢɫɚɧɢɢ ɪɟɲɟɧɢɹ ɡɚɞɚɱ. Ɋɟɡɭɥɶɬɚɬɵ ɩɟɪɜɵɯ ɪɚɛɨɬ ɩɨ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɸ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ɫ ɩɨɦɨɳɶɸ ɨɩɢɫɚɧɧɨɣ ɩɪɨɰɟɞɭɪɵ ɨɩɭɛɥɢɤɨɜɚɧɵ ɜ [47, 302, 303]. 4.
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Ʉɨɧɬɪɚɫɬɢɪɨɜɚɧɢɟ ɛɟɡ ɭɯɭɞɲɟɧɢɹ ɉɭɫɬɶ ɧɚɦ ɞɚɧɚ ɬɨɥɶɤɨ ɨɛɭɱɟɧɧɚɹ ɧɟɣɪɨɧɧɚɹ ɫɟɬɶ ɢ ɨɛɭɱɚɸɳɟɟ ɦɧɨɠɟɫɬɜɨ. Ⱦɨɩɭɫɬɢɦ, ɱɬɨ ɜɢɞ ɮɭɧɤɰɢɢ ɨɰɟɧɤɢ ɢ ɩɪɨɰɟɞɭɪɚ ɨɛɭɱɟɧɢɹ ɧɟɣɪɨɧɧɨɣ ɫɟɬɢ ɧɟɢɡɜɟɫɬɧɵ. ȼ ɷɬɨɦ ɫɥɭɱɚɟ ɬɚɤ ɠɟ ɜɨɡɦɨɠɧɨ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɟ ɫɟɬɢ. ɉɪɟɞɩɨɥɨɠɢɦ, ɱɬɨ ɞɚɧɧɚɹ ɫɟɬɶ ɢɞɟɚɥɶɧɨ ɪɟɲɚɟɬ ɡɚɞɚɱɭ. ȼ ɷɬɨɦ ɫɥɭɱɚɟ ɜɨɡɦɨɠɧɨ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɟ ɫɟɬɢ ɞɚɠɟ ɩɪɢ ɨɬɫɭɬɫɬɜɢɢ ɨɛɭɱɚɸɳɟɣ ɜɵɛɨɪɤɢ, ɩɨɫɤɨɥɶɤɭ ɟɟ ɦɨɠɧɨ ɫɝɟɧɟɪɢɪɨɜɚɬɶ ɢɫɩɨɥɶɡɭɹ ɫɟɬɶ ɞɥɹ ɩɨɥɭɱɟɧɢɹ ɨɬɜɟɬɨɜ. Ɂɚɞɚɱɚ ɧɟ ɭɯɭɞɲɚɸɳɟɝɨ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɹ ɫɬɚɜɢɬɫɹ ɫɥɟɞɭɸɳɢɦ ɨɛɪɚɡɨɦ: ɧɟɨɛɯɨɞɢɦɨ ɬɚɤ ɩɪɨɜɟɫɬɢ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɟ ɩɚɪɚɦɟɬɪɨɜ, ɱɬɨɛɵ ɜɵɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɫɟɬɢ ɩɪɢ ɪɟɲɟɧɢɢ ɜɫɟɯ ɩɪɢɦɟɪɨɜ ɢɡɦɟɧɢɥɢɫɶ ɧɟ ɛɨɥɟɟ ɱɟɦ ɧɚ ɡɚɞɚɧɧɭɸ ɜɟɥɢɱɢɧɭ. Ⱦɥɹ ɪɟɲɟɧɢɹ ɡɚɞɚɱɚ ɪɟɞɭɰɢɪɭɟɬɫɹ ɧɚ ɨɬɞɟɥɶɧɵɣ ɚɞɚɩɬɢɜɧɵɣ ɫɭɦɦɚɬɨɪ: ɧɟɨɛɯɨɞɢɦɨ ɬɚɤ ɢɡɦɟɧɢɬɶ ɩɚɪɚɦɟɬɪɵ, ɱɬɨɛɵ ɜɵɯɨɞɧɨɣ ɫɢɝɧɚɥ ɚɞɚɩɬɢɜɧɨɝɨ ɫɭɦɦɚɬɨɪɚ ɩɪɢ ɪɟɲɟɧɢɢ ɤɚɠɞɨɝɨ ɩɪɢɦɟɪɚ ɢɡɦɟɧɢɥɫɹ ɧɟ ɛɨɥɟɟ ɱɟɦ ɧɚ ɡɚɞɚɧɧɭɸ ɜɟɥɢɱɢɧɭ. q q Ɉɛɨɡɧɚɱɢɦ ɱɟɪɟɡ x p p-ɣ ɜɯɨɞɧɨɣ ɫɢɝɧɚɥ ɫɭɦɦɚɬɨɪɚ ɩɪɢ ɪɟɲɟɧɢɢ q-ɨ ɩɪɢɦɟɪɚ; ɱɟɪɟɡ f – ɜɵɯɨɞɧɨɣ ɫɢɝɧɚɥ ɫɭɦɦɚɬɨɪɚ ɩɪɢ ɪɟɲɟɧɢɢ q-ɨ ɩɪɢɦɟɪɚ; ɱɟɪɟɡ w p – ɜɟɫ p-ɨ ɜɯɨɞɧɨɝɨ ɫɢɝɧɚɥɚ ɫɭɦɦɚɬɨɪɚ; ɱɟɪɟɡ
e – ɬɪɟɛɭɟɦɭɸ ɬɨɱɧɨɫɬɶ; ɱɟɪɟɡ n – ɱɢɫɥɨ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ ɫɭɦɦɚɬɨɪɚ; ɱɟɪɟɡ m – ɱɢɫɥɨ ɩɪɢɦɟ-
ɪɨɜ. Ɉɱɟɜɢɞɧɨ, ɱɬɨ ɩɪɢ ɪɟɲɟɧɢɢ ɩɪɢɦɟɪɚ ɜɵɩɨɥɧɹɟɬɫɹ ɪɚɜɟɧɫɬɜɨ
ɬɚɤɨɣ ɧɚɛɨɪ ɢɧɞɟɤɫɨɜ
I = {i1 ,K, ik } , ɱɬɨ f -
åa p x p
p ÎI
fq =
n
å w p x qp . Ɍɪɟɛɭɟɬɫɹ ɧɚɣɬɢ
p =1
< e , ɝɞɟ a p – ɧɨɜɵɣ ɜɟɫ p-ɨ ɜɯɨɞɧɨɝɨ ɫɢɝ-
ɧɚɥɚ ɫɭɦɦɚɬɨɪɚ. ɇɚɛɨɪ ɢɧɞɟɤɫɨɜ ɛɭɞɟɦ ɫɬɪɨɢɬɶ ɩɨ ɫɥɟɞɭɸɳɟɦɭ ɚɥɝɨɪɢɬɦɭ. ( ) ( 0) ( ) 1. ɉɨɥɨɠɢɦ f = f , x *p = x p , I 0 = Æ , J 0 = 1, K , n , k=0. 2.
* Ⱦɥɹ ɜɫɟɯ ɜɟɤɬɨɪɨɜ x p ɬɚɤɢɯ, ɱɬɨ
p ÎJ
(k )
{
}
, ɩɪɨɞɟɥɚɟɦ ɫɥɟɞɭɸɳɟɟ ɩɪɟɨɛɪɚɡɨɜɚɧɢɟ: ɟɫɥɢ
{}
x *p << e , ɬɨ ɢɫɤɥɸɱɚɟɦ p ɢɡ ɦɧɨɠɟɫɬɜɚ ɨɛɪɚɛɚɬɵɜɚɟɦɵɯ ɜɟɤɬɨɪɨɜ – J ( k ) = J ( k ) / p , ɜ
(k ) * * ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ ɧɨɪɦɢɪɭɟɦ ɜɟɤɬɨɪ x p ɧɚ ɟɞɢɧɢɱɧɭɸ ɞɥɢɧɭ – x p = x p 3. 4.
ȿɫɥɢ f
(k )
( ) < e ɢɥɢ J 0 = Æ , ɬɨ ɩɟɪɟɯɨɞɢɦ ɤ ɲɚɝɭ 10.
(k ) ɇɚɯɨɞɢɦ ik +1 – ɧɨɦɟɪ ɜɟɤɬɨɪɚ, ɧɚɢɛɨɥɟɟ ɛɥɢɡɤɨɝɨ ɤ f ɢɡ ɭɫɥɨɜɢɹ ( ( k) (k) k) (k) = min f , x p . f , xi k +1 p ÎJ ( k )
(
5.
6. 7.
8.
x *p .
)
(
)
{
}
( k +1) = J ( k ) / i ɂɫɤɥɸɱɚɟɦ ik +1 ɢɡ ɦɧɨɠɟɫɬɜɚ ɢɧɞɟɤɫɨɜ ɨɛɪɚɛɚɬɵɜɚɟɦɵɯ ɜɟɤɬɨɪɨɜ: J k +1 . ( ( k +1) k) = I U i k +1 Ⱦɨɛɚɜɥɹɟɦ ik + 1 ɜ ɦɧɨɠɟɫɬɜɨ ɢɧɞɟɤɫɨɜ ɧɚɣɞɟɧɧɵɯ ɜɟɤɬɨɪɨɜ: I ȼɵɱɢɫɥɹɟɦ ɧɟ ɚɩɩɪɨɤɫɢɦɢɪɨɜɚɧɧɭɸ ɱɚɫɬɶ (ɨɲɢɛɤɭ ɚɩɩɪɨɤɫɢɦɚɰɢɢ) ɜɟɤɬɨɪɚ ɜɵɯɨɞɧɵɯ ɫɢɝɧɚ( k +1) = f ( k ) - f ( k ) , x ( k ) x ( k ) ɥɨɜ: f i k +1 i k +1
{
(
}
)
ɉɪɟɨɛɪɚɡɭɟɦ ɨɛɪɚɛɚɬɵɜɚɟɦɵɟ ɜɟɤɬɨɪɚ ɤ ɩɪɨɦɟɠɭɬɨɱɧɨɦɭ ɩɪɟɞɫɬɚɜɥɟɧɢɸ – ɨɪɬɨɝɨɧɚɥɢɡɭɟɦ ɢɯ ɤ (k) (k ) (k) ɜɟɤɬɨɪɭ xi , ɞɥɹ ɱɟɝɨ ɤɚɠɞɵɣ ɜɟɤɬɨɪ x p , ɭ ɤɨɬɨɪɨɝɨ p Î J ɩɪɟɨɛɪɚɡɭɟɦ ɩɨ ɫɥɟɞɭɸɳɟɣ k +1 ɮɨɪɦɭɥɟ:
(
)
(k) (k) x *p = x (pk ) - x (pk ) , xi x . k +1 i k +1
9. ɍɜɟɥɢɱɢɜɚɟɦ k ɧɚ ɟɞɢɧɢɰɭ ɢ ɩɟɪɟɯɨɞɢɦ ɤ ɲɚɝɭ 2. 10. ȿɫɥɢ k = 0 , ɬɨ ɜɟɫɶ ɫɭɦɦɚɬɨɪ ɭɞɚɥɹɟɬɫɹ ɢɡ ɫɟɬɢ ɢ ɪɚɛɨɬɚ ɚɥɝɨɪɢɬɦɚ ɡɚɜɟɪɲɚɟɬɫɹ. 11. ȿɫɥɢ k = n + 1 , ɬɨ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɟ ɧɟɜɨɡɦɨɠɧɨ ɢ ɫɭɦɦɚɬɨɪ ɨɫɬɚɟɬɫɹ ɧɟɢɡɦɟɧɧɵɦ.
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12. ȼ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ ɩɨɥɚɝɚɟɦ
ɬɟɦɭ ɭɪɚɜɧɟɧɢɣ
f - f (k) =
I = I ( k ) ɢ ɜɵɱɢɫɥɹɟɦ ɧɨɜɵɟ ɜɟɫɚ ɫɜɹɡɟɣ a p ( p Î I ) ɪɟɲɚɹ ɫɢɫ-
åapxp .
p ÎI
13. ɍɞɚɥɹɟɦ ɢɡ ɫɟɬɢ ɫɜɹɡɢ ɫ ɧɨɦɟɪɚɦɢ
p Î J , ɜɟɫɚ ɨɫɬɚɜɲɢɯɫɹ ɫɜɹɡɟɣ ɩɨɥɚɝɚɟɦ ɪɚɜɧɵɦɢ
a p ( p Î I ). Ⱦɚɧɧɚɹ ɩɪɨɰɟɞɭɪɚ ɩɨɡɜɨɥɹɟɬ ɩɪɨɢɡɜɨɞɢɬɶ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɟ ɚɞɚɩɬɢɜɧɵɯ ɫɭɦɦɚɬɨɪɨɜ. ɉɪɢɱɟɦ ɡɧɚɱɟɧɢɹ, ɜɵɱɢɫɥɹɟɦɵɟ ɤɚɠɞɵɦ ɫɭɦɦɚɬɨɪɨɦ ɩɨɫɥɟ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɹ, ɨɬɥɢɱɚɸɬɫɹ ɨɬ ɢɫɯɨɞɧɵɯ ɧɟ ɛɨɥɟɟ ɱɟɦ ɧɚ ɡɚɞɚɧɧɭɸ ɜɟɥɢɱɢɧɭ. Ɉɞɧɚɤɨ, ɢɫɯɨɞɧɨ ɛɵɥɚ ɡɚɞɚɧɚ ɬɨɥɶɤɨ ɦɚɤɫɢɦɚɥɶɧɨ ɞɨɩɭɫɬɢɦɚɹ ɩɨɝɪɟɲɧɨɫɬɶ ɪɚɛɨɬɵ ɫɟɬɢ ɜ ɰɟɥɨɦ. ɋɩɨɫɨɛɵ ɩɨɥɭɱɟɧɢɹ ɞɨɩɭɫɬɢɦɵɯ ɩɨɝɪɟɲɧɨɫɬɟɣ ɞɥɹ ɨɬɞɟɥɶɧɵɯ ɫɭɦɦɚɬɨɪɨɜ ɢɫɯɨɞɹ ɢɡ ɡɚɞɚɧɧɨɣ ɞɨɩɭɫɬɢɦɨɣ ɩɨɝɪɟɲɧɨɫɬɢ ɞɥɹ ɜɫɟɣ ɫɟɬɢ ɨɩɢɫɚɧɵ ɜ ɪɹɞɟ ɪɚɛɨɬ [94 –96, 167, 209 – 213, 352].
Ƚɢɛɪɢɞɧɚɹ ɩɪɨɰɟɞɭɪɚ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɹ Ɇɨɠɧɨ ɭɩɪɨɫɬɢɬɶ ɩɪɨɰɟɞɭɪɭ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɹ, ɨɩɢɫɚɧɧɭɸ ɜ ɪɚɡɞ. «Ʉɨɧɬɪɚɫɬɢɪɨɜɚɧɢɟ ɛɟɡ ɭɯɭɞɲɟɧɢɹ». ɉɪɟɞɥɚɝɚɟɦɚɹ ɩɪɨɰɟɞɭɪɚ ɝɨɞɢɬɫɹ ɬɨɥɶɤɨ ɞɥɹ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɹ ɜɟɫɨɜ ɫɜɹɡɟɣ ɚɞɚɩɬɢɜɧɨɝɨ ɫɭɦɦɚɬɨɪɚ (ɫɦ. ɪɚɡɞ. «ɋɨɫɬɚɜɧɵɟ ɷɥɟɦɟɧɬɵ»). Ʉɨɧɬɪɚɫɬɢɪɨɜɚɧɢɟ ɜɟɫɨɜ ɫɜɹɡɟɣ ɩɪɨɢɡɜɨɞɢɬɫɹ ɨɬɞɟɥɶɧɨ ɞɥɹ ɤɚɠɞɨɝɨ ɫɭɦɦɚɬɨɪɚ. Ⱥɞɚɩɬɢɜɧɵɣ ɫɭɦɦɚɬɨɪ ɫɭɦɦɢɪɭɟɬ ɜɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɧɟɣɪɨɧɚ, ɭɦɧɨɠɟɧɧɵɟ ɧɚ ɫɨɨɬɜɟɬɫɬɜɭɸɳɢɟ ɜɟɫɚ ɫɜɹɡɟɣ. Ⱦɥɹ ɪɚɛɨɬɵ ɧɟɣɪɨɧɚ ɧɚɢɦɟɧɟɟ ɡɧɚɱɢɦɵɦ ɛɭɞɟɦ ɫɱɢɬɚɬɶ ɬɨɬ ɜɟɫ, ɤɨɬɨɪɵɣ ɩɪɢ q ɪɟɲɟɧɢɢ ɩɪɢɦɟɪɚ ɞɚɫɬ ɧɚɢɦɟɧɶɲɢɣ ɜɤɥɚɞ ɜ ɫɭɦɦɭ. Ɉɛɨɡɧɚɱɢɦ ɱɟɪɟɡ x p ɜɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɪɚɫɫɦɚɬɪɢ-
(
)
ɜɚɟɦɨɝɨ ɚɞɚɩɬɢɜɧɨɝɨ ɫɭɦɦɚɬɨɪɚ ɩɪɢ ɪɟɲɟɧɢɢ q-ɝɨ ɩɪɢɦɟɪɚ. ɉɨɤɚɡɚɬɟɥɟɦ ɡɧɚɱɢɦɨɫɬɢ ɜɟɫɚ ɧɚɡɨɜɟɦ ɫɥɟq q * ɞɭɸɳɭɸ ɜɟɥɢɱɢɧɭ: c p = w p - w p × x p . ɍɫɪɟɞɧɟɧɧɵɣ ɩɨ ɜɫɟɦ ɩɪɢɦɟɪɚɦ ɨɛɭɱɚɸɳɟɝɨ ɦɧɨɠɟɫɬɜɚ
(
)
* q ɩɨɤɚɡɚɬɟɥɶ ɡɧɚɱɢɦɨɫɬɢ ɢɦɟɟɬ ɜɢɞ c p = w p - w p × max x p . ɉɪɨɢɡɜɨɞɢɦ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɟ ɩɨ ɩɪɨq ɰɟɞɭɪɟ, ɩɪɢɜɟɞɟɧɧɨɣ ɜ ɪɚɡɞ. «Ʉɨɧɬɪɚɫɬɢɪɨɜɚɧɢɟ ɧɚ ɨɫɧɨɜɟ ɩɨɤɚɡɚɬɟɥɟɣ ɡɧɚɱɢɦɨɫɬɢ». ȼ ɫɚɦɨɣ ɩɪɨɰɟɞɭɪɟ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɹ ɟɫɬɶ ɬɨɥɶɤɨ ɨɞɧɨ ɨɬɥɢɱɢɟ – ɜɦɟɫɬɨ ɩɪɨɜɟɪɤɢ ɧɚ ɧɚɥɢɱɢɟ ɨɲɢɛɨɤ ɩɪɢ ɩɪɟɞɴɹɜɥɟɧɢɢ ɜɫɟɯ ɩɪɢɦɟɪɨɜ ɩɪɨɜɟɪɹɟɬɫɹ, ɱɬɨ ɧɨɜɵɟ ɜɵɯɨɞɧɵɟ ɫɢɝɧɚɥɵ ɫɟɬɢ ɨɬɥɢɱɚɸɬɫɹ ɨɬ ɩɟɪɜɨɧɚɱɚɥɶɧɵɯ ɧɟ ɛɨɥɟɟ ɱɟɦ ɧɚ ɡɚɞɚɧɧɭɸ ɜɟɥɢɱɢɧɭ.
Ʉɨɧɬɪɚɫɬɢɪɨɜɚɧɢɟ ɩɪɢ ɨɛɭɱɟɧɢɢ ɋɭɳɟɫɬɜɭɟɬ ɟɳɟ ɨɞɢɧ ɫɩɨɫɨɛ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɹ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ. ɂɞɟɹ ɷɬɨɝɨ ɫɩɨɫɨɛɚ ɫɨɫɬɨɢɬ ɜ ɬɨɦ, ɱɬɨ ɮɭɧɤɰɢɹ ɨɰɟɧɤɢ ɦɨɞɟɪɧɢɡɢɪɭɟɬɫɹ ɬɚɤɢɦ ɫɩɨɫɨɛɨɦ, ɱɬɨɛɵ ɞɥɹ ɫɧɢɠɟɧɢɹ ɨɰɟɧɤɢ ɛɵɥɨ ɜɵɝɨɞɧɨ ɩɪɢɜɟɫɬɢ ɫɟɬɶ ɤ ɡɚɞɚɧɧɨɦɭ ɜɢɞɭ. Ɋɚɫɫɦɨɬɪɢɦ ɪɟɲɟɧɢɟ ɡɚɞɚɱɢ ɩɪɢɜɟɞɟɧɢɹ ɩɚɪɚɦɟɬɪɨɜ ɫɟɬɢ ɤ ɜɵɞɟɥɟɧɧɵɦ ɡɧɚɱɟɧɢɹɦ. ɂɫɩɨɥɶɡɭɹ ɨɛɨɡɧɚɱɟɧɢɹ ɢɡ ɩɪɟɞɵɞɭɳɢɯ ɪɚɡɞɟɥɨɜ ɬɪɟɛɭɟɦɭɸ ɞɨɛɚɜɤɭ ɤ ɮɭɧɤɰɢɢ ɨɰɟɧɤɢ, ɹɜɥɹɸɳɭɸɫɹ ɲɬɪɚɮɨɦ ɡɚ ɨɬɤɥɨɧɟɧɢɟ ɡɧɚɱɟɧɢɹ ɩɚɪɚɦɟɬɪɚ ɨɬ ɛɥɢɠɚɣɲɟɝɨ ɜɵɞɟɥɟɧɧɨɝɨ ɡɧɚɱɟɧɢɹ:, ɦɨɠɧɨ 2 ɡɚɩɢɫɚɬɶ ɜ ɜɢɞɟ w p - w*p . p Ⱦɥɹ ɪɟɲɟɧɢɹ ɞɪɭɝɢɯ ɡɚɞɚɱ ɜɢɞ ɞɨɛɚɜɨɤ ɤ ɮɭɧɤɰɢɢ ɨɰɟɧɤɢ ɦɧɨɝɨ ɫɥɨɠɧɟɟ.
å(
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Ɉɩɪɟɞɟɥɟɧɢɟ ɩɨɤɚɡɚɬɟɥɟɣ ɡɧɚɱɢɦɨɫɬɢ ȼ ɞɚɧɧɨɦ ɪɚɡɞɟɥɟ ɨɩɢɫɚɧ ɫɩɨɫɨɛ ɨɩɪɟɞɟɥɟɧɢɹ ɩɨɤɚɡɚɬɟɥɟɣ ɡɧɚɱɢɦɨɫɬɢ ɩɚɪɚɦɟɬɪɨɜ ɢ ɫɢɝɧɚɥɨɜ. . Ⱦɚɥɟɟ ɛɭɞɟɦ ɝɨɜɨɪɢɬɶ ɨɛ ɨɩɪɟɞɟɥɟɧɢɢ ɡɧɚɱɢɦɨɫɬɢ ɩɚɪɚɦɟɬɪɨɜ. ɉɨɤɚɡɚɬɟɥɢ ɡɧɚɱɢɦɨɫɬɢ ɫɢɝɧɚɥɨɜ ɫɟɬɢ ɨɩɪɟɞɟɥɹɸɬɫɹ ɩɨ ɬɟɦ ɠɟ ɮɨɪɦɭɥɚɦ ɫ ɡɚɦɟɧɨɣ ɩɚɪɚɦɟɬɪɨɜ ɧɚ ɫɢɝɧɚɥɵ.
Ɉɩɪɟɞɟɥɟɧɢɟ ɩɨɤɚɡɚɬɟɥɟɣ ɡɧɚɱɢɦɨɫɬɢ ɱɟɪɟɡ ɝɪɚɞɢɟɧɬ ɇɟɣɪɨɧɧɚɹ ɫɟɬɶ ɞɜɨɣɫɬɜɟɧɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ ɦɨɠɟɬ ɜɵɱɢɫɥɹɬɶ ɝɪɚɞɢɟɧɬ ɮɭɧɤɰɢɢ ɨɰɟɧɤɢ ɩɨ ɜɯɨɞɧɵɦ ɫɢɝɧɚɥɚɦ ɢ ɨɛɭɱɚɟɦɵɦ ɩɚɪɚɦɟɬɪɚɦ ɫɟɬɢ ɉɨɤɚɡɚɬɟɥɟɦ ɡɧɚɱɢɦɨɫɬɢ ɩɚɪɚɦɟɬɪɚ ɩɪɢ ɪɟɲɟɧɢɢ q-ɨ ɩɪɢɦɟɪɚ ɛɭɞɟɦ ɧɚɡɵɜɚɬɶ ɜɟɥɢɱɢɧɭ, ɤɨɬɨɪɚɹ ɩɨɤɚɡɵɜɚɟɬ ɧɚɫɤɨɥɶɤɨ ɢɡɦɟɧɢɬɫɹ ɡɧɚɱɟɧɢɟ ɮɭɧɤɰɢɢ ɨɰɟɧɤɢ ɪɟɲɟɧɢɹ ɫɟɬɶɸ q-ɨ ɩɪɢɦɟɪɚ ɟɫɥɢ ɬɟɤɭɳɟɟ * ɡɧɚɱɟɧɢɟ ɩɚɪɚɦɟɬɪɚ w p ɡɚɦɟɧɢɬɶ ɧɚ ɜɵɞɟɥɟɧɧɨɟ ɡɧɚɱɟɧɢɟ w p . Ɍɨɱɧɨ ɷɬɭ ɜɟɥɢɱɢɧɭ ɦɨɠɧɨ ɨɩɪɟɞɟɥɢɬɶ ɩɪɨɢɡɜɟɞɹ ɡɚɦɟɧɭ ɢ ɜɵɱɢɫɥɢɜ ɨɰɟɧɤɭ ɫɟɬɢ. Ɉɞɧɚɤɨ ɭɱɢɬɵɜɚɹ ɛɨɥɶɲɨɟ ɱɢɫɥɨ ɩɚɪɚɦɟɬɪɨɜ ɫɟɬɢ ɜɵɱɢɫɥɟɧɢɟ
CHAP9.DOC
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ɩɨɤɚɡɚɬɟɥɟɣ ɡɧɚɱɢɦɨɫɬɢ ɞɥɹ ɜɫɟɯ ɩɚɪɚɦɟɬɪɨɜ ɛɭɞɟɬ ɡɚɧɢɦɚɬɶ ɦɧɨɝɨ ɜɪɟɦɟɧɢ. Ⱦɥɹ ɭɫɤɨɪɟɧɢɹ ɩɪɨɰɟɞɭɪɵ ɨɰɟɧɤɢ ɩɚɪɚɦɟɬɪɨɜ ɡɧɚɱɢɦɨɫɬɢ ɜɦɟɫɬɨ ɬɨɱɧɵɯ ɡɧɚɱɟɧɢɣ ɢɫɩɨɥɶɡɭɸɬ ɪɚɡɥɢɱɧɵɟ ɨɰɟɧɤɢ [33, 64, 90]. Ɋɚɫɫɦɨɬɪɢɦ ɩɪɨɫɬɟɣɲɭɸ ɢ ɧɚɢɛɨɥɟɟ ɢɫɩɨɥɶɡɭɟɦɭɸ ɥɢɧɟɣɧɭɸ ɨɰɟɧɤɭ ɩɨɤɚɡɚɬɟɥɟɣ ɡɧɚɱɢɦɨɫɬɢ. Ɋɚɡɥɨɠɢɦ ɮɭɧɤɰɢɸ ɨɰɟɧɤɢ ɜ ɪɹɞ Ɍɟɣɥɨɪɚ ɫ ɬɨɱɧɨɫɬɶɸ ɞɨ ɱɥɟɧɨɜ ɩɟɪɜɨɝɨ ɩɨɪɹɞɤɚ:
¶Hq ( w p - w*p ) ,ɝɞɟ Hq0 – ɡɧɚɱɟɧɢɟ ɮɭɧɤɰɢɢ ɨɰɟɧɤɢ ɪɟɲɟɧɢɹ q-ɨ ɩɪɢɦɟɪɚ ɩɪɢ Hq ( w* ) = Hq0 + å ¶w p
p
*
w = w . Ɍɚɤɢɦ ɨɛɪɚɡɨɦ ɩɨɤɚɡɚɬɟɥɶ ɡɧɚɱɢɦɨɫɬɢ p-ɨ ɩɚɪɚɦɟɬɪɚ ɩɪɢ ɪɟɲɟɧɢɢ q-ɨ ɩɪɢɦɟɪɚ ɨɩɪɟɞɟɥɹɟɬɫɹ ɩɨ ɫɥɟɞɭɸɳɟɣ ɮɨɪɦɭɥɟ: c pq =
¶Hq
¶w p
( w p - w*p )
(1)
ɉɨɤɚɡɚɬɟɥɶ ɡɧɚɱɢɦɨɫɬɢ (1) ɦɨɠɟɬ ɜɵɱɢɫɥɹɬɶɫɹ ɞɥɹ ɪɚɡɥɢɱɧɵɯ ɨɛɴɟɤɬɨɜ. ɇɚɢɛɨɥɟɟ ɱɚɫɬɨ ɟɝɨ ɜɵɱɢɫɥɹɸɬ ɞɥɹ ɨɛɭɱɚɟɦɵɯ ɩɚɪɚɦɟɬɪɨɜ ɫɟɬɢ. Ɉɞɧɚɤɨ ɩɨɤɚɡɚɬɟɥɶ ɡɧɚɱɢɦɨɫɬɢ ɜɢɞɚ (1) ɩɪɢɦɟɧɢɦ ɢ ɞɥɹ ɫɢɝɧɚɥɨɜ. Ʉɚɤ ɭɠɟ ɨɬɦɟɱɚɥɨɫɶ ɜ ɝɥɚɜɟ «Ɉɩɢɫɚɧɢɟ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ» ɫɟɬɶ ɩɪɢ ɨɛɪɚɬɧɨɦ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɢ ɜɫɟɝɞɚ ɜɵɱɢɫɥɹɟɬ ɞɜɚ ɜɟɤɬɨɪɚ ɝɪɚɞɢɟɧɬɚ – ɝɪɚɞɢɟɧɬ ɮɭɧɤɰɢɢ ɨɰɟɧɤɢ ɩɨ ɨɛɭɱɚɟɦɵɦ ɩɚɪɚɦɟɬɪɚɦ ɫɟɬɢ ɢ ɩɨ ɜɫɟɦ ɫɢɝɧɚɥɚɦ ɫɟɬɢ. ȿɫɥɢ ɩɨɤɚɡɚɬɟɥɶ ɡɧɚɱɢɦɨɫɬɢ ɜɵɱɢɫɥɹɟɬɫɹ ɞɥɹ ɜɵɹɜɥɟɧɢɹ ɧɚɢɦɟɧɟɟ ɡɧɚɱɢɦɨɝɨ ɧɟɣɪɨɧɚ, ɬɨ ɫɥɟɞɭɟɬ ɜɵɱɢɫɥɹɬɶ ɩɨɤɚɡɚɬɟɥɶ ɡɧɚɱɢɦɨɫɬɢ ɜɵɯɨɞɧɨɝɨ ɫɢɝɧɚɥɚ ɧɟɣɪɨɧɚ. Ⱥɧɚɥɨɝɢɱɧɨ, ɜ ɡɚɞɚɱɟ ɨɩɪɟɞɟɥɟɧɢɹ ɧɚɢɦɟɧɟɟ ɡɧɚɱɢɦɨɝɨ ɜɯɨɞɧɨɝɨ ɫɢɝɧɚɥɚ ɧɭɠɧɨ ɜɵɱɢɫɥɹɬɶ ɡɧɚɱɢɦɨɫɬɶ ɷɬɨɝɨ ɫɢɝɧɚɥɚ, ɚ ɧɟ ɫɭɦɦɭ ɡɧɚɱɢɦɨɫɬɟɣ ɜɟɫɨɜ ɫɜɹɡɟɣ, ɧɚ ɤɨɬɨɪɵɟ ɷɬɨɬ ɫɢɝɧɚɥ ɩɨɞɚɟɬɫɹ.
ɍɫɪɟɞɧɟɧɢɟ ɩɨ ɨɛɭɱɚɸɳɟɦɭ ɦɧɨɠɟɫɬɜɭ ɉɨɤɚɡɚɬɟɥɶ ɡɧɚɱɢɦɨɫɬɢ ɩɚɪɚɦɟɬɪɚ
c qp ɡɚɜɢɫɢɬ ɨɬ ɬɨɱɤɢ ɜ ɩɪɨɫɬɪɚɧɫɬɜɟ ɩɚɪɚɦɟɬɪɨɜ, ɜ ɤɨɬɨɪɨɣ
ɨɧ ɜɵɱɢɫɥɟɧ ɢ ɨɬ ɩɪɢɦɟɪɚ ɢɡ ɨɛɭɱɚɸɳɟɝɨ ɦɧɨɠɟɫɬɜɚ. ɋɭɳɟɫɬɜɭɟɬ ɞɜɚ ɩɪɢɧɰɢɩɢɚɥɶɧɨ ɪɚɡɧɵɯ ɩɨɞɯɨɞɚ ɞɥɹ ɩɨɥɭɱɟɧɢɹ ɩɨɤɚɡɚɬɟɥɹ ɡɧɚɱɢɦɨɫɬɢ ɩɚɪɚɦɟɬɪɚ, ɧɟ ɡɚɜɢɫɹɳɟɝɨ ɨɬ ɩɪɢɦɟɪɚ. ɉɪɢ ɩɟɪɜɨɦ ɩɨɞɯɨɞɟ ɫɱɢɬɚɟɬɫɹ, ɱɬɨ ɜ ɨɛɭɱɚɸɳɟɣ ɜɵɛɨɪɤɟ ɡɚɤɥɸɱɟɧɚ ɩɨɥɧɚɹ ɢɧɮɨɪɦɚɰɢɹ ɨ ɜɫɟɯ ɜɨɡɦɨɠɧɵɯ ɩɪɢɦɟɪɚɯ. ȼ ɷɬɨɦ ɫɥɭɱɚɟ, ɩɨɞ ɩɨɤɚɡɚɬɟɥɟɦ ɡɧɚɱɢɦɨɫɬɢ ɩɨɧɢɦɚɸɬ ɜɟɥɢɱɢɧɭ, ɤɨɬɨɪɚɹ ɩɨɤɚɡɵɜɚɟɬ ɧɚɫɤɨɥɶɤɨ ɢɡɦɟɧɢɬɫɹ ɡɧɚɱɟɧɢɟ ɮɭɧɤɰɢɢ ɨɰɟɧɤɢ ɩɨ ɨɛɭɱɚɸɳɟɦɭ ɦɧɨɠɟɫɬɜɭ, ɟɫɥɢ ɬɟɤɭɳɟɟ ɡɧɚɱɟɧɢɟ ɩɚɪɚɦɟɬɪɚ w p ɡɚɦɟɧɢɬɶ ɧɚ ɜɵɞɟɥɟɧɧɨɟ ɡɧɚɱɟɧɢɟ
w*p . ɗɬɚ ɜɟɥɢɱɢɧɚ ɜɵɱɢɫɥɹɟɬɫɹ ɩɨ ɫɥɟɞɭɸɳɟɣ ɮɨɪɦɭɥɟ: cp =
¶HɈɆ w p - w*p . ¶w p
(
)
(2)
ȼ ɪɚɦɤɚɯ ɞɪɭɝɨɝɨ ɩɨɞɯɨɞɚ ɨɛɭɱɚɸɳɟɟ ɦɧɨɠɟɫɬɜɨ ɪɚɫɫɦɚɬɪɢɜɚɸɬ ɤɚɤ ɫɥɭɱɚɣɧɭɸ ɜɵɛɨɪɤɭ ɜ ɩɪɨɫɬɪɚɧɫɬɜɟ ɜɯɨɞɧɵɯ ɩɚɪɚɦɟɬɪɨɜ. ȼ ɷɬɨɦ ɫɥɭɱɚɟ ɩɨɤɚɡɚɬɟɥɟɦ ɡɧɚɱɢɦɨɫɬɢ ɩɨ ɜɫɟɦɭ ɨɛɭɱɚɸɳɟɦɭ ɦɧɨɠɟɫɬɜɭ ɛɭɞɟɬ ɫɥɭɠɢɬɶ ɪɟɡɭɥɶɬɚɬ ɧɟɤɨɬɨɪɨɝɨ ɭɫɪɟɞɧɟɧɢɹ ɩɨ ɨɛɭɱɚɸɳɟɣ ɜɵɛɨɪɤɟ. ɋɭɳɟɫɬɜɭɟɬ ɦɧɨɠɟɫɬɜɨ ɫɩɨɫɨɛɨɜ ɭɫɪɟɞɧɟɧɢɹ. Ɋɚɫɫɦɨɬɪɢɦ ɞɜɚ ɢɡ ɧɢɯ. ȿɫɥɢ ɜ ɪɟɡɭɥɶɬɚɬɟ ɭɫɪɟɞɧɟɧɢɹ ɩɨɤɚɡɚɬɟɥɶ ɡɧɚɱɢɦɨɫɬɢ ɞɨɥɠɟɧ ɞɚɜɚɬɶ ɫɪɟɞɧɸɸ ɡɧɚɱɢɦɨɫɬɶ, ɬɨ ɬɚɤɨɣ ɩɨɤɚɡɚɬɟɥɶ ɜɵɱɢɫɥɹɟɬɫɹ ɩɨ ɫɥɟɞɭɸɳɟɣ ɮɨɪɦɭɥɟ: 1 m q (3) cp = c . m q =1 p ȿɫɥɢ ɜ ɪɟɡɭɥɶɬɚɬɟ ɭɫɪɟɞɧɟɧɢɹ ɩɨɤɚɡɚɬɟɥɶ ɡɧɚɱɢɦɨɫɬɢ ɞɨɥɠɟɧ ɞɚɜɚɬɶ ɜɟɥɢɱɢɧɭ, ɤɨɬɨɪɭɸ ɧɟ ɩɪɟɜɨɫɯɨɞɹɬ ɩɨɤɚɡɚɬɟɥɢ ɡɧɚɱɢɦɨɫɬɢ ɩɨ ɨɬɞɟɥɶɧɵɦ ɩɪɢɦɟɪɚɦ (ɡɧɚɱɢɦɨɫɬɶ ɷɬɨɝɨ ɩɚɪɚɦɟɬɪɚ ɩɨ ɨɬɞɟɥɶɧɨɦɭ ɩɪɢɦɟɪɭ ɧɟ ɛɨɥɶɲɟ ɱɟɦ c p ), ɬɨ ɬɚɤɨɣ ɩɨɤɚɡɚɬɟɥɶ ɜɵɱɢɫɥɹɟɬɫɹ ɩɨ ɫɥɟɞɭɸɳɟɣ ɮɨɪɦɭɥɟ:
å
c p = max c pq .
(4) q ɉɨɤɚɡɚɬɟɥɶ ɡɧɚɱɢɦɨɫɬɢ (4) ɯɨɪɨɲɨ ɡɚɪɟɤɨɦɟɧɞɨɜɚɥ ɫɟɛɹ ɩɪɢ ɢɫɩɨɥɶɡɨɜɚɧɢɢ ɜ ɪɚɛɨɬɚɯ ɝɪɭɩɩɵ ɇɟɣɪɨɄɨɦɩ.
ɇɚɤɨɩɥɟɧɢɟ ɩɨɤɚɡɚɬɟɥɟɣ ɡɧɚɱɢɦɨɫɬɢ ȼɫɟ ɩɨɤɚɡɚɬɟɥɢ ɡɧɚɱɢɦɨɫɬɢ ɡɚɜɢɫɹɬ ɨɬ ɬɨɱɤɢ ɜ ɩɪɨɫɬɪɚɧɫɬɜɟ ɩɚɪɚɦɟɬɪɨɜ ɫɟɬɢ, ɜ ɤɨɬɨɪɨɣ ɨɧɢ ɜɵɱɢɫɥɟɧɵ, ɢ ɦɨɝɭɬ ɫɢɥɶɧɨ ɢɡɦɟɧɹɬɶɫɹ ɩɪɢ ɩɟɪɟɯɨɞɟ ɨɬ ɨɞɧɨɣ ɬɨɱɤɢ ɤ ɞɪɭɝɨɣ. Ⱦɥɹ ɩɨɤɚɡɚɬɟɥɟɣ ɡɧɚɱɢɦɨɫɬɢ,
CHAP9.DOC
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ɜɵɱɢɫɥɟɧɧɵɯ ɫ ɢɫɩɨɥɶɡɨɜɚɧɢɟɦ ɝɪɚɞɢɟɧɬɚ ɷɬɚ ɡɚɜɢɫɢɦɨɫɬɶ ɟɳɟ ɫɢɥɶɧɟɟ, ɩɨɫɤɨɥɶɤɭ ɩɪɢ ɨɛɭɱɟɧɢɢ ɩɨ ɦɟɬɨɞɭ ɧɚɢɫɤɨɪɟɣɲɟɝɨ ɫɩɭɫɤɚ (ɫɦ. ɪɚɡɞɟɥ «Ɇɟɬɨɞ ɧɚɢɫɤɨɪɟɣɲɟɝɨ ɫɩɭɫɤɚ») ɜ ɞɜɭɯ ɫɨɫɟɞɧɢɯ ɬɨɱɤɚɯ ɩɪɨɫɬɪɚɧɫɬɜɚ ɩɚɪɚɦɟɬɪɨɜ, ɜ ɤɨɬɨɪɵɯ ɜɵɱɢɫɥɹɥɫɹ ɝɪɚɞɢɟɧɬ, ɝɪɚɞɢɟɧɬɵ ɨɪɬɨɝɨɧɚɥɶɧɵ. Ⱦɥɹ ɫɧɹɬɢɹ ɡɚɜɢɫɢɦɨɫɬɢ ɨɬ ɬɨɱɤɢ ɩɪɨɫɬɪɚɧɫɬɜɚ ɢɫɩɨɥɶɡɭɸɬɫɹ ɩɨɤɚɡɚɬɟɥɢ ɡɧɚɱɢɦɨɫɬɢ, ɜɵɱɢɫɥɟɧɧɵɟ ɜ ɧɟɫɤɨɥɶɤɢɯ ɬɨɱɤɚɯ. Ⱦɚɥɟɟ ɨɧɢ ɭɫɪɟɞɧɹɸɬɫɹ ɩɨ ɮɨɪɦɭɥɚɦ ɚɧɚɥɨɝɢɱɧɵɦ (3) ɢ (4). ȼɨɩɪɨɫ ɨ ɜɵɛɨɪɟ ɬɨɱɟɤ ɜ ɩɪɨɫɬɪɚɧɫɬɜɟ ɩɚɪɚɦɟɬɪɨɜ ɜ ɤɨɬɨɪɵɯ ɜɵɱɢɫɥɹɬɶ ɩɨɤɚɡɚɬɟɥɢ ɡɧɚɱɢɦɨɫɬɢ ɨɛɵɱɧɨ ɪɟɲɚɟɬɫɹ ɩɪɨɫɬɨ. ȼ ɯɨɞɟ ɧɟɫɤɨɥɶɤɢɯ ɲɚɝɨɜ ɨɛɭɱɟɧɢɹ ɩɨ ɥɸɛɨɦɭ ɢɡ ɝɪɚɞɢɟɧɬɧɵɯ ɦɟɬɨɞɨɜ ɩɪɢ ɤɚɠɞɨɦ ɜɵɱɢɫɥɟɧɢɢ ɝɪɚɞɢɟɧɬɚ ɜɵɱɢɫɥɹɸɬɫɹ ɢ ɩɨɤɚɡɚɬɟɥɢ ɡɧɚɱɢɦɨɫɬɢ. ɑɢɫɥɨ ɲɚɝɨɜ ɨɛɭɱɟɧɢɹ, ɜ ɯɨɞɟ ɤɨɬɨɪɵɯ ɧɚɤɚɩɥɢɜɚɸɬɫɹ ɩɨɤɚɡɚɬɟɥɢ ɡɧɚɱɢɦɨɫɬɢ, ɞɨɥɠɧɨ ɛɵɬɶ ɧɟ ɫɥɢɲɤɨɦ ɛɨɥɶɲɢɦ, ɩɨɫɤɨɥɶɤɭ ɩɪɢ ɛɨɥɶɲɨɦ ɱɢɫɥɟ ɲɚɝɨɜ ɨɛɭɱɟɧɢɹ ɩɟɪɜɵɟ ɜɵɱɢɫɥɟɧɧɵɟ ɩɨɤɚɡɚɬɟɥɢ ɡɧɚɱɢɦɨɫɬɢ ɬɟɪɹɸɬ ɫɦɵɫɥ, ɨɫɨɛɟɧɧɨ ɩɪɢ ɢɫɩɨɥɶɡɨɜɚɧɢɢ ɭɫɪɟɞɧɟɧɢɹ ɩɨ ɮɨɪɦɭɥɟ (4).
ɋɬɚɧɞɚɪɬ ɩɟɪɜɨɝɨ ɭɪɨɜɧɹ ɤɨɦɩɨɧɟɧɬɚ ɤɨɧɬɪɚɫɬɟɪ ȼ ɷɬɨɦ ɪɚɡɞɟɥɟ ɩɪɢɜɨɞɢɬɫɹ ɫɬɚɧɞɚɪɬ ɹɡɵɤɚ ɨɩɢɫɚɧɢɹ ɤɨɦɩɨɧɟɧɬɚ ɤɨɧɬɪɚɫɬɟɪ. Ʉɨɦɩɨɧɟɧɬ ɤɨɧɬɪɚɫɬɟɪ ɜɨ ɦɧɨɝɨɦ ɩɨɞɨɛɟɧ ɤɨɦɩɨɧɟɧɬɭ ɭɱɢɬɟɥɶ. Ɍɚɤ ɜ ɹɡɵɤɟ ɨɩɢɫɚɧɢɹ ɤɨɦɩɨɧɟɧɬɚ ɤɨɧɬɪɚɫɬɟɪ ɞɨɩɭɫɤɚɟɬɫɹ ɢɫɩɨɥɶɡɨɜɚɧɢɟ ɮɭɧɤɰɢɣ, ɨɩɢɫɚɧɧɵɯ ɜ ɪɚɡɞɟɥɟ «ɋɩɢɫɨɤ ɫɬɚɧɞɚɪɬɧɵɯ ɮɭɧɤɰɢɣ» ɝɥɚɜɵ «ɍɱɢɬɟɥɶ».
əɡɵɤ ɨɩɢɫɚɧɢɹ ɤɨɧɬɪɚɫɬɟɪɚ ȼ ɨɬɥɢɱɢɟ ɨɬ ɬɚɤɢɯ ɤɨɦɩɨɧɟɧɬ ɤɚɤ ɨɰɟɧɤɚ, ɫɟɬɶ ɢ ɢɧɬɟɪɩɪɟɬɚɬɨɪ ɨɬɜɟɬɚ, ɤɨɧɬɪɚɫɬɟɪ ɧɟ ɹɜɥɹɟɬɫɹ ɫɨɫɬɚɜɧɵɦ ɨɛɴɟɤɬɨɦ. Ɉɞɧɚɤɨ, ɤɨɧɬɪɚɫɬɟɪ ɦɨɠɟɬ ɫɨɫɬɨɹɬɶ ɢɡ ɦɧɨɠɟɫɬɜɚ ɮɭɧɤɰɢɣ, ɜɵɡɵɜɚɸɳɢɯ ɞɪɭɝ ɞɪɭɝɚ. ɋɨɛɫɬɜɟɧɧɨ ɤɨɧɬɪɚɫɬɟɪ – ɷɬɨ ɩɪɨɰɟɞɭɪɚ, ɭɩɪɚɜɥɹɸɳɚɹ ɩɪɨɰɟɫɫɨɦ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɹ. Ʉɥɸɱɟɜɵɟ ɫɥɨɜɚ, ɫɩɟɰɢɮɢɱɟɫɤɢɟ ɞɥɹ ɹɡɵɤɚ ɨɩɢɫɚɧɢɹ ɤɨɧɬɪɚɫɬɟɪɚ ɩɪɢɜɟɞɟɧɵ ɜ ɬɚɛɥ. 3
Ȼɢɛɥɢɨɬɟɤɢ ɮɭɧɤɰɢɣ ɤɨɧɬɪɚɫɬɟɪɚ
Ɍɚɛɥɢɰɚ 3. Ʉɥɸɱɟɜɵɟ ɫɥɨɜɚ ɫɩɟɰɢɮɢɱɟɫɤɢɟ ɞɥɹ ɹɡɵɤɚ ɨɩɢɫɚɧɢɹ ɤɨɧɬɪɚɫɬɟɪɚ Ʉɥɸɱɟɜɨɟ ɫɥɨɜɨɄɪɚɬɤɨɟ ɨɩɢɫɚɧɢɟ 1. Main ɇɚɱɚɥɨ ɝɥɚɜɧɨɣ ɩɪɨɰɟɞɭɪɵ 2. Contrast Ɂɚɝɨɥɨɜɨɤ ɨɩɢɫɚɧɢɹ ɤɨɧɬɪɚɫɬɟɪɚ 3. ContrLib Ɂɚɝɨɥɨɜɨɤ ɨɩɢɫɚɧɢɹ ɛɢɛɥɢɨɬɟɤɢ ɮɭɧɤɰɢɣ 4. Used ɉɨɞɤɥɸɱɟɧɢɟ ɛɢɛɥɢɨɬɟɤ ɮɭɧɤɰɢɣ 5. ContrastFunc Ƚɥɨɛɚɥɶɧɚɹ ɩɟɪɟɦɟɧɧɚɹ ɬɢɩɚ ɮɭɧɤɰɢɹ.
Ȼɢɛɥɢɨɬɟɤɢ ɮɭɧɤɰɢɣ ɤɨɧɬɪɚɫɬɟɪɚ ɫɨɞɟɪɠɚɬ ɨɩɢɫɚɧɢɟ ɮɭɧɤɰɢɣ, ɧɟɨɛɯɨɞɢɦɵɯ ɞɥɹ ɪɚɛɨɬɵ ɨɞɧɨɝɨ ɢɥɢ ɧɟɫɤɨɥɶɤɢɯ ɤɨɧɬɪɚɫɬɟɪɨɜ. ɂɫɩɨɥɶɡɨɜɚɧɢɟ ɛɢɛɥɢɨɬɟɤ ɩɨɡɜɨɥɹɟɬ ɢɡɛɟɠɚɬɶ ɞɭɛɥɢɪɨɜɚɧɢɹ ɮɭɧɤɰɢɣ ɜ ɪɚɡɥɢɱɧɵɯ ɤɨɧɬɪɚɫɬɟɪɚɯ. Ɉɩɢɫɚɧɢɟ ɛɢɛɥɢɨɬɟɤɢ ɮɭɧɤɰɢɣ ɚɧɚɥɨɝɢɱɧɨ ɨɩɢɫɚɧɢɸ ɤɨɧɬɪɚɫɬɟɪɚ, ɧɨ ɧɟ ɫɨɞɟɪɠɢɬ ɝɥɚɜɧɨɣ ɩɪɨɰɟɞɭɪɵ.
ȻɇɎ ɹɡɵɤɚ ɨɩɢɫɚɧɢɹ ɤɨɧɬɪɚɫɬɟɪɚ Ɉɛɨɡɧɚɱɟɧɢɹ, ɩɪɢɧɹɬɵɟ ɜ ɞɚɧɧɨɦ ɪɚɫɲɢɪɟɧɢɢ ȻɇɎ ɢ ɨɩɢɫɚɧɢɟ ɪɹɞɚ ɤɨɧɫɬɪɭɤɰɢɣ ɩɪɢɜɟɞɟɧɵ ɜ ɝɥɚɜɟ «Ɉɛɳɢɣ ɫɬɚɧɞɚɪɬ» ɜ ɪɚɡɞɟɥɟ «Ɉɩɢɫɚɧɢɟ ɹɡɵɤɚ ɨɩɢɫɚɧɢɹ ɤɨɦɩɨɧɟɧɬ». <Ɉɩɢɫɚɧɢɟ ɛɢɛɥɢɨɬɟɤɢ> ::= <Ɂɚɝɨɥɨɜɨɤ ɛɢɛɥɢɨɬɟɤɢ> <Ɉɩɢɫɚɧɢɟ ɝɥɨɛɚɥɶɧɵɯ ɩɟɪɟɦɟɧɧɵɯ> <Ɉɩɢɫɚɧɢɟ ɮɭɧɤɰɢɣ> <Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɛɢɛɥɢɨɬɟɤɢ> <Ɂɚɝɨɥɨɜɨɤ ɛɢɛɥɢɨɬɟɤɢ> ::= ContrLib <ɂɦɹ ɛɢɛɥɢɨɬɟɤɢ> [Used <ɋɩɢɫɨɤ ɢɦɟɧ ɛɢɛɥɢɨɬɟɤ>] <ɂɦɹ ɛɢɛɥɢɨɬɟɤɢ> ::= <ɂɞɟɧɬɢɮɢɤɚɬɨɪ> <ɋɩɢɫɨɤ ɢɦɟɧ ɛɢɛɥɢɨɬɟɤ> ::= <ɂɦɹ ɢɫɩɨɥɶɡɭɟɦɨɣ ɛɢɛɥɢɨɬɟɤɢ> [,<ɋɩɢɫɨɤ ɢɦɟɧ ɛɢɛɥɢɨɬɟɤ>] <ɂɦɹ ɢɫɩɨɥɶɡɭɟɦɨɣ ɛɢɛɥɢɨɬɟɤɢ> ::= <ɂɞɟɧɬɢɮɢɤɚɬɨɪ> <Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɛɢɛɥɢɨɬɟɤɢ> ::= End ContrLib <Ɉɩɢɫɚɧɢɟ ɤɨɧɬɪɚɫɬɟɪɚ> ::= <Ɂɚɝɨɥɨɜɨɤ ɤɨɧɬɪɚɫɬɟɪɚ> <Ɉɩɢɫɚɧɢɟ ɝɥɨɛɚɥɶɧɵɯ ɩɟɪɟɦɟɧɧɵɯ> <Ɉɩɢɫɚɧɢɟ ɮɭɧɤɰɢɣ> <Ƚɥɚɜɧɚɹ ɩɪɨɰɟɞɭɪɚ> <Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɤɨɧɬɪɚɫɬɟɪɚ> <Ɂɚɝɨɥɨɜɨɤ ɤɨɧɬɪɚɫɬɟɪɚ> ::= Contrast <ɂɦɹ ɛɢɛɥɢɨɬɟɤɢ> [Used <ɋɩɢɫɨɤ ɢɦɟɧ ɛɢɛɥɢɨɬɟɤ>] <Ƚɥɚɜɧɚɹ ɩɪɨɰɟɞɭɪɚ> ::= Main <Ɉɩɢɫɚɧɢɟ ɫɬɚɬɢɱɟɫɤɢɯ ɩɟɪɟɦɟɧɧɵɯ> <Ɉɩɢɫɚɧɢɟ ɩɟɪɟɦɟɧɧɵɯ> <Ɍɟɥɨ ɮɭɧɤɰɢɢ> <Ʉɨɧɟɰ ɨɩɢɫɚɧɢɹ ɤɨɧɬɪɚɫɬɟɪɚ> End Contrast
Ɉɩɢɫɚɧɢɟ ɹɡɵɤɚ ɨɩɢɫɚɧɢɹ ɤɨɧɬɪɚɫɬɟɪɚ əɡɵɤ ɨɩɢɫɚɧɢɹ ɤɨɧɬɪɚɫɬɟɪɚ ɹɜɥɹɟɬɫɹ ɧɚɢɛɨɥɟɟ ɩɪɨɫɬɵɦ ɢɡ ɜɫɟɯ ɹɡɵɤɨɜ ɨɩɢɫɚɧɢɹ ɤɨɦɩɨɧɟɧɬ. Ɏɚɤɬɢɱɟɫɤɢ ɜɫɟ ɫɢɧɬɚɤɫɢɱɟɫɤɢɟ ɤɨɧɫɬɪɭɤɰɢɢ ɷɬɨɝɨ ɹɡɵɤɚ ɨɩɢɫɚɧɵ ɜ ɝɥɚɜɟ «Ɉɛɳɢɣ ɫɬɚɧɞɚɪɬ». ȼ ɬɟɥɟ ɮɭɧɤɰɢɢ, ɹɜɥɹɸɳɟɦɫɹ ɱɚɫɬɶɸ ɝɥɚɜɧɨɣ ɩɪɨɰɟɞɭɪɵ ɧɟɞɨɩɭɫɬɢɦ ɨɩɟɪɚɬɨɪ ɜɨɡɜɪɚɬɚ ɡɧɚɱɟɧɢɹ, ɩɨɫɤɨɥɶɤɭ ɝɥɚɜɧɚɹ ɩɪɨɰɟɞɭɪɚ ɧɟ ɹɜɥɹɟɬɫɹ ɮɭɧɤɰɢɟɣ. Ʉɨɧɬɪɚɫɬɟɪ ɢɦɟɟɬ ɨɞɧɭ ɝɥɨɛɚɥɶɧɭɸ ɩɪɟɞɨɩɪɟɞɟɥɟɧɧɭɸ ɩɟɪɟɦɟɧɧɭɸ ContrastFunc. ɗɬɚ ɩɟɪɟɦɟɧɧɚɹ ɞɨɥɠɧɚ ɨɛɹɡɚɬɟɥɶɧɨ ɛɵɬɶ ɨɩɪɟɞɟɥɟɧɚ – ɟɣ ɧɭɠɧɨ ɩɪɢɫɜɨɢɬɶ ɚɞɪɟɫ ɮɭɧɤɰɢɢ, ɤɨɬɨɪɚɹ ɛɭɞɟɬ ɜɵɡɵɜɚɬɶ-
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ɫɹ ɤɚɠɞɵɣ ɪɚɡ ɩɨɫɥɟ ɬɨɝɨ, ɤɚɤ ɧɟɣɪɨɧɧɚɹ ɫɟɬɶ ɜɵɱɢɫɥɢɬ ɝɪɚɞɢɟɧɬ ɩɨɫɥɟ ɪɟɲɟɧɢɹ ɨɞɧɨɝɨ ɩɪɢɦɟɪɚ. Ɏɭɧɤɰɢɹ, ɚɞɪɟɫ ɤɨɬɨɪɨɣ ɩɪɢɫɜɚɢɜɚɟɬɫɹ ɩɟɪɟɦɟɧɧɨɣ ContrastFunc ɞɨɥɠɧɚ ɛɵɬɶ ɨɛɴɹɜɥɟɧɚ ɫɥɟɞɭɸɳɢɦ ɨɛɪɚɡɨɦ: Function MyContrast( TheEnd : Logic ) : Logic; Ɂɧɚɱɟɧɢɹ ɚɪɝɭɦɟɧɬɚ TheEnd ɢɦɟɸɬ ɫɥɟɞɭɸɳɢɣ ɫɦɵɫɥ: ɢɫɬɢɧɚ – ɨɛɭɱɟɧɢɟ ɜɟɞɟɬɫɹ ɩɨɡɚɞɚɱɧɨ ɢɥɢ ɡɚɤɨɧɱɟɧ ɩɪɨɫɦɨɬɪ ɨɛɭɱɚɸɳɟɝɨ ɦɧɨɠɟɫɬɜɚ; ɥɨɠɶ – ɨɛɪɚɛɨɬɚɧ ɟɳɟ ɨɞɢɧ ɩɪɢɦɟɪ ɨɛɭɱɚɸɳɟɝɨ ɦɧɨɠɟɫɬɜɚ ɩɪɢ ɨɛɭɱɟɧɢɢ ɩɨ ɜɫɟɦɭ ɡɚɞɚɱɧɢɤɭ ɜ ɰɟɥɨɦ. ɋɥɟɞɭɟɬ ɭɱɟɫɬɶ, ɱɬɨ ɩɪɢ ɨɛɭɱɟɧɢɢ ɩɨ ɜɫɟɦɭ ɨɛɭɱɚɸɳɟɦɭ ɦɧɨɠɟɫɬɜɭ ɜ ɰɟɥɨɦ, ɧɟɣɪɨɧɧɚɹ ɫɟɬɶ ɧɚɤɚɩɥɢɜɚɟɬ ɝɪɚɞɢɟɧɬɵ ɜɫɟɯ ɩɪɢɦɟɪɨɜ, ɬɚɤ ɱɬɨ ɩɪɢ ɩɟɪɜɨɦ ɜɵɡɨɜɟ ɮɭɧɤɰɢɢ ɜ ɫɟɬɢ ɯɪɚɧɢɬɫɹ ɝɪɚɞɢɟɧɬ ɮɭɧɤɰɢɢ ɨɰɟɧɤɢ ɩɨ ɪɟɡɭɥɶɬɚɬɚɦ ɪɟɲɟɧɢɹ ɩɟɪɜɨɝɨ ɩɪɢɦɟɪɚ; ɩɪɢ ɜɬɨɪɨɦ – ɪɟɡɭɥɶɬɚɬɚɦ ɪɟɲɟɧɢɹ ɩɟɪɜɵɯ ɞɜɭɯ ɩɪɢɦɟɪɨɜ ɢ ɬ.ɞ. Ɏɭɧɤɰɢɹ ɜɨɡɜɪɚɳɚɟɬ ɡɧɚɱɟɧɢɟ ɥɨɠɶ, ɟɫɥɢ ɜ ɯɨɞɟ ɟɟ ɪɚɛɨɬɵ ɩɪɨɢɡɨɲɥɚ ɨɲɢɛɤɚ. ȼ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ ɨɧɚ ɜɨɡɜɪɚɳɚɟɬ ɡɧɚɱɟɧɢɟ ɢɫɬɢɧɚ. Ɂɧɚɱɟɧɢɟ ɩɟɪɟɦɟɧɧɨɣ ContrastFunc ɩɪɢɫɜɚɢɜɚɟɬɫɹ ɨɩɟɪɚɬɨɪɨɦ ɩɪɢɫɜɚɢɜɚɧɢɹ: ContrastFunc = MyContrast ȿɫɥɢ ɡɧɚɱɟɧɢɟ ɩɟɪɟɦɟɧɧɨɣ ContrastFunc ɧɟ ɡɚɞɚɧɨ, ɬɨ ɨɧɚ ɭɤɚɡɵɜɚɟɬ ɧɚ ɢɫɩɨɥɶɡɭɟɦɭɸ ɩɨ ɭɦɨɥɱɚɧɢɸ ɮɭɧɤɰɢɸ EmptyContrast, ɤɨɬɨɪɚɹ ɩɪɨɫɬɨ ɜɨɡɜɪɚɳɚɟɬ ɡɧɚɱɟɧɢɟ ɢɫɬɢɧɚ.
ɋɬɚɧɞɚɪɬ ɜɬɨɪɨɝɨ ɭɪɨɜɧɹ ɤɨɦɩɨɧɟɧɬɚ ɤɨɧɬɪɚɫɬɟɪ Ʉɨɦɩɨɧɟɧɬɚ ɤɨɧɬɪɚɫɬɟɪ ɨɞɧɨɜɪɟɦɟɧɧɨ ɪɚɛɨɬɚɟɬ ɬɨɥɶɤɨ ɫ ɨɞɧɢɦ ɤɨɧɬɪɚɫɬɟɪɨɦ. Ɂɚɩɪɨɫɵ ɤ ɤɨɦɩɨɧɟɧɬɟ ɤɨɧɬɪɚɫɬɟɪ ɦɨɠɧɨ ɪɚɡɛɢɬɶ ɧɚ ɫɥɟɞɭɸɳɢɟ ɝɪɭɩɩɵ. 1. Ʉɨɧɬɪɚɫɬɢɪɨɜɚɧɢɟ ɫɟɬɢ. 2. ɑɬɟɧɢɟ/ɡɚɩɢɫɶ ɤɨɧɬɪɚɫɬɟɪɚ. 3. ɂɧɢɰɢɚɰɢɹ ɪɟɞɚɤɬɨɪɚ ɤɨɧɬɪɚɫɬɟɪɚ. 4. Ɋɚɛɨɬɚ ɫ ɩɚɪɚɦɟɬɪɚɦɢ ɤɨɧɬɪɚɫɬɟɪɚ.
Ʉɨɧɬɪɚɫɬɢɪɨɜɚɧɢɟ ɫɟɬɢ Ʉ ɞɚɧɧɨɣ ɝɪɭɩɩɟ ɨɬɧɨɫɹɬɫɹ ɬɪɢ ɡɚɩɪɨɫɚ – ɤɨɧɬɪɚɫɬɢɪɨɜɚɬɶ ɫɟɬɶ (ContrastNet), ɩɪɟɪɜɚɬɶ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɟ (CloseContrast) ɢ ɤɨɧɬɪɚɫɬɢɪɨɜɚɬɶ ɩɪɢɦɟɪ (ContrastExample).
Ʉɨɧɬɪɚɫɬɢɪɨɜɚɬɶ ɫɟɬɶ(ContrastNet) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function ContrastNet: Logic; C: Logic ContrastNet() Ⱥɪɝɭɦɟɧɬɨɜ ɧɟɬ.
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ɇɚɡɧɚɱɟɧɢɟ – ɩɪɨɢɡɜɨɞɢɬ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɟ ɫɟɬɢ. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. ȿɫɥɢ Error <> 0, ɬɨ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. ȿɫɥɢ ɜ ɦɨɦɟɧɬ ɩɨɥɭɱɟɧɢɹ ɡɚɩɪɨɫɚ ɤɨɧɬɪɚɫɬɟɪ ɧɟ ɡɚɝɪɭɠɟɧ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 701 – ɧɟɜɟɪɧɨɟ ɢɦɹ ɤɨɦɩɨɧɟɧɬɚ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. ȼɵɩɨɥɧɹɟɬɫɹ ɝɥɚɜɧɚɹ ɩɪɨɰɟɞɭɪɚ ɡɚɝɪɭɠɟɧɧɨɝɨ ɤɨɧɬɪɚɫɬɟɪɚ. ȿɫɥɢ ɜɨ ɜɪɟɦɹ ɜɵɩɨɥɧɟɧɢɹ ɡɚɩɪɨɫɚ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ, ɚ ɡɧɚɱɟɧɢɟ ɩɟɪɟɦɟɧɧɨɣ Error ɪɚɜɧɨ ɧɭɥɸ, ɬɨ ɝɟɧɟɪɢɪɭɟɬɫɹ ɜɧɭɬɪɟɧɧɹɹ ɨɲɢɛɤɚ 705 – ɨɲɢɛɤɚ ɢɫɩɨɥɧɟɧɢɹ ɤɨɧɬɪɚɫɬɟɪɚ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. ȿɫɥɢ ɜɨ ɜɪɟɦɹ ɜɵɩɨɥɧɟɧɢɹ ɡɚɩɪɨɫɚ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ, ɚ ɡɧɚɱɟɧɢɟ ɩɟɪɟɦɟɧɧɨɣ Error ɧɟ ɪɚɜɧɨ ɧɭɥɸ, ɬɨ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ.
ɉɪɟɪɜɚɬɶ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɟ (CloseContrast) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function CloseContrast: Logic; C: Logic CloseContrast() Ⱥɪɝɭɦɟɧɬɨɜ ɧɟɬ. ɇɚɡɧɚɱɟɧɢɟ – ɩɪɟɪɵɜɚɟɬ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɟ ɫɟɬɢ. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ.
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4. 5.
6.
ȿɫɥɢ Error <> 0, ɬɨ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. ȿɫɥɢ ɜ ɦɨɦɟɧɬ ɩɨɥɭɱɟɧɢɹ ɡɚɩɪɨɫɚ ɤɨɧɬɪɚɫɬɟɪ ɧɟ ɡɚɝɪɭɠɟɧ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 701 – ɧɟɜɟɪɧɨɟ ɢɦɹ ɤɨɦɩɨɧɟɧɬɚ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. ȿɫɥɢ ɜ ɦɨɦɟɧɬ ɩɨɥɭɱɟɧɢɹ ɡɚɩɪɨɫɚ ɧɟ ɜɵɩɨɥɧɹɟɬɫɹ ɡɚɩɪɨɫ ContrastNet, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 706 – ɧɟɜɟɪɧɨɟ ɢɫɩɨɥɶɡɨɜɚɧɢɟ ɡɚɩɪɨɫɚ ɧɚ ɩɪɟɪɵɜɚɧɢɟ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɹ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. Ɂɚɜɟɪɲɚɟɬɫɹ ɜɵɩɨɥɧɟɧɢɟ ɬɟɤɭɳɟɝɨ ɲɚɝɚ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɹ ɫɟɬɢ. ȿɫɥɢ ɜɨ ɜɪɟɦɹ ɜɵɩɨɥɧɟɧɢɹ ɡɚɩɪɨɫɚ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ, ɚ ɡɧɚɱɟɧɢɟ ɩɟɪɟɦɟɧɧɨɣ Error ɪɚɜɧɨ ɧɭɥɸ, ɬɨ ɝɟɧɟɪɢɪɭɟɬɫɹ ɜɧɭɬɪɟɧɧɹɹ ɨɲɢɛɤɚ 705 – ɨɲɢɛɤɚ ɢɫɩɨɥɧɟɧɢɹ ɤɨɧɬɪɚɫɬɟɪɚ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. ȿɫɥɢ ɜɨ ɜɪɟɦɹ ɜɵɩɨɥɧɟɧɢɹ ɡɚɩɪɨɫɚ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ, ɚ ɡɧɚɱɟɧɢɟ ɩɟɪɟɦɟɧɧɨɣ Error ɧɟ ɪɚɜɧɨ ɧɭɥɸ, ɬɨ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ.
Ʉɨɧɬɪɚɫɬɢɪɨɜɚɬɶ ɩɪɢɦɟɪ (ContrastExample) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function ContrastExample( TheEnd : Logic) : Logic; C: Logic ContrastExample(Logic TheEnd ) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɚ: TheEnd – ɡɧɚɱɟɧɢɟ ɚɪɝɭɦɟɧɬɚ ɢɦɟɟɬ ɫɥɟɞɭɸɳɢɣ ɫɦɵɫɥ: ɥɨɠɶ – ɨɛɪɚɛɨɬɚɧ ɟɳɟ ɨɞɢɧ ɩɪɢɦɟɪ ɨɛɭɱɚɸɳɟɝɨ ɦɧɨɠɟɫɬɜɚ ɩɪɢ ɨɛɭɱɟɧɢɢ ɩɨ ɜɫɟɦɭ ɡɚɞɚɱɧɢɤɭ ɜ ɰɟɥɨɦ. ɇɚɡɧɚɱɟɧɢɟ – ɢɡɜɥɟɤɚɟɬ ɢɡ ɫɟɬɢ ɧɟɨɛɯɨɞɢɦɵɟ ɞɥɹ ɜɵɱɢɫɥɟɧɢɹ ɩɨɤɚɡɚɬɟɥɟɣ ɡɧɚɱɢɦɨɫɬɢ ɩɚɪɚɦɟɬɪɵ. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. ȿɫɥɢ Error <> 0, ɬɨ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. ȼɵɡɵɜɚɟɬ ɮɭɧɤɰɢɸ, ɚɞɪɟɫ ɤɨɬɨɪɨɣ ɯɪɚɧɢɬɫɹ ɜ ɩɟɪɟɦɟɧɧɨɣ ContrastFunc, ɩɟɪɟɞɚɜɚɹ ɟɣ ɚɪɝɭɦɟɧɬ TheEnd ɜ ɤɚɱɟɫɬɜɟ ɚɪɝɭɦɟɧɬɚ. 3. ȿɫɥɢ ɮɭɧɤɰɢɹ ContrastFunc ɜɨɡɜɪɚɳɚɟɬ ɡɧɚɱɟɧɢɟ ɥɨɠɶ, ɚ ɡɧɚɱɟɧɢɟ ɩɟɪɟɦɟɧɧɨɣ Error ɪɚɜɧɨ ɧɭɥɸ, ɬɨ ɝɟɧɟɪɢɪɭɟɬɫɹ ɜɧɭɬɪɟɧɧɹɹ ɨɲɢɛɤɚ 705 – ɨɲɢɛɤɚ ɢɫɩɨɥɧɟɧɢɹ ɤɨɧɬɪɚɫɬɟɪɚ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 4. ȿɫɥɢ ɮɭɧɤɰɢɹ ContrastFunc ɜɨɡɜɪɚɳɚɟɬ ɡɧɚɱɟɧɢɟ ɥɨɠɶ, ɚ ɡɧɚɱɟɧɢɟ ɩɟɪɟɦɟɧɧɨɣ Error ɧɟ ɪɚɜɧɨ ɧɭɥɸ, ɬɨ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 5. Ɂɚɩɪɨɫ ɜ ɤɚɱɟɫɬɜɟ ɪɟɡɭɥɶɬɚɬɚ ɜɨɡɜɪɚɳɚɟɬ ɜɨɡɜɪɚɳɟɧɧɨɟ ɮɭɧɤɰɢɟɣ ContrastFunc ɡɧɚɱɟɧɢɟ. 1. 2.
ɑɬɟɧɢɟ/ɡɚɩɢɫɶ ɤɨɧɬɪɚɫɬɟɪɚ ȼ ɞɚɧɧɨɦ ɪɚɡɞɟɥɟ ɨɩɢɫɚɧɵ ɡɚɩɪɨɫɵ ɩɨɡɜɨɥɹɸɳɢɟ, ɡɚɝɪɭɡɢɬɶ ɤɨɧɬɪɚɫɬɟɪ ɫ ɞɢɫɤɚ ɢɥɢ ɢɡ ɩɚɦɹɬɢ, ɜɵɝɪɭɡɢɬɶ ɤɨɧɬɪɚɫɬɟɪɚ ɢ ɫɨɯɪɚɧɢɬɶ ɬɟɤɭɳɟɝɨ ɤɨɧɬɪɚɫɬɟɪɚ ɧɚ ɞɢɫɤɟ ɢɥɢ ɜ ɩɚɦɹɬɢ.
ɉɪɨɱɢɬɚɬɶ ɤɨɧɬɪɚɫɬɟɪɚ (cnAdd) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function cnAdd( CompName : PString ) : Logic; C: Logic cnAdd(PString CompName) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɚ: CompName – ɭɤɚɡɚɬɟɥɶ ɧɚ ɫɬɪɨɤɭ ɫɢɦɜɨɥɨɜ, ɫɨɞɟɪɠɚɳɭɸ ɢɦɹ ɮɚɣɥɚ ɤɨɦɩɨɧɟɧɬɚ ɢɥɢ ɚɞɪɟɫ ɨɩɢɫɚɧɢɹ ɤɨɦɩɨɧɟɧɬɚ. ɇɚɡɧɚɱɟɧɢɟ – ɱɢɬɚɟɬ ɤɨɧɬɪɚɫɬɟɪɚ ɫ ɞɢɫɤɚ ɢɥɢ ɢɡ ɩɚɦɹɬɢ.
1.
Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. ȿɫɥɢ ɜ ɤɚɱɟɫɬɜɟ ɚɪɝɭɦɟɧɬɚ CompName ɞɚɧɚ ɫɬɪɨɤɚ, ɩɟɪɜɵɟ ɱɟɬɵɪɟ ɫɢɦɜɨɥɚ ɤɨɬɨɪɨɣ ɫɨɫɬɚɜɥɹɸɬ ɫɥɨɜɨ File, ɬɨ ɨɫɬɚɥɶɧɚɹ ɱɚɫɬɶ ɫɬɪɨɤɢ ɫɨɞɟɪɠɢɬ ɢɦɹ ɤɨɦɩɨɧɟɧɬɚ ɢ ɩɨɫɥɟ ɩɪɨɛɟɥɚ ɢɦɹ ɮɚɣɥɚ, ɫɨɞɟɪɠɚɳɟɝɨ ɤɨɦɩɨɧɟɧɬ. ȼ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ ɫɱɢɬɚɟɬɫɹ, ɱɬɨ ɚɪɝɭɦɟɧɬ CompName ɫɨɞɟɪɠɢɬ ɭɤɚɡɚɬɟɥɶ ɧɚ ɨɛɥɚɫɬɶ ɩɚɦɹɬɢ, ɫɨɞɟɪɠɚɳɭɸ ɨɩɢɫɚɧɢɟ ɤɨɦɩɨɧɟɧɬɚ ɜ ɮɨɪɦɚɬɟ ɞɥɹ ɡɚɩɢɫɢ ɧɚ ɞɢɫɤ. ȿɫɥɢ ɨɩɢɫɚɧɢɟ ɧɟ ɜɦɟɳɚɟɬɫɹ ɜ ɨɞɧɭ ɨɛɥɚɫɬɶ ɩɚɦɹɬɢ, ɬɨ ɞɨɩɭɫɤɚɟɬɫɹ ɜɤɥɸɱɟɧɢɟ ɜ ɬɟɤɫɬ ɨɩɢɫɚɧɢɹ ɤɨɦ-
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2. 3.
ɩɨɧɟɧɬɚ ɤɥɸɱɟɜɨɝɨ ɫɥɨɜɚ Continue, ɡɚ ɤɨɬɨɪɵɦ ɫɥɟɞɭɟɬ ɱɟɬɵɪɟ ɛɚɣɬɚ, ɫɨɞɟɪɠɚɳɢɟ ɚɞɪɟɫ ɫɥɟɞɭɸɳɟɣ ɨɛɥɚɫɬɢ ɩɚɦɹɬɢ. ȿɫɥɢ ɜ ɞɚɧɧɵɣ ɦɨɦɟɧɬ ɡɚɝɪɭɠɟɧ ɞɪɭɝɨɣ ɤɨɧɬɪɚɫɬɟɪ, ɬɨ ɜɵɩɨɥɧɹɟɬɫɹ ɡɚɩɪɨɫ cnDelete. Ʉɨɧɬɪɚɫɬɟɪ ɫɱɢɬɵɜɚɟɬɫɹ ɢɡ ɮɚɣɥɚ ɢɥɢ ɢɡ ɩɚɦɹɬɢ. ȿɫɥɢ ɫɱɢɬɵɜɚɧɢɟ ɡɚɜɟɪɲɚɟɬɫɹ ɩɨ ɨɲɢɛɤɟ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 702 – ɨɲɢɛɤɚ ɫɱɢɬɵɜɚɧɢɹ ɤɨɧɬɪɚɫɬɟɪɚ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ.
ɍɞɚɥɟɧɢɟ ɤɨɧɬɪɚɫɬɟɪɚ (cnDelete) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function cnDelete : Logic; C: Logic cnDelete() Ⱥɪɝɭɦɟɧɬɨɜ ɧɟɬ. ɇɚɡɧɚɱɟɧɢɟ – ɭɞɚɥɹɟɬ ɡɚɝɪɭɠɟɧɧɨɝɨ ɜ ɩɚɦɹɬɶ ɤɨɧɬɪɚɫɬɟɪɚ. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. 1. ȿɫɥɢ ɫɩɢɫɨɤ ɜ ɦɨɦɟɧɬ ɩɨɥɭɱɟɧɢɹ ɡɚɩɪɨɫɚ ɤɨɧɬɪɚɫɬɟɪ ɧɟ ɡɚɝɪɭɠɟɧ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 701 – ɧɟɜɟɪɧɨɟ ɢɦɹ ɤɨɧɬɪɚɫɬɟɪɚ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ.
Ɂɚɩɢɫɶ ɤɨɧɬɪɚɫɬɟɪɚ (cnWrite) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function cnWrite(Var FileName : PString) : Logic; C: Logic cnWrite(PString* FileName) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: CompName – ɭɤɚɡɚɬɟɥɶ ɧɚ ɫɬɪɨɤɭ ɫɢɦɜɨɥɨɜ, ɫɨɞɟɪɠɚɳɭɸ ɢɦɹ ɤɨɧɬɪɚɫɬɟɪɚ. FileName – ɢɦɹ ɮɚɣɥɚ ɢɥɢ ɚɞɪɟɫ ɩɚɦɹɬɢ, ɤɭɞɚ ɧɚɞɨ ɡɚɩɢɫɚɬɶ ɤɨɧɬɪɚɫɬɟɪɚ. ɇɚɡɧɚɱɟɧɢɟ – ɫɨɯɪɚɧɹɟɬ ɤɨɧɬɪɚɫɬɟɪɚ ɜ ɮɚɣɥɟ ɢɥɢ ɜ ɩɚɦɹɬɢ. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. 1. ȿɫɥɢ ɜ ɦɨɦɟɧɬ ɩɨɥɭɱɟɧɢɹ ɡɚɩɪɨɫɚ ɤɨɧɬɪɚɫɬɟɪ ɧɟ ɡɚɝɪɭɠɟɧ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 701 – ɧɟɜɟɪɧɨɟ ɢɦɹ ɤɨɧɬɪɚɫɬɟɪɚ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 2. ȿɫɥɢ ɜ ɤɚɱɟɫɬɜɟ ɚɪɝɭɦɟɧɬɚ FileName ɞɚɧɚ ɫɬɪɨɤɚ, ɩɟɪɜɵɟ ɱɟɬɵɪɟ ɫɢɦɜɨɥɚ ɤɨɬɨɪɨɣ ɫɨɫɬɚɜɥɹɸɬ ɫɥɨɜɨ File, ɬɨ ɨɫɬɚɥɶɧɚɹ ɱɚɫɬɶ ɫɬɪɨɤɢ ɫɨɞɟɪɠɢɬ ɢɦɹ ɮɚɣɥɚ, ɞɥɹ ɡɚɩɢɫɢ ɤɨɦɩɨɧɟɧɬɚ. ȼ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ FileName ɞɨɥɠɟɧ ɫɨɞɟɪɠɚɬɶ ɩɭɫɬɨɣ ɭɤɚɡɚɬɟɥɶ. ȼ ɷɬɨɦ ɫɥɭɱɚɟ ɡɚɩɪɨɫ ɜɟɪɧɟɬ ɜ ɧɟɦ ɭɤɚɡɚɬɟɥɶ ɧɚ ɨɛɥɚɫɬɶ ɩɚɦɹɬɢ, ɤɭɞɚ ɛɭɞɟɬ ɩɨɦɟɳɟɧɨ ɨɩɢɫɚɧɢɟ ɤɨɦɩɨɧɟɧɬɚ ɜ ɮɨɪɦɚɬɟ ɞɥɹ ɡɚɩɢɫɢ ɧɚ ɞɢɫɤ. ȿɫɥɢ ɨɩɢɫɚɧɢɟ ɧɟ ɜɦɟɳɚɟɬɫɹ ɜ ɨɞɧɭ ɨɛɥɚɫɬɶ ɩɚɦɹɬɢ, ɬɨ ɜ ɬɟɤɫɬ ɛɭɞɟɬ ɜɤɥɸɱɟɧɨ ɤɥɸɱɟɜɨɟ ɫɥɨɜɨ Continue, ɡɚ ɤɨɬɨɪɵɦ ɫɥɟɞɭɟɬ ɱɟɬɵɪɟ ɛɚɣɬɚ, ɫɨɞɟɪɠɚɳɢɟ ɚɞɪɟɫ ɫɥɟɞɭɸɳɟɣ ɨɛɥɚɫɬɢ ɩɚɦɹɬɢ. 3. ȿɫɥɢ ɜɨ ɜɪɟɦɹ ɫɨɯɪɚɧɟɧɢɹ ɤɨɦɩɨɧɟɧɬɚ ɜɨɡɧɢɤɧɟɬ ɨɲɢɛɤɚ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 703 – ɨɲɢɛɤɚ ɫɨɯɪɚɧɟɧɢɹ ɤɨɧɬɪɚɫɬɟɪɚ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ.
ɂɧɢɰɢɚɰɢɹ ɪɟɞɚɤɬɨɪɚ ɤɨɧɬɪɚɫɬɟɪɚ Ʉ ɷɬɨɣ ɝɪɭɩɩɟ ɡɚɩɪɨɫɨɜ ɨɬɧɨɫɢɬɫɹ ɡɚɩɪɨɫ, ɤɨɬɨɪɵɣ ɢɧɢɰɢɢɪɭɟɬ ɪɚɛɨɬɭ ɧɟ ɪɚɫɫɦɚɬɪɢɜɚɟɦɨɝɨ ɜ ɞɚɧɧɨɣ ɪɚɛɨɬɟ ɤɨɦɩɨɧɟɧɬɚ – ɪɟɞɚɤɬɨɪɚ ɤɨɧɬɪɚɫɬɟɪɚ.
Ɋɟɞɚɤɬɢɪɨɜɚɬɶ ɤɨɧɬɪɚɫɬɟɪɚ (cnEdit) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Procedure cnEdit(CompName : PString); C: void cnEdit(PString CompName) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɚ:
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CompName – ɭɤɚɡɚɬɟɥɶ ɧɚ ɫɬɪɨɤɭ ɫɢɦɜɨɥɨɜ – ɢɦɹ ɮɚɣɥɚ ɢɥɢ ɚɞɪɟɫ ɩɚɦɹɬɢ, ɫɨɞɟɪɠɚɳɢɟ ɨɩɢɫɚɧɢɟ ɤɨɧɬɪɚɫɬɟɪɚ. ȿɫɥɢ ɜ ɤɚɱɟɫɬɜɟ ɚɪɝɭɦɟɧɬɚ CompName ɞɚɧɚ ɫɬɪɨɤɚ, ɩɟɪɜɵɟ ɱɟɬɵɪɟ ɫɢɦɜɨɥɚ ɤɨɬɨɪɨɣ ɫɨɫɬɚɜɥɹɸɬ ɫɥɨɜɨ File, ɬɨ ɨɫɬɚɥɶɧɚɹ ɱɚɫɬɶ ɫɬɪɨɤɢ ɫɨɞɟɪɠɢɬ ɢɦɹ ɤɨɧɬɪɚɫɬɟɪɚ ɢ ɩɨɫɥɟ ɩɪɨɛɟɥɚ ɢɦɹ ɮɚɣɥɚ, ɫɨɞɟɪɠɚɳɟɝɨ ɨɩɢɫɚɧɢɟ ɤɨɧɬɪɚɫɬɟɪɚ. ȼ ɩɪɨɬɢɜɧɨɦ ɫɥɭɱɚɟ ɫɱɢɬɚɟɬɫɹ, ɱɬɨ ɚɪɝɭɦɟɧɬ CompName ɫɨɞɟɪɠɢɬ ɭɤɚɡɚɬɟɥɶ ɧɚ ɨɛɥɚɫɬɶ ɩɚɦɹɬɢ, ɫɨɞɟɪɠɚɳɭɸ ɨɩɢɫɚɧɢɟ ɤɨɧɬɪɚɫɬɟɪɚ ɜ ɮɨɪɦɚɬɟ ɞɥɹ ɡɚɩɢɫɢ ɧɚ ɞɢɫɤ. ȿɫɥɢ ɨɩɢɫɚɧɢɟ ɧɟ ɜɦɟɳɚɟɬɫɹ ɜ ɨɞɧɭ ɨɛɥɚɫɬɶ ɩɚɦɹɬɢ, ɬɨ ɞɨɩɭɫɤɚɟɬɫɹ ɜɤɥɸɱɟɧɢɟ ɜ ɬɟɤɫɬ ɨɩɢɫɚɧɢɹ ɤɥɸɱɟɜɨɝɨ ɫɥɨɜɚ Continue, ɡɚ ɤɨɬɨɪɵɦ ɫɥɟɞɭɟɬ ɱɟɬɵɪɟ ɛɚɣɬɚ, ɫɨɞɟɪɠɚɳɢɟ ɚɞɪɟɫ ɫɥɟɞɭɸɳɟɣ ɨɛɥɚɫɬɢ ɩɚɦɹɬɢ. ȿɫɥɢ ɜ ɤɚɱɟɫɬɜɟ ɚɪɝɭɦɟɧɬɚ CompName ɩɟɪɟɞɚɧ ɩɭɫɬɨɣ ɭɤɚɡɚɬɟɥɶ ɢɥɢ ɭɤɚɡɚɬɟɥɶ ɧɚ ɩɭɫɬɭɸ ɫɬɪɨɤɭ, ɬɨ ɪɟɞɚɤɬɨɪ ɫɨɡɞɚɟɬ ɧɨɜɨɝɨ ɤɨɧɬɪɚɫɬɟɪɚ.
Ɋɚɛɨɬɚ ɫ ɩɚɪɚɦɟɬɪɚɦɢ ɤɨɧɬɪɚɫɬɟɪɚ ȼ ɞɚɧɧɨɦ ɪɚɡɞɟɥɟ ɨɩɢɫɚɧɵ ɡɚɩɪɨɫɵ, ɩɨɡɜɨɥɹɸɳɢɟ ɢɡɦɟɧɹɬɶ ɩɚɪɚɦɟɬɪɵ ɤɨɧɬɪɚɫɬɟɪɚ.
ɉɨɥɭɱɢɬɶ ɩɚɪɚɦɟɬɪɵ (cnGetData) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function cnGetData(Var Param : PRealArray ) : Logic; C: Logic cnGetData(PRealArray* Param) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɚ: Param – ɚɞɪɟɫ ɦɚɫɫɢɜɚ ɩɚɪɚɦɟɬɪɨɜ. ɇɚɡɧɚɱɟɧɢɟ – ɜɨɡɜɪɚɳɚɟɬ ɜɟɤɬɨɪ ɩɚɪɚɦɟɬɪɨɜ ɤɨɧɬɪɚɫɬɟɪɚ. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. 1. ȿɫɥɢ Error <> 0, ɬɨ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 2. ȿɫɥɢ ɜ ɦɨɦɟɧɬ ɩɨɥɭɱɟɧɢɹ ɡɚɩɪɨɫɚ ɤɨɧɬɪɚɫɬɟɪ ɧɟ ɡɚɝɪɭɠɟɧ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 701 – ɧɟɜɟɪɧɨɟ ɢɦɹ ɤɨɦɩɨɧɟɧɬɚ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 3. ȼ ɦɚɫɫɢɜ, ɚɞɪɟɫ ɤɨɬɨɪɨɝɨ ɩɟɪɟɞɚɧ ɜ ɚɪɝɭɦɟɧɬɟ Param, ɡɚɧɨɫɹɬɫɹ ɡɧɚɱɟɧɢɹ ɩɚɪɚɦɟɬɪɨɜ. ɉɚɪɚɦɟɬɪɵ ɡɚɧɨɫɹɬɫɹ ɜ ɦɚɫɫɢɜ ɜ ɩɨɪɹɞɤɟ ɨɩɢɫɚɧɢɹ ɜ ɪɚɡɞɟɥɟ ɨɩɢɫɚɧɢɹ ɫɬɚɬɢɱɟɫɤɢɯ ɩɟɪɟɦɟɧɧɵɯ.
ɉɨɥɭɱɢɬɶ ɢɦɟɧɚ ɩɚɪɚɦɟɬɪɨɜ (cnGetName) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function cnGetName(Var Param : PRealArray ) : Logic; C: Logic cnGetName(PRealArray* Param) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɚ: Param – ɚɞɪɟɫ ɦɚɫɫɢɜɚ ɭɤɚɡɚɬɟɥɟɣ ɧɚ ɧɚɡɜɚɧɢɹ ɩɚɪɚɦɟɬɪɨɜ. ɇɚɡɧɚɱɟɧɢɟ – ɜɨɡɜɪɚɳɚɟɬ ɜɟɤɬɨɪ ɭɤɚɡɚɬɟɥɟɣ ɧɚ ɧɚɡɜɚɧɢɹ ɩɚɪɚɦɟɬɪɨɜ ɤɨɧɬɪɚɫɬɟɪɚ. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. 1. ȿɫɥɢ Error <> 0, ɬɨ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 2. ȿɫɥɢ ɜ ɦɨɦɟɧɬ ɩɨɥɭɱɟɧɢɹ ɡɚɩɪɨɫɚ ɤɨɧɬɪɚɫɬɟɪ ɧɟ ɡɚɝɪɭɠɟɧ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 701 – ɧɟɜɟɪɧɨɟ ɢɦɹ ɤɨɦɩɨɧɟɧɬɚ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 3. ȼ ɦɚɫɫɢɜ, ɚɞɪɟɫ ɤɨɬɨɪɨɝɨ ɩɟɪɟɞɚɧ ɜ ɚɪɝɭɦɟɧɬɟ Param, ɡɚɧɨɫɹɬɫɹ ɚɞɪɟɫɚ ɫɢɦɜɨɥɶɧɵɯ ɫɬɪɨɤ, ɫɨɞɟɪɠɚɳɢɯ ɧɚɡɜɚɧɢɹ ɩɚɪɚɦɟɬɪɨɜ.
ɍɫɬɚɧɨɜɢɬɶ ɩɚɪɚɦɟɬɪɵ (cnSetData) Ɉɩɢɫɚɧɢɟ ɡɚɩɪɨɫɚ: Pascal: Function cnSetData(Param : PRealArray ) : Logic; C: Logic cnSetData(PRealArray Param) Ɉɩɢɫɚɧɢɟ ɚɪɝɭɦɟɧɬɨɜ: Param – ɚɞɪɟɫ ɦɚɫɫɢɜɚ ɩɚɪɚɦɟɬɪɨɜ.
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ɇɚɡɧɚɱɟɧɢɟ – ɡɚɦɟɧɹɟɬ ɡɧɚɱɟɧɢɹ ɩɚɪɚɦɟɬɪɨɜ ɤɨɧɬɪɚɫɬɟɪɚ ɧɚ ɡɧɚɱɟɧɢɹ, ɩɟɪɟɞɚɧɧɵɟ, ɜ ɚɪɝɭɦɟɧɬɟ Param. Ɉɩɢɫɚɧɢɟ ɢɫɩɨɥɧɟɧɢɹ. ȿɫɥɢ Error <> 0, ɬɨ ɜɵɩɨɥɧɟɧɢɟ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. ȿɫɥɢ ɜ ɦɨɦɟɧɬ ɩɨɥɭɱɟɧɢɹ ɡɚɩɪɨɫɚ ɤɨɧɬɪɚɫɬɟɪ ɧɟ ɡɚɝɪɭɠɟɧ, ɬɨ ɜɨɡɧɢɤɚɟɬ ɨɲɢɛɤɚ 701 – ɧɟɜɟɪɧɨɟ ɢɦɹ ɤɨɦɩɨɧɟɧɬɚ, ɭɩɪɚɜɥɟɧɢɟ ɩɟɪɟɞɚɟɬɫɹ ɨɛɪɚɛɨɬɱɢɤɭ ɨɲɢɛɨɤ, ɚ ɨɛɪɚɛɨɬɤɚ ɡɚɩɪɨɫɚ ɩɪɟɤɪɚɳɚɟɬɫɹ. 3. ɉɚɪɚɦɟɬɪɵ, ɡɧɚɱɟɧɢɹ ɤɨɬɨɪɵɯ ɯɪɚɧɹɬɫɹ ɜ ɦɚɫɫɢɜɟ, ɚɞɪɟɫ ɤɨɬɨɪɨɝɨ ɩɟɪɟɞɚɧ ɜ ɚɪɝɭɦɟɧɬɟ Param, ɩɟɪɟɞɚɸɬɫɹ ɤɨɧɬɪɚɫɬɟɪɭ. 1. 2.
Ɉɛɪɚɛɨɬɤɚ ɨɲɢɛɨɤ ȼ ɬɚɛɥ. 4 ɩɪɢɜɟɞɟɧ ɩɨɥɧɵɣ ɫɩɢɫɨɤ ɨɲɢɛɨɤ, ɤɨɬɨɪɵɟ ɦɨɝɭɬ ɜɨɡɧɢɤɚɬɶ ɩɪɢ ɜɵɩɨɥɧɟɧɢɢ ɡɚɩɪɨɫɨɜ ɤɨɦɩɨɧɟɧɬɨɦ ɤɨɧɬɪɚɫɬɟɪ, ɢ ɞɟɣɫɬɜɢɹ ɫɬɚɧɞɚɪɬɧɨɝɨ ɨɛɪɚɛɨɬɱɢɤɚ ɨɲɢɛɨɤ. Ɍɚɛɥɢɰɚ 4. Ɉɲɢɛɤɢ ɤɨɦɩɨɧɟɧɬɚ ɤɨɧɬɪɚɫɬɟɪ ɢ ɞɟɣɫɬɜɢɹ ɫɬɚɧɞɚɪɬɧɨɝɨ ɨɛɪɚɛɨɬɱɢɤɚ ɨɲɢɛɨɤ. ʋɇɚɡɜɚɧɢɟ ɨɲɢɛɤɢɋɬɚɧɞɚɪɬɧɚɹ ɨɛɪɚɛɨɬɤɚ 701 ɇɟɫɨɜɦɟɫɬɢɦɨɫɬɶ ɫɟɬɢ ɢ ɤɨɧɬɪɚɫɬɟɪɚɁɚɧɟɫɟɧɢɟ ɧɨɦɟɪɚ ɜ Error 702 Ɉɲɢɛɤɚ ɫɱɢɬɵɜɚɧɢɹ ɤɨɧɬɪɚɫɬɟɪɚɁɚɧɟɫɟɧɢɟ ɧɨɦɟɪɚ ɜ Error 703 Ɉɲɢɛɤɚ ɫɨɯɪɚɧɟɧɢɹ ɤɨɧɬɪɚɫɬɟɪɚɁɚɧɟɫɟɧɢɟ ɧɨɦɟɪɚ ɜ Error 704 ɇɟɤɨɪɪɟɤɬɧɚɹ ɪɚɛɨɬɚ ɫ ɩɚɦɹɬɶɸɁɚɧɟɫɟɧɢɟ ɧɨɦɟɪɚ ɜ Error 705 Ɉɲɢɛɤɚ ɢɫɩɨɥɧɟɧɢɹ ɤɨɧɬɪɚɫɬɟɪɚɁɚɧɟɫɟɧɢɟ ɧɨɦɟɪɚ ɜ Error 706 ɇɟɜɟɪɧɨɟ ɢɫɩɨɥɶɡɨɜɚɧɢɟ ɡɚɩɪɨɫɚ ɧɚ ɩɪɟɪɵɜɚɧɢɟ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɹɁɚɧɟɫɟɧɢɟ ɧɨɦɟɪɚ ɜ Error
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ɋɩɢɫɨɤ ɥɢɬɟɪɚɬɭɪɵ1 1. 2. 3. 4. 5. 6. 7. 8.
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Ⱥɣɜɚɡɹɧ ɋ.Ⱥ., Ȼɟɠɚɟɜɚ Ɂ.ɂ., ɋɬɚɪɨɜɟɪɨɜ Ɉ.ȼ. Ʉɥɚɫɫɢɮɢɤɚɰɢɹ ɦɧɨɝɨɦɟɪɧɵɯ ɧɚɛɥɸɞɟɧɢɣ.Ɇ.: ɋɬɚɬɢɫɬɢɤɚ, 1974.- 240 ɫ. Ⱥɣɡɟɪɦɚɧ Ɇ.Ⱥ., Ȼɪɚɜɟɪɦɚɧ ɗ.Ɇ., Ɋɨɡɨɧɨɷɪ Ʌ.ɂ. Ɇɟɬɨɞ ɩɨɬɟɧɰɢɚɥɶɧɵɯ ɮɭɧɤɰɢɣ ɜ ɬɟɨɪɢɢ ɨɛɭɱɟɧɢɹ ɦɚɲɢɧ. Ɇ.: ɇɚɭɤɚ, 1970.- 383 ɫ. Ⱥɧɚɫɬɚɡɢ Ⱥ. ɉɫɢɯɨɥɨɝɢɱɟɫɤɨɟ ɬɟɫɬɢɪɨɜɚɧɢɟ. Ɇ. ɉɟɞɚɝɨɝɢɤɚ, 1982. Ʉɧɢɝɚ 1. 320 ɫ.; Ʉɧɢɝɚ 2. 360 ɫ. Ⱥɧɞɟɪɫɨɧ Ɍ. ȼɜɟɞɟɧɢɟ ɜ ɦɧɨɝɨɦɟɪɧɵɣ ɫɬɚɬɢɫɬɢɱɟɫɤɢɣ ɚɧɚɥɢɡ.- Ɇ.: Ɏɢɡɦɚɬɝɢɡ, 1963. 500 ɫ. Ⱥɧɞɟɪɫɨɧ Ɍ. ɋɬɚɬɢɫɬɢɱɟɫɤɢɣ ɚɧɚɥɢɡ ɜɪɟɦɟɧɧɵɯ ɪɹɞɨɜ. Ɇ.: Ɇɢɪ, 1976. 755 ɫ. Ⱥɧɭɮɪɢɟɜ Ⱥ.Ɏ. ɉɫɢɯɨɞɢɚɝɧɨɫɬɢɤɚ ɤɚɤ ɞɟɹɬɟɥɶɧɨɫɬɶ ɢ ɧɚɭɱɧɚɹ ɞɢɫɰɢɩɥɢɧɚ // ȼɨɩɪɨɫɵ ɩɫɢɯɨɥɨɝɢɢ, 1994, ʋ 2. ɋ. 123-130. Ⱥɪɤɚɞɶɟɜ Ⱥ.Ƚ., Ȼɪɚɜɟɪɦɚɧ ɗ.Ɇ. Ɉɛɭɱɟɧɢɟ ɦɚɲɢɧɵ ɤɥɚɫɫɢɮɢɤɚɰɢɢ ɨɛɴɟɤɬɨɜ.- Ɇ.: ɇɚɭɤɚ, 1971.- 172 ɫ. Ⱥɯɚɩɤɢɧ ɘ.Ʉ., ȼɫɟɜɨɥɞɨɜ ɇ.ɂ., Ȼɚɪɰɟɜ ɋ.ɂ. ɢ ɞɪ. Ȼɢɨɬɟɯɧɢɤɚ - ɧɨɜɨɟ ɧɚɩɪɚɜɥɟɧɢɟ ɤɨɦɩɶɸɬɟɪɢɡɚɰɢɢ. ɋɟɪɢɹ "Ɍɟɨɪɟɬɢɱɟɫɤɚɹ ɢ ɩɪɢɤɥɚɞɧɚɹ ɛɢɨɮɢɡɢɤɚ", Ɇ: ɢɡɞ. ȼɂɇɂɂɌɂ, 1990. 144 ɫ. Ȼɚɪɰɟɜ ɋ.ɂ. ɇɟɤɨɬɨɪɵɟ ɫɜɨɣɫɬɜɚ ɚɞɚɩɬɢɜɧɵɯ ɫɟɬɟɣ. ɉɪɟɩɪɢɧɬ ɂɎ ɋɈ Ⱥɇ ɋɋɋɊ, Ʉɪɚɫɧɨɹɪɫɤ, 1987, ʋ71Ȼ, 17 ɫ. Ȼɚɪɰɟɜ ɋ.ɂ., Ƚɢɥɟɜ ɋ.ȿ., Ɉɯɨɧɢɧ ȼ.Ⱥ. ɉɪɢɧɰɢɩ ɞɜɨɣɫɬɜɟɧɧɨɫɬɢ ɜ ɨɪɝɚɧɢɡɚɰɢɢ ɚɞɚɩɬɢɜɧɵɯ ɫɢɫɬɟɦ ɨɛɪɚɛɨɬɤɢ ɢɧɮɨɪɦɚɰɢɢ// Ⱦɢɧɚɦɢɤɚ ɯɢɦɢɱɟɫɤɢɯ ɢ ɛɢɨɥɨɝɢɱɟɫɤɢɯ ɫɢɫɬɟɦ, ɇɨɜɨɫɢɛɢɪɫɤ, ɇɚɭɤɚ, 1989, ɫ.6-55. Ȼɚɪɰɟɜ ɋ.ɂ., Ʌɚɧɤɢɧ ɘ.ɉ. Ɇɨɞɟɥɢɪɨɜɚɧɢɟ ɚɧɚɥɨɝɨɜɵɯ ɚɞɚɩɬɢɜɧɵɯ ɫɟɬɟɣ. ɉɪɟɩɪɢɧɬ ɂɧɫɬɢɬɭɬɚ ɛɢɨɮɢɡɢɤɢ ɋɈ ɊȺɇ, Ʉɪɚɫɧɨɹɪɫɤ, 1993, ʋ203Ȼ, 36 ɫ. Ȼɚɪɰɟɜ ɋ.ɂ., Ʌɚɧɤɢɧ ɘ.ɉ. ɋɪɚɜɧɢɬɟɥɶɧɵɟ ɫɜɨɣɫɬɜɚ ɚɞɚɩɬɢɜɧɵɯ ɫɟɬɟɣ ɫ ɩɨɥɹɪɧɵɦɢ ɢ ɧɟɩɨɥɹɪɧɵɦɢ ɫɢɧɚɩɫɚɦɢ. ɉɪɟɩɪɢɧɬ ɂɧɫɬɢɬɭɬɚ ɛɢɨɮɢɡɢɤɢ ɋɈ ɊȺɇ, Ʉɪɚɫɧɨɹɪɫɤ, 1993, ʋ196Ȼ, 26 ɫ. Ȼɚɪɰɟɜ ɋ.ɂ., Ɇɚɲɢɯɢɧɚ ɇ.ɘ., ɋɭɪɨɜ ɋ.ȼ. ɇɟɣɪɨɧɧɵɟ ɫɟɬɢ: ɩɨɞɯɨɞɵ ɤ ɚɩɩɚɪɚɬɧɨɣ ɪɟɚɥɢɡɚɰɢɢ. ɉɪɟɩɪɢɧɬ ɂɎ ɋɈ Ⱥɇ ɋɋɋɊ, Ʉɪɚɫɧɨɹɪɫɤ, 1990, ʋ122Ȼ, 14 ɫ. Ȼɚɪɰɟɜ ɋ.ɂ., Ɉɯɨɧɢɧ ȼ.Ⱥ. Ⱥɞɚɩɬɢɜɧɵɟ ɫɟɬɢ ɨɛɪɚɛɨɬɤɢ ɢɧɮɨɪɦɚɰɢɢ. ɉɪɟɩɪɢɧɬ ɂɎ ɋɈ Ⱥɇ ɋɋɋɊ, Ʉɪɚɫɧɨɹɪɫɤ, 1986, ʋ59Ȼ, 20 ɫ. Ȼɚɪɰɟɜ ɋ.ɂ., Ɉɯɨɧɢɧ ȼ.Ⱥ. Ⱥɞɚɩɬɢɜɧɵɟ ɫɟɬɢ, ɮɭɧɤɰɢɨɧɢɪɭɸɳɢɟ ɜ ɧɟɩɪɟɪɵɜɧɨɦ ɜɪɟɦɟɧɢ // ɗɜɨɥɸɰɢɨɧɧɨɟ ɦɨɞɟɥɢɪɨɜɚɧɢɟ ɢ ɤɢɧɟɬɢɤɚ, ɇɨɜɨɫɢɛɢɪɫɤ, ɇɚɭɤɚ, 1992, ɫ.24-30. Ȼɟɪɤɢɧɛɥɢɬ Ɇ.Ȼ., Ƚɟɥɶɮɚɧɞ ɂ.Ɇ., Ɏɟɥɶɞɦɚɧ Ⱥ.Ƚ. Ⱦɜɢɝɚɬɟɥɶɧɵɟ ɡɚɞɚɱɢ ɢ ɪɚɛɨɬɚ ɩɚɪɚɥɥɟɥɶɧɵɯ ɩɪɨɝɪɚɦɦ // ɂɧɬɟɥɥɟɤɬɭɚɥɶɧɵɟ ɩɪɨɰɟɫɫɵ ɢ ɢɯ ɦɨɞɟɥɢɪɨɜɚɧɢɟ. Ɉɪɝɚɧɢɡɚɰɢɹ ɞɜɢɠɟɧɢɹ.- Ɇ.: ɇɚɭɤɚ, 1991.- ɋ. 37-54. Ȼɨɧɧɟɪ Ɋ.ȿ. ɇɟɤɨɬɨɪɵɟ ɦɟɬɨɞɵ ɤɥɚɫɫɢɮɢɤɚɰɢɢ // Ⱥɜɬɨɦɚɬɢɱɟɫɤɢɣ ɚɧɚɥɢɡ ɢɡɨɛɪɚɠɟɧɢɣ.- Ɇ.: Ɇɢɪ, 1969.- ɋ. 205-234. Ȼɨɪɢɫɨɜ Ⱥ.ȼ., Ƚɢɥɟɜ ɋ.ȿ., Ƚɨɥɨɜɟɧɤɢɧ ɋ.ȿ., Ƚɨɪɛɚɧɶ Ⱥ.ɇ., Ⱦɨɝɚɞɢɧ ɋ.Ⱥ., Ʉɨɱɟɧɨɜ Ⱦ.Ⱥ., Ɇɚɫɥɟɧɧɢɤɨɜɚ ȿ.ȼ., Ɇɚɬɸɲɢɧ Ƚ.ȼ., Ɇɢɪɤɟɫ ȿ.Ɇ., ɇɨɡɞɪɚɱɟɜ Ʉ.Ƚ., Ɋɨɫɫɢɟɜ Ⱦ.Ⱥ., ɋɚɜɱɟɧɤɨ Ⱥ.Ⱥ., ɒɭɥɶɦɚɧ ȼ.Ⱥ. ɇɟɣɪɨɢɦɢɬɚɬɨɪ "MultiNeuron" ɢ ɟɝɨ ɩɪɢɦɟɧɟɧɢɹ ɜ ɦɟɞɢɰɢɧɟ. // Ɇɚɬɟɦɚɬɢɱɟɫɤɨɟ ɨɛɟɫɩɟɱɟɧɢɟ ɢ ɚɪɯɢɬɟɤɬɭɪɚ ɗȼɆ: Ɇɚɬɟɪɢɚɥɵ ɧɚɭɱɧɨ-ɬɟɯɧɢɱɟɫɤɨɣ ɤɨɧɮɟɪɟɧɰɢɢ "ɉɪɨɛɥɟɦɵ ɬɟɯɧɢɤɢ ɢ ɬɟɯɧɨɥɨɝɢɣ XXI ɜɟɤɚ", 22-25 ɦɚɪɬɚ 1994 ɝ. / ɄȽɌɍ. Ʉɪɚɫɧɨɹɪɫɤ, 1994. ɋ. 14-18. Ȼɪɚɜɟɪɦɚɧ ɗ.Ɇ., Ɇɭɱɧɢɤ ɂ.Ȼ. ɋɬɪɭɤɬɭɪɧɵɟ ɦɟɬɨɞɵ ɨɛɪɚɛɨɬɤɢ ɷɦɩɢɪɢɪɢɱɟɫɤɢɯ ɞɚɧɧɵɯ. Ɇ.: ɇɚɭɤɚ. Ƚɥɚɜɧɚɹ ɪɟɞɚɤɰɢɹ ɮɢɡɢɤɨ-ɦɚɬɟɦɚɬɢɱɟɫɤɨɣ ɥɢɬɟɪɚɬɭɪɵ. 1983. - 464 ɫ. Ȼɭɤɚɬɨɜɚ ɂ.Ʌ. ɗɜɨɥɸɰɢɨɧɧɨɟ ɦɨɞɟɥɢɪɨɜɚɧɢɟ ɢ ɟɝɨ ɩɪɢɥɨɠɟɧɢɹ.- Ɇ.: ɇɚɭɤɚ, 1979.- 231 ɫ. Ȼɭɪɥɚɱɭɤ Ʌ.Ɏ., Ʉɨɪɠɨɜɚ ȿ.ɘ. Ʉ ɩɨɫɬɪɨɟɧɢɸ ɬɟɨɪɢɢ "ɢɡɦɟɪɟɧɧɨɣ ɢɧɞɢɜɢɞɭɚɥɶɧɨɫɬɢ" ɜ ɩɫɢɯɨɞɢɚɝɧɨɫɬɢɤɟ // ȼɨɩɪɨɫɵ ɩɫɢɯɨɥɨɝɢɢ 1994, N5. ɋ. 5-11. ȼɚɜɢɥɨɜ ȿ.ɂ., ȿɝɨɪɨɜ Ȼ.Ɇ., Ʌɚɧɰɟɜ ȼ.ɋ., Ɍɨɰɟɧɤɨ ȼ.Ƚ. ɋɢɧɬɟɡ ɫɯɟɦ ɧɚ ɩɨɪɨɝɨɜɵɯ ɷɥɟɦɟɧɬɚɯ. - Ɇ.: ɋɨɜ. ɪɚɞɢɨ, 1970. ȼɚɩɧɢɤ ȼ.ɇ., ɑɟɪɜɨɧɟɧɤɢɫ Ⱥ.Ɏ. Ɍɟɨɪɢɹ ɪɚɫɩɨɡɧɚɜɚɧɢɹ ɨɛɪɚɡɨɜ. - Ɇ.: ɇɚɭɤɚ, 1974. ȼɟɞɟɧɨɜ Ⱥ.Ⱥ. Ɇɨɞɟɥɢɪɨɜɚɧɢɟ ɷɥɟɦɟɧɬɨɜ ɦɵɲɥɟɧɢɹ. - Ɇ.: ɇɚɭɤɚ, 1988.
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ɋɩɢɫɨɤɥɢɬɟɪɚɬɭɪɵ ɡɚɜɟɞɨɦɨ ɧɟɩɨɥɨɧ. ȼ ɧɟɝɨ ɜɤɥɸɱɟɧɵ ɤɥɚɫɫɢɱɟɫɤɢɟ ɪɚɛɨɬɵ, ɦɨɧɨɝɪɚɮɢɢ ɢ ɫɬɚɬɶɢ ɨɛɡɨɪɧɨɝɨ ɯɚɪɚɤɬɟɪɚ, ɚ ɬɚɤɠɟ ɩɭɛɥɢɤɚɰɢɢ, ɧɟɩɨɫɪɟɞɫɬɜɟɧɧɨ ɢɫɩɨɥɶɡɨɜɚɧɧɵɟ ɜ ɤɧɢɝɟ. Ʉɪɨɦɟ ɬɨɝɨ, ɜ ɫɩɢɫɨɤ ɜɤɥɸɱɟɧɵ ɜɫɟ ɫɤɨɥɶɤɨ-ɧɢɛɭɞɶ ɫɭɳɟɫɬɜɟɧɧɵɟ ɩɭɛɥɢɤɚɰɢɢ ɱɥɟɧɨɜ ɤɪɚɫɧɨɹɪɫɤɨɣ ɝɪɭɩɩɵ ɇɟɣɪɨɄɨɦɩ, ɜɵɲɟɞɲɢɟ ɤ ɦɨɦɟɧɬɭ ɫɞɚɱɢ ɤɧɢɝɢ ɜ ɩɟɱɚɬɶ.
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25. Ƚɚɜɪɢɥɨɜɚ Ɍ.Ⱥ., ɑɟɪɜɢɧɫɤɚɹ Ʉ.Ɋ., əɲɢɧ Ⱥ.Ɇ. Ɏɨɪɦɢɪɨɜɚɧɢɟ ɩɨɥɹ ɡɧɚɧɢɣ ɧɚ ɩɪɢɦɟɪɟ ɩɫɢɯɨɞɢɚɝɧɨɫɬɢɤɢ // ɂɡɜ. Ⱥɇ ɋɋɋɊ. Ɍɟɯɧ. ɤɢɛɟɪɧɟɬɢɤɚ, 1988, ʋ 5.- ɋ. 72-85. 26. Ƚɚɥɭɲɤɢɧ Ⱥ.ɂ. ɋɢɧɬɟɡ ɦɧɨɝɨɫɥɨɣɧɵɯ ɫɯɟɦ ɪɚɫɩɨɡɧɚɜɚɧɢɹ ɨɛɪɚɡɨɜ. - Ɇ.: ɗɧɟɪɝɢɹ, 1974. 27. Ƚɚɥɭɲɤɢɧ Ⱥ.ɂ., Ɏɨɦɢɧ ɘ.ɂ. ɇɟɣɪɨɧɧɵɟ ɫɟɬɢ ɤɚɤ ɥɢɧɟɣɧɵɟ ɩɨɫɥɟɞɨɜɚɬɟɥɶɧɵɟ ɦɚɲɢɧɵ. Ɇ.: ɂɡɞ-ɜɨ ɆȺɂ, 1991. 28. Ƚɟɥɶɮɚɧɞ ɂ.Ɇ., ɐɟɬɥɢɧ Ɇ.Ʌ. Ɉ ɦɚɬɟɦɚɬɢɱɟɫɤɨɦ ɦɨɞɟɥɢɪɨɜɚɧɢɢ ɦɟɯɚɧɢɡɦɨɜ ɰɟɧɬɪɚɥɶɧɨɣ ɧɟɪɜɧɨɣ ɫɢɫɬɟɦɵ // Ɇɨɞɟɥɢ ɫɬɪɭɤɬɭɪɧɨ-ɮɭɧɤɰɢɨɧɚɥɶɧɨɣ ɨɪɝɚɧɢɡɚɰɢɢ ɧɟɤɨɬɨɪɵɯ ɛɢɨɥɨɝɢɱɟɫɤɢɯ ɫɢɫɬɟɦ.- Ɇ.: ɇɚɭɤɚ, 1966.- ɋ. 9-26. 29. Ƚɢɥɟɜ ɋ.ȿ. "ɋɪɚɜɧɟɧɢɟ ɦɟɬɨɞɨɜ ɨɛɭɱɟɧɢɹ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ", // Ɍɟɡɢɫɵ ɞɨɤɥɚɞɨɜ III ȼɫɟɪɨɫɢɣɫɤɨɝɨ ɫɟɦɢɧɚɪɚ "ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ", Ʉɪɚɫɧɨɹɪɫɤ: ɂɡɞ-ɜɨ ɄȽɌɍ, ɫɫ. 80-81. 30. Ƚɢɥɟɜ ɋ.ȿ. "ɋɪɚɜɧɟɧɢɟ ɯɚɪɚɤɬɟɪɢɫɬɢɱɟɫɤɢɯ ɮɭɧɤɰɢɣ ɧɟɣɪɨɧɨɜ", // Ɍɟɡɢɫɵ ɞɨɤɥɚɞɨɜ III ȼɫɟɪɨɫɢɣɫɤɨɝɨ ɫɟɦɢɧɚɪɚ "ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ", Ʉɪɚɫɧɨɹɪɫɤ: ɂɡɞ-ɜɨ ɄȽɌɍ, 1995, ɫ. 82 31. Ƚɢɥɟɜ ɋ.ȿ. Forth-propagation - ɦɟɬɨɞ ɜɵɱɢɫɥɟɧɢɹ ɝɪɚɞɢɟɧɬɨɜ ɨɰɟɧɤɢ // Ɍɟɡɢɫɵ ɞɨɤɥɚɞɨɜ II ȼɫɟɪɨɫɫɢɣɫɤɨɝɨ ɪɚɛɨɱɟɝɨ ɫɟɦɢɧɚɪɚ "ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ" (Ʉɪɚɫɧɨɹɪɫɤ, 710 ɨɤɬɹɛɪɹ 1994 ɝ.) / Ʉɪɚɫɧɨɹɪɫɤ: ɂɡɞ-ɜɨ ɄȽɌɍ, 1994, ɫ. 36-37. 32. Ƚɢɥɟɜ ɋ.ȿ. Ⱥɜɬɨɪɟɮ. ɞɢɫɫ. ɤɚɧɞ.ɮɢɡ.-ɦɚɬ. ɧɚɭɤ // Ʉɪɚɫɧɨɹɪɫɤ, ɄȽɌɍ, 1997. 33. Ƚɢɥɟɜ ɋ.ȿ. Ⱥɥɝɨɪɢɬɦ ɫɨɤɪɚɳɟɧɢɹ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ, ɨɫɧɨɜɚɧɧɵɣ ɧɚ ɪɚɡɧɨɫɬɧɨɣ ɨɰɟɧɤɟ ɜɬɨɪɵɯ ɩɪɨɢɡɜɨɞɧɵɯ ɰɟɥɟɜɨɣ ɮɭɧɤɰɢɢ // ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ. Ɍɟɡɢɫɵ ɞɨɤɥɚɞɨɜ 5 ȼɫɟɪɨɫɫɢɣɫɤɨɝɨ ɫɟɦɢɧɚɪɚ, 3-5 ɨɤɬɹɛɪɹ 1997 ɝ. / ɉɨɞ ɪɟɞ. Ⱥ.ɇ.Ƚɨɪɛɚɧɹ. Ʉɪɚɫɧɨɹɪɫɤ: ɢɡɞ. ɄȽɌɍ, 1997. ɋ. 45-46. 34. Ƚɢɥɟɜ ɋ.ȿ. Ƚɢɛɪɢɞ ɫɟɬɢ ɞɜɨɣɫɬɜɟɧɧɨɫɬɢ ɢ ɥɢɧɟɣɧɨɣ ɫɟɬɢ// ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɵ/ ɬɟɡɢɫɵ ɞɨɤɥɚɞɨɜ ɪɚɛɨɱɟɝɨ ɫɟɦɢɧɚɪɚ 8-11 ɨɤɬɹɛɪɹ 1993 ɝ., Ʉɪɚɫɧɨɹɪɫɤ/ ɂɧɫɬɢɬɭɬ ɛɢɨɮɢɡɢɤɢ ɋɈ ɊȺɇ, 1993. ɋ. 25. 35. Ƚɢɥɟɜ ɋ.ȿ. Ɇɟɬɨɞ ɩɨɥɭɱɟɧɢɹ ɝɪɚɞɢɟɧɬɨɜ ɨɰɟɧɤɢ ɩɨ ɩɨɞɫɬɪɨɟɱɧɵɦ ɩɚɪɚɦɟɬɪɚɦ ɛɟɡ ɢɫɩɨɥɶɡɨɜɚɧɢɹ back propagation // ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ: Ɇɚɬɟɪɢɚɥɵ III ȼɫɟɪɨɫɫɢɣɫɤɨɝɨ ɫɟɦɢɧɚɪɚ, 6-8 ɨɤɬɹɛɪɹ 1995 ɝ. ɑ. 1/ɉɨɞ ɪɟɞ. Ⱥ.ɇ.Ƚɨɪɛɚɧɹ; Ʉɪɚɫɧɨɹɪɫɤ: ɂɡɞɜɨ ɄȽɌɍ, 1995. ɋ. 91-100. 36. Ƚɢɥɟɜ ɋ.ȿ. ɇɟɣɪɨɫɟɬɶ ɫ ɤɜɚɞɪɚɬɢɱɧɵɦɢ ɫɭɦɦɚɬɨɪɚɦɢ// ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɵ/ ɬɟɡɢɫɵ ɞɨɤɥɚɞɨɜ ɪɚɛɨɱɟɝɨ ɫɟɦɢɧɚɪɚ 8-11 ɨɤɬɹɛɪɹ 1993 ɝ., Ʉɪɚɫɧɨɹɪɫɤ/ ɂɧɫɬɢɬɭɬ ɛɢɨɮɢɡɢɤɢ ɋɈ ɊȺɇ, 1993. ɋ. 11-12. 37. Ƚɢɥɟɜ ɋ.ȿ., Ƚɨɪɛɚɧɶ Ⱥ.ɇ. “ɉɥɨɬɧɨɫɬɶ ɩɨɥɭɝɪɭɩɩ ɧɟɩɪɟɪɵɜɧɵɯ ɮɭɧɤɰɢɣ”.- 4 ȼɫɟɪɨɫɫɢɣɫɤɢɣ ɪɚɛɨɱɢɣ ɫɟɦɢɧɚɪ “ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ”, 5-7 ɨɤɬɹɛɪɹ 1996 ɝ., Ɍɟɡɢɫɵ ɞɨɤɥɚɞɨɜ. Ʉɪɚɫɧɨɹɪɫɤ: ɢɡɞ. ɄȽɌɍ, 1996, ɫ. 7-9. 38. Ƚɢɥɟɜ ɋ.ȿ., Ƚɨɪɛɚɧɶ Ⱥ.ɇ. Ɉ ɩɨɥɧɨɬɟ ɤɥɚɫɫɚ ɮɭɧɤɰɢɣ, ɜɵɱɢɫɥɢɦɵɯ ɧɟɣɪɨɧɧɵɦɢ ɫɟɬɹɦɢ. ȼɬɨɪɨɣ ɋɢɛɢɪɫɤɢɣ ɤɨɧɝɪɟɫɫ ɩɨ ɉɪɢɤɥɚɞɧɨɣ ɢ ɂɧɞɭɫɬɪɢɚɥɶɧɨɣ Ɇɚɬɟɦɚɬɢɤɟ, ɩɨɫɜɹɳɟɧɧɵɣ ɩɚɦɹɬɢ Ⱥ.Ⱥ.Ʌɹɩɭɧɨɜɚ (1911-1973), Ⱥ.ɉ.ȿɪɲɨɜɚ (1931-1988) ɢ ɂ.Ⱥ.ɉɨɥɟɬɚɟɜɚ (1915-1983). ɇɨɜɨɫɢɛɢɪɫɤ, ɢɸɧɶ 1996. Ɍɟɡɢɫɵ ɞɨɤɥɚɞɨɜ, ɱɚɫɬɶ 1. ɂɡɞ. ɂɧɫɬɢɬɭɬɚ ɦɚɬɟɦɚɬɢɤɢ ɋɈ ɊȺɇ. ɋ. 6. 39. Ƚɢɥɟɜ ɋ.ȿ., Ƚɨɪɛɚɧɶ Ⱥ.ɇ., Ɇɢɪɤɟɫ ȿ.Ɇ. Ɇɚɥɵɟ ɷɤɫɩɟɪɬɵ ɢ ɜɧɭɬɪɟɧɧɢɟ ɤɨɧɮɥɢɤɬɵ ɜ ɨɛɭɱɚɟɦɵɯ ɧɟɣɪɨɧɧɵɯ ɫɟɬɹɯ // Ⱦɨɤɥɚɞɵ Ⱥɤɚɞɟɦɢɢ ɇɚɭɤ ɋɋɋɊ.- 1991.- Ɍ.320, N.1.- ɋ.220223. 40. Ƚɢɥɟɜ ɋ.ȿ., Ƚɨɪɛɚɧɶ Ⱥ.ɇ., Ɇɢɪɤɟɫ ȿ.Ɇ., Ʉɨɱɟɧɨɜ Ⱦ.Ⱥ., Ɋɨɫɫɢɟɜ Ⱦ.Ⱥ. "Ɉɩɪɟɞɟɥɟɧɢɟ ɡɧɚɱɢɦɨɫɬɢ ɨɛɭɱɚɸɳɢɯ ɩɚɪɚɦɟɬɪɨɜ ɞɥɹ ɩɪɢɧɹɬɢɹ ɧɟɣɪɨɧɧɨɣ ɫɟɬɶɸ ɪɟɲɟɧɢɹ ɨɛ ɨɬɜɟɬɟ"// ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɵ/ ɬɟɡɢɫɵ ɞɨɤɥɚɞɨɜ ɪɚɛɨɱɟɝɨ ɫɟɦɢɧɚɪɚ 8-11 ɨɤɬɹɛɪɹ 1993 ɝ., Ʉɪɚɫɧɨɹɪɫɤ/ ɂɧɫɬɢɬɭɬ ɛɢɨɮɢɡɢɤɢ ɋɈ ɊȺɇ, 1993. ɋ. 8. 41. Ƚɢɥɟɜ ɋ.ȿ., Ƚɨɪɛɚɧɶ Ⱥ.ɇ., Ɇɢɪɤɟɫ ȿ.Ɇ., Ʉɨɱɟɧɨɜ Ⱦ.Ⱥ., Ɋɨɫɫɢɟɜ Ⱦ.Ⱥ. "ɇɟɣɪɨɫɟɬɟɜɚɹ ɩɪɨɝɪɚɦɦɚ MultiNeuron"// ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɵ/ ɬɟɡɢɫɵ ɞɨɤɥɚɞɨɜ ɪɚɛɨɱɟɝɨ ɫɟɦɢɧɚɪɚ 8-11 ɨɤɬɹɛɪɹ 1993 ɝ., Ʉɪɚɫɧɨɹɪɫɤ/ ɂɧɫɬɢɬɭɬ ɛɢɨɮɢɡɢɤɢ ɋɈ ɊȺɇ, 1993. ɋ. 9. 42. Ƚɢɥɟɜ ɋ.ȿ., Ƚɨɪɛɚɧɶ Ⱥ.ɇ., Ɇɢɪɤɟɫ ȿ.Ɇ., ɇɨɜɨɯɨɞɶɤɨ Ⱥ.ɘ. "ɉɚɤɟɬ ɩɪɨɝɪɚɦɦ ɢɦɢɬɚɰɢɢ ɪɚɡɥɢɱɧɵɯ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ"// ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɵ/ ɬɟɡɢɫɵ ɞɨɤɥɚɞɨɜ ɪɚɛɨɱɟɝɨ ɫɟɦɢɧɚɪɚ 8-11 ɨɤɬɹɛɪɹ 1993 ɝ., Ʉɪɚɫɧɨɹɪɫɤ/ ɂɧɫɬɢɬɭɬ ɛɢɨɮɢɡɢɤɢ ɋɈ ɊȺɇ, 1993. ɋ. 7. 43. Ƚɢɥɟɜ ɋ.ȿ., Ʉɨɱɟɧɨɜ Ⱦ.Ⱥ., Ɇɢɪɤɟɫ ȿ.Ɇ., Ɋɨɫɫɢɟɜ Ⱦ.Ⱥ. Ʉɨɧɬɪɚɫɬɢɪɨɜɚɧɢɟ, ɨɰɟɧɤɚ ɡɧɚɱɢɦɨɫɬɢ ɩɚɪɚɦɟɬɪɨɜ, ɨɩɬɢɦɢɡɚɰɢɹ ɢɯ ɡɧɚɱɟɧɢɣ ɢ ɢɯ ɢɧɬɟɪɩɪɟɬɚɰɢɹ ɜ ɧɟɣɪɨɧɧɵɯ ɫɟɬɹɯ // ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ: Ɇɚɬɟɪɢɚɥɵ III ȼɫɟɪɨɫɫɢɣɫɤɨɝɨ ɫɟɦɢɧɚɪɚ, 6-8 ɨɤɬɹɛɪɹ 1995 ɝ. ɑ. 1/ɉɨɞ ɪɟɞ. Ⱥ.ɇ.Ƚɨɪɛɚɧɹ; Ʉɪɚɫɧɨɹɪɫɤ: ɂɡɞ-ɜɨ ɄȽɌɍ, 1995. ɋ. 66-78.
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44. Ƚɢɥɟɜ ɋ.ȿ., Ɇɢɪɤɟɫ ȿ.Ɇ. Ɉɛɭɱɟɧɢɟ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ // ɗɜɨɥɸɰɢɨɧɧɨɟ ɦɨɞɟɥɢɪɨɜɚɧɢɟ ɢ ɤɢɧɟɬɢɤɚ. ɇɨɜɨɫɢɛɢɪɫɤ: ɇɚɭɤɚ, 1992. ɋ. 9-23. 45. Ƚɢɥɟɜ ɋ.ȿ., Ɇɢɪɤɟɫ ȿ.Ɇ., ɇɨɜɨɯɨɞɶɤɨ Ⱥ.ɘ., ɐɚɪɟɝɨɪɨɞɰɟɜ ȼ.Ƚ., ɑɟɪɬɵɤɨɜ ɉ.ȼ. "ɉɪɨɟɤɬ ɹɡɵɤɚ ɨɩɢɫɚɧɢɹ ɧɟɣɪɨɫɟɬɟɜɵɯ ɚɜɬɨɦɚɬɨɜ" // Ɍɟɡɢɫɵ ɞɨɤɥɚɞɨɜ II ȼɫɟɪɨɫɫɢɣɫɤɨɝɨ ɪɚɛɨɱɟɝɨ ɫɟɦɢɧɚɪɚ "ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ" (Ʉɪɚɫɧɨɹɪɫɤ, 7-10 ɨɤɬɹɛɪɹ 1994 ɝ.) / Ʉɪɚɫɧɨɹɪɫɤ: ɂɡɞ-ɜɨ ɄȽɌɍ, 1994, ɫ. 35. 46. Ƚɢɥɟɜɚ Ʌ.ȼ., Ƚɢɥɟɜ ɋ.ȿ., Ƚɨɪɛɚɧɶ Ⱥ.ɇ. ɇɟɣɪɨɫɟɬɟɜɨɣ ɛɢɧɚɪɧɵɣ ɤɥɚɫɫɢɮɢɤɚɬɨɪ "CLAB" (ɨɩɢɫɚɧɢɟ ɩɚɤɟɬɚ ɩɪɨɝɪɚɦɦ). Ʉɪɚɫɧɨɹɪɫɤ: ɂɧ-ɬ ɛɢɨɮɢɡɢɤɢ ɋɈ ɊȺɇ, 1992. 25 c. ɉɪɟɩɪɢɧɬ ʋ 194 Ȼ. 47. Ƚɢɥɟɜɚ Ʌ.ȼ., Ƚɢɥɟɜ ɋ.ȿ., Ƚɨɪɛɚɧɶ Ⱥ.ɇ., Ƚɨɪɞɢɟɧɤɨ ɉ.ȼ., ȿɪɟɦɢɧ Ⱦ.ɂ., Ʉɨɱɟɧɨɜ Ⱦ.Ⱥ., Ɇɢɪɤɟɫ ȿ.Ɇ., Ɋɨɫɫɢɟɜ Ⱦ.Ⱥ., ɍɦɧɨɜ ɇ.Ⱥ. ɇɟɣɪɨɩɪɨɝɪɚɦɦɵ. ɍɱɟɛɧɨɟ ɩɨɫɨɛɢɟ: ȼ 2 ɱ. // Ʉɪɚɫɧɨɹɪɫɤ, Ʉɪɚɫɧɨɹɪɫɤɢɣ ɝɨɫɭɞɚɪɫɬɜɟɧɧɵɣ ɬɟɯɧɢɱɟɫɤɢɣ ɭɧɢɜɟɪɫɢɬɟɬ, 1994.260 ɫ. 48. Ƚɢɥɥ Ɏ., Ɇɸɪɪɟɣ ɍ., Ɋɚɣɬ Ɇ. ɉɪɚɤɬɢɱɟɫɤɚɹ ɨɩɬɢɦɢɡɚɰɢɹ. Ɇ.: Ɇɢɪ,1985. 509 ɫ. 49. Ƚɨɥɨɜɟɧɤɢɧ ɋ.ȿ., Ƚɨɪɛɚɧɶ Ⱥ.ɇ., ɒɭɥɶɦɚɧ ȼ.Ⱥ., Ɋɨɫɫɢɟɜ Ⱦ.Ⱥ., ɇɚɡɚɪɨɜ Ȼ.ɇ., Ɇɨɫɢɧɚ ȼ.Ⱥ., Ɂɢɧɱɟɧɤɨ Ɉ.ɉ., Ɇɢɪɤɟɫ ȿ.Ɇ., Ɇɚɬɸɲɢɧ Ƚ.ȼ., Ȼɭɝɚɟɧɤɨ ɇ.ɇ. Ȼɚɡɚ ɞɚɧɧɵɯ ɞɥɹ ɚɩɪɨɛɚɰɢɢ ɫɢɫɬɟɦ ɪɚɫɩɨɡɧɚɜɚɧɢɹ ɢ ɩɪɨɝɧɨɡɚ: ɨɫɥɨɠɧɟɧɢɹ ɢɧɮɚɪɤɬɚ ɦɢɨɤɚɪɞɚ // ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ. Ɍɟɡɢɫɵ ɞɨɤɥɚɞɨɜ 5 ȼɫɟɪɨɫɫɢɣɫɤɨɝɨ ɫɟɦɢɧɚɪɚ, 3-5 ɨɤɬɹɛɪɹ 1997 ɝ. / ɉɨɞ ɪɟɞ. Ⱥ.ɇ.Ƚɨɪɛɚɧɹ. Ʉɪɚɫɧɨɹɪɫɤ: ɢɡɞ. ɄȽɌɍ, 1997. ɋ. 47. 50. Ƚɨɥɨɜɟɧɤɢɧ ɋ.ȿ., ɇɚɡɚɪɨɜ Ȼ.ȼ., Ɇɚɬɸɲɢɧ Ƚ.ȼ., Ɋɨɫɫɢɟɜ Ⱦ.Ⱥ., ɒɟɜɱɟɧɤɨ ȼ.Ɏ., Ɂɢɧɱɟɧɤɨ Ɉ.ɉ., Ɍɨɤɚɪɟɜɚ ɂ.Ɇ. ɉɪɨɝɧɨɡɢɪɨɜɚɧɢɟ ɜɨɡɧɢɤɧɨɜɟɧɢɹ ɦɟɪɰɚɬɟɥɶɧɨɣ ɚɪɢɬɦɢɢ ɜ ɨɫɬɪɵɣ ɢ ɩɨɞɨɫɬɪɵɣ ɩɟɪɢɨɞɵ ɢɧɮɚɪɤɬɚ ɦɢɨɤɚɪɞɚ ɫ ɩɨɦɨɳɶɸ ɤɨɦɩɶɸɬɟɪɧɵɯ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ // Ⱥɤɬɭɚɥɶɧɵɟ ɩɪɨɛɥɟɦɵ ɪɟɚɛɢɥɢɬɚɰɢɢ ɛɨɥɶɧɵɯ ɫ ɫɟɪɞɟɱɧɨ-ɫɨɫɭɞɢɫɬɵɦɢ ɡɚɛɨɥɟɜɚɧɢɹɦɢ. Ɍɟɡ. ɞɨɤɥ. ɫɢɦɩɨɡɢɭɦɚ 18-20 ɦɚɹ 1994 ɝ., "Ʉɪɚɫɧɨɹɪɫɤɨɟ Ɂɚɝɨɪɶɟ", Ʉɪɚɫɧɨɹɪɫɤ.1994.- ɋ.28. 51. Ƚɨɥɨɜɟɧɤɢɧ ɋ.ȿ., Ɋɨɫɫɢɟɜ Ⱦ.Ⱥ., ɇɚɡɚɪɨɜ Ȼ.ȼ., ɒɭɥɶɦɚɧ ȼ.Ⱥ., Ɇɚɬɸɲɢɧ Ƚ.ȼ., Ɂɢɧɱɟɧɤɨ Ɉ.ɉ. ɉɪɨɝɧɨɡɢɪɨɜɚɧɢɟ ɜɨɡɧɢɤɧɨɜɟɧɢɹ ɮɢɛɪɢɥɥɹɰɢɢ ɩɪɟɞɫɟɪɞɢɣ ɤɚɤ ɨɫɥɨɠɧɟɧɢɹ ɢɧɮɚɪɤɬɚ ɦɢɨɤɚɪɞɚ ɫ ɩɨɦɨɳɶɸ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ // Ⱦɢɚɝɧɨɫɬɢɤɚ, ɢɧɮɨɪɦɚɬɢɤɚ ɢ ɦɟɬɪɨɥɨɝɢɹ - 94.Ɍɟɡ. ɧɚɭɱɧɨ-ɬɟɯɧɢɱɟɫɤɨɣ ɤɨɧɮɟɪɟɧɰɢɢ (ɝ. ɋɚɧɤɬ-ɉɟɬɟɪɛɭɪɝ, 28-30 ɢɸɧɹ 1994 ɝ.).ɋ.ɉɟɬɟɪɛɭɪɝ.- 1994.- ɋ.349. 52. Ƚɨɥɨɜɟɧɤɢɧ ɋ.ȿ., Ɋɨɫɫɢɟɜ Ⱦ.Ⱥ., ɒɭɥɶɦɚɧ ȼ.Ⱥ., Ɇɚɬɸɲɢɧ Ƚ.ȼ., ɒɟɜɱɟɧɤɨ ȼ.Ɏ. ɉɪɨɝɧɨɡɢɪɨɜɚɧɢɟ ɫɟɪɞɟɱɧɨɣ ɧɟɞɨɫɬɚɬɨɱɧɨɫɬɢ ɭ ɛɨɥɶɧɵɯ ɫɨ ɫɥɨɠɧɵɦɢ ɧɚɪɭɲɟɧɢɹɦɢ ɫɟɪɞɟɱɧɨɝɨ ɪɢɬɦɚ ɫ ɩɨɦɨɳɶɸ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ // Ⱦɢɚɝɧɨɫɬɢɤɚ, ɢɧɮɨɪɦɚɬɢɤɚ ɢ ɦɟɬɪɨɥɨɝɢɹ 94.- Ɍɟɡ. ɧɚɭɱɧɨ-ɬɟɯɧɢɱɟɫɤɨɣ ɤɨɧɮɟɪɟɧɰɢɢ (ɝ. ɋɚɧɤɬ-ɉɟɬɟɪɛɭɪɝ, 28-30 ɢɸɧɹ 1994 ɝ.).- ɋ.ɉɟɬɟɪɛɭɪɝ.- 1994.ɋ.350-351. 53. Ƚɨɥɭɛɶ Ⱦ.ɇ., Ƚɨɪɛɚɧɶ Ⱥ.ɇ. Ɇɧɨɝɨɱɚɫɬɢɱɧɵɟ ɫɟɬɱɚɬɤɢ ɞɥɹ ɚɫɫɨɰɢɚɬɢɜɧɨɣ ɩɚɦɹɬɢ. ȼɬɨɪɨɣ ɋɢɛɢɪɫɤɢɣ ɤɨɧɝɪɟɫɫ ɩɨ ɉɪɢɤɥɚɞɧɨɣ ɢ ɂɧɞɭɫɬɪɢɚɥɶɧɨɣ Ɇɚɬɟɦɚɬɢɤɟ, ɩɨɫɜɹɳɟɧɧɵɣ ɩɚɦɹɬɢ Ⱥ.Ⱥ.Ʌɹɩɭɧɨɜɚ (1911-1973), Ⱥ.ɉ.ȿɪɲɨɜɚ (1931-1988) ɢ ɂ.Ⱥ.ɉɨɥɟɬɚɟɜɚ (1915-1983). ɇɨɜɨɫɢɛɢɪɫɤ, ɢɸɧɶ 1996. Ɍɟɡɢɫɵ ɞɨɤɥɚɞɨɜ, ɱɚɫɬɶ 3. ɂɡɞ. ɂɧɫɬɢɬɭɬɚ ɦɚɬɟɦɚɬɢɤɢ ɋɈ ɊȺɇ. ɋ. 271. 54. Ƚɨɪɛɚɧɶ Ⱥ.ɇ, ɇɟɣɪɨɄɨɦɩ ɢɥɢ 9 ɥɟɬ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɧɵɯ ɢɫɫɥɟɞɨɜɚɧɢɣ ɜ Ʉɪɚɫɧɨɹɪɫɤɟ // Ⱥɤɬɭɚɥɶɧɵɟ ɩɪɨɛɥɟɦɵ ɢɧɮɨɪɦɚɬɢɤɢ, ɩɪɢɤɥɚɞɧɨɣ ɦɚɬɟɦɚɬɢɤɢ ɢ ɦɟɯɚɧɢɤɢ, ɱ. 3, ɇɨɜɨɫɢɛɢɪɫɤ - Ʉɪɚɫɧɨɹɪɫɤ: ɢɡ-ɜɨ ɋɈ ɊȺɇ, 1996. - ɫ. 13 - 37. 55. Ƚɨɪɛɚɧɶ Ⱥ.ɇ. Ⱥɥɝɨɪɢɬɦɵ ɢ ɩɪɨɝɪɚɦɦɵ ɛɵɫɬɪɨɝɨ ɨɛɭɱɟɧɢɹ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ. // ɗɜɨɥɸɰɢɨɧɧɨɟ ɦɨɞɟɥɢɪɨɜɚɧɢɟ ɢ ɤɢɧɟɬɢɤɚ. ɇɨɜɨɫɢɛɢɪɫɤ: ɇɚɭɤɚ, 1992. ɋ.36-39. 56. Ƚɨɪɛɚɧɶ Ⱥ.ɇ. Ȼɵɫɬɪɨɟ ɞɢɮɮɟɪɟɧɰɢɪɨɜɚɧɢɟ ɫɥɨɠɧɵɯ ɮɭɧɤɰɢɣ ɢ ɨɛɪɚɬɧɨɟ ɪɚɫɩɪɨɫɬɪɚɧɟɧɢɟ ɨɲɢɛɤɢ // ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ. Ɍɟɡɢɫɵ ɞɨɤɥɚɞɨɜ 5 ȼɫɟɪɨɫɫɢɣɫɤɨɝɨ ɫɟɦɢɧɚɪɚ, 3-5 ɨɤɬɹɛɪɹ 1997 ɝ. / ɉɨɞ ɪɟɞ. Ⱥ.ɇ.Ƚɨɪɛɚɧɹ. Ʉɪɚɫɧɨɹɪɫɤ: ɢɡɞ. ɄȽɌɍ, 1997. ɋ. 5456. 57. Ƚɨɪɛɚɧɶ Ⱥ.ɇ. Ȼɵɫɬɪɨɟ ɞɢɮɮɟɪɟɧɰɢɪɨɜɚɧɢɟ, ɞɜɨɣɫɬɜɟɧɧɨɫɬɶ ɢ ɨɛɪɚɬɧɨɟ ɪɚɫɩɪɨɫɬɪɚɧɟɧɢɟ ɨɲɢɛɤɢ / ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɇɨɜɨɫɢɛɢɪɫɤ: ɇɚɭɤɚ, ɋɢɛɢɪɫɤɚɹ ɢɡɞɚɬɟɥɶɫɤɚɹ ɮɢɪɦɚ ɊȺɇ, 1998. 58. Ƚɨɪɛɚɧɶ Ⱥ.ɇ. ȼɨɡɦɨɠɧɨɫɬɢ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ / ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɇɨɜɨɫɢɛɢɪɫɤ: ɇɚɭɤɚ, ɋɢɛɢɪɫɤɚɹ ɢɡɞɚɬɟɥɶɫɤɚɹ ɮɢɪɦɚ ɊȺɇ, 1998. 59. Ƚɨɪɛɚɧɶ Ⱥ.ɇ. Ⱦɜɨɣɫɬɜɟɧɧɨɫɬɶ ɜ ɫɟɬɹɯ ɚɜɬɨɦɚɬɨɜ // ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ: Ɇɚɬɟɪɢɚɥɵ III ȼɫɟɪɨɫɫɢɣɫɤɨɝɨ ɫɟɦɢɧɚɪɚ, 6-8 ɨɤɬɹɛɪɹ 1995 ɝ. ɑ. 1/ɉɨɞ ɪɟɞ. Ⱥ.ɇ.Ƚɨɪɛɚɧɹ; Ʉɪɚɫɧɨɹɪɫɤ: ɂɡɞ-ɜɨ ɄȽɌɍ, 1995. ɋ. 32-66. 60. Ƚɨɪɛɚɧɶ Ⱥ.ɇ. Ɇɵ ɩɪɟɞɥɚɝɚɟɦ ɞɥɹ ɤɨɧɬɪɨɥɹ ɤɚɱɟɫɬɜɚ ɢɫɩɨɥɶɡɨɜɚɬɶ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɵ // ɋɬɚɧɞɚɪɬɵ ɢ ɤɚɱɟɫɬɜɨ, 1994, ʋ 10, ɫ. 52.
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61. Ƚɨɪɛɚɧɶ Ⱥ.ɇ. ɇɟɣɪɨɤɨɦɩ // ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ: Ɇɚɬɟɪɢɚɥɵ III ȼɫɟɪɨɫɫɢɣɫɤɨɝɨ ɫɟɦɢɧɚɪɚ, 6-8 ɨɤɬɹɛɪɹ 1995 ɝ. ɑ. 1/ɉɨɞ ɪɟɞ. Ⱥ.ɇ.Ƚɨɪɛɚɧɹ; Ʉɪɚɫɧɨɹɪɫɤ: ɂɡɞɜɨ ɄȽɌɍ, 1995. ɋ. 3-31. 62. Ƚɨɪɛɚɧɶ Ⱥ.ɇ. ɇɟɣɪɨɤɨɦɩɶɸɬɟɪ, ɢɥɢ Ⱥɧɚɥɨɝɨɜɵɣ ɪɟɧɟɫɫɚɧɫ. Ɇɢɪ ɉɄ, 1994. ʋ 10. ɋ. 126130. 63. Ƚɨɪɛɚɧɶ Ⱥ.ɇ. Ɉɛɨɛɳɟɧɢɟ ɚɩɩɪɨɤɫɢɦɚɰɢɨɧɧɨɣ ɬɟɨɪɟɦɵ Cɬɨɭɧɚ // ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ. Ɍɟɡɢɫɵ ɞɨɤɥɚɞɨɜ 5 ȼɫɟɪɨɫɫɢɣɫɤɨɝɨ ɫɟɦɢɧɚɪɚ, 3-5 ɨɤɬɹɛɪɹ 1997 ɝ. / ɉɨɞ ɪɟɞ. Ⱥ.ɇ.Ƚɨɪɛɚɧɹ. Ʉɪɚɫɧɨɹɪɫɤ: ɢɡɞ. ɄȽɌɍ, 1997. ɋ. 59-62. 64. Ƚɨɪɛɚɧɶ Ⱥ.ɇ. Ɉɛɭɱɟɧɢɟ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ. Ɇ.: ɢɡɞ. ɋɋɋɊ-ɋɒȺ ɋɉ "ParaGraph", 1990. 160 ɫ. (English Translation: AMSE Transaction, Scientific Siberian, A, 1993. Vol. 6. Neurocomputing. PP. 1-134). 65. Ƚɨɪɛɚɧɶ Ⱥ.ɇ. ɉɪɨɛɥɟɦɚ ɫɤɪɵɬɵɯ ɩɚɪɚɦɟɬɪɨɜ ɢ ɡɚɞɚɱɢ ɬɪɚɧɫɩɨɧɢɪɨɜɚɧɧɨɣ ɪɟɝɪɟɫɫɢɢ // ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ. Ɍɟɡɢɫɵ ɞɨɤɥɚɞɨɜ 5 ȼɫɟɪɨɫɫɢɣɫɤɨɝɨ ɫɟɦɢɧɚɪɚ, 3-5 ɨɤɬɹɛɪɹ 1997 ɝ. / ɉɨɞ ɪɟɞ. Ⱥ.ɇ.Ƚɨɪɛɚɧɹ. Ʉɪɚɫɧɨɹɪɫɤ: ɢɡɞ. ɄȽɌɍ, 1997. ɋ. 57-58. 66. Ƚɨɪɛɚɧɶ Ⱥ.ɇ. ɉɪɨɟɤɰɢɨɧɧɵɟ ɫɟɬɱɚɬɤɢ ɞɥɹ ɨɛɪɚɛɨɬɤɢ ɛɢɧɚɪɧɵɯ ɢɡɨɛɪɚɠɟɧɢɣ. // Ɇɚɬɟɦɚɬɢɱɟɫɤɨɟ ɨɛɟɫɩɟɱɟɧɢɟ ɢ ɚɪɯɢɬɟɤɬɭɪɚ ɗȼɆ / Ɇɚɬɟɪɢɚɥɵ ɧɚɭɱɧɨ-ɬɟɯɧɢɱɟɫɤɨɣ ɤɨɧɮɟɪɟɧɰɢɢ "ɉɪɨɛɥɟɦɵ ɬɟɯɧɢɤɢ ɢ ɬɟɯɧɨɥɨɝɢɣ XXI ɜɟɤɚ", 22-25 ɦɚɪɬɚ 1994 ɝ., Ʉɪɚɫɧɨɹɪɫɤ: ɢɡɞ. ɄȽɌɍ, 1994. ɋ. 50-54. 67. Ƚɨɪɛɚɧɶ Ⱥ.ɇ. Ɋɚɡɦɵɬɵɟ ɷɬɚɥɨɧɵ ɜ ɨɛɭɱɟɧɢɢ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ // Ɍɟɡɢɫɵ ɞɨɤɥɚɞɨɜ II ȼɫɟɪɨɫɫɢɣɫɤɨɝɨ ɪɚɛɨɱɟɝɨ ɫɟɦɢɧɚɪɚ "ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ" (Ʉɪɚɫɧɨɹɪɫɤ, 710 ɨɤɬɹɛɪɹ 1994 ɝ.) / Ʉɪɚɫɧɨɹɪɫɤ: ɂɡɞ-ɜɨ ɄȽɌɍ, 1994, ɫ. 6-9. 68. Ƚɨɪɛɚɧɶ Ⱥ.ɇ. Ɋɟɲɟɧɢɟ ɡɚɞɚɱ ɧɟɣɪɨɧɧɵɦɢ ɫɟɬɹɦɢ / ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɇɨɜɨɫɢɛɢɪɫɤ: ɇɚɭɤɚ, ɋɢɛɢɪɫɤɚɹ ɢɡɞɚɬɟɥɶɫɤɚɹ ɮɢɪɦɚ ɊȺɇ, 1998. 69. Ƚɨɪɛɚɧɶ Ⱥ.ɇ. ɋɢɫɬɟɦɵ ɫ ɧɚɫɥɟɞɨɜɚɧɢɟɦ ɢ ɷɮɮɟɤɬɵ ɨɬɛɨɪɚ. // ɗɜɨɥɸɰɢɨɧɧɨɟ ɦɨɞɟɥɢɪɨɜɚɧɢɟ ɢ ɤɢɧɟɬɢɤɚ. ɇɨɜɨɫɢɛɢɪɫɤ: ɇɚɭɤɚ, 1992. ɋ. 40-71. 70. Ƚɨɪɛɚɧɶ Ⱥ.ɇ. Ɍɨɱɧɨɟ ɩɪɟɞɫɬɚɜɥɟɧɢɟ ɦɧɨɝɨɱɥɟɧɨɜ ɨɬ ɧɟɫɤɨɥɶɤɢɯ ɩɟɪɟɦɟɧɧɵɯ ɫ ɩɨɦɨɳɶɸ ɥɢɧɟɣɧɵɯ ɮɭɧɤɰɢɣ, ɨɩɟɪɚɰɢɢ ɫɭɩɟɪɩɨɡɢɰɢɢ ɢ ɩɪɨɢɡɜɨɥɶɧɨɝɨ ɧɟɥɢɧɟɣɧɨɝɨ ɦɧɨɝɨɱɥɟɧɚ ɨɬ ɨɞɧɨɝɨ ɩɟɪɟɦɟɧɧɨɝɨ // ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ. Ɍɟɡɢɫɵ ɞɨɤɥɚɞɨɜ 5 ȼɫɟɪɨɫɫɢɣɫɤɨɝɨ ɫɟɦɢɧɚɪɚ, 3-5 ɨɤɬɹɛɪɹ 1997 ɝ. / ɉɨɞ ɪɟɞ. Ⱥ.ɇ.Ƚɨɪɛɚɧɹ. Ʉɪɚɫɧɨɹɪɫɤ: ɢɡɞ. ɄȽɌɍ, 1997. ɋ. 63-65. 71. Ƚɨɪɛɚɧɶ Ⱥ.ɇ. ɗɬɨɬ ɞɢɜɧɵɣ ɧɨɜɵɣ ɤɨɦɩɶɸɬɟɪɧɵɣ ɦɢɪ. Ɂɚɦɟɬɤɢ ɨ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚɯ ɢ ɧɨɜɨɣ ɬɟɯɧɢɱɟɫɤɨɣ ɪɟɜɨɥɸɰɢɢ // Ɇɚɬɟɦɚɬɢɱɟɫɤɨɟ ɨɛɟɫɩɟɱɟɧɢɟ ɢ ɚɪɯɢɬɟɤɬɭɪɚ ɗȼɆ / Ɇɚɬɟɪɢɚɥɵ ɧɚɭɱɧɨ-ɬɟɯɧɢɱɟɫɤɨɣ ɤɨɧɮɟɪɟɧɰɢɢ "ɉɪɨɛɥɟɦɵ ɬɟɯɧɢɤɢ ɢ ɬɟɯɧɨɥɨɝɢɣ XXI ɜɟɤɚ", 22-25 ɦɚɪɬɚ 1994 ɝ., Ʉɪɚɫɧɨɹɪɫɤ: ɢɡɞ. ɄȽɌɍ, 1994. ɋ.42-49. 72. Ƚɨɪɛɚɧɶ Ⱥ.ɇ., Ⱦɪɭɠɢɧɢɧɚ ɇ.ȼ., Ɋɨɫɫɢɟɜ Ⱦ.Ⱥ. “ɇɟɣɪɨɫɟɬɟɜɚɹ ɢɧɬɟɪɩɪɟɬɚɰɢɹ ɫɩɟɤɬɪɨɮɨɬɨɦɟɬɪɢɱɟɫɤɨɝɨ ɫɩɨɫɨɛɚ ɢɫɫɥɟɞɨɜɚɧɢɹ ɫɨɞɟɪɠɚɧɢɹ ɦɟɥɚɧɢɧɚ ɜ ɪɟɫɧɢɰɚɯ ɢ ɩɨɞɫɱɟɬ ɡɧɚɱɢɦɨɫɬɢ ɨɛɭɱɚɸɳɢɯ ɩɚɪɚɦɟɬɪɨɜ ɧɟɪɨɫɟɬɢ ɫ ɰɟɥɶɸ ɞɢɚɝɧɨɫɬɢɤɢ ɭɜɟɚɥɶɧɵɯ ɦɟɥɚɧɨɦ”.- 4 ȼɫɟɪɨɫɫɢɣɫɤɢɣ ɪɚɛɨɱɢɣ ɫɟɦɢɧɚɪ “ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ”, 5-7 ɨɤɬɹɛɪɹ 1996 ɝ., Ɍɟɡɢɫɵ ɞɨɤɥɚɞɨɜ. Ʉɪɚɫɧɨɹɪɫɤ: ɢɡɞ. ɄȽɌɍ, 1996, ɫ. 94. 73. Ƚɨɪɛɚɧɶ Ⱥ.ɇ., Ʉɨɲɭɪ ȼ.Ⱦ. ɇɟɣɪɨɫɟɬɟɜɵɟ ɦɨɞɟɥɢ ɢ ɦɟɬɨɞɵ ɪɟɲɟɧɢɹ ɡɚɞɚɱ ɞɢɧɚɦɢɤɢ ɫɩɥɨɲɧɵɯ ɫɪɟɞ ɢ ɮɢɡɢɤɢ ɜɡɚɢɦɨɞɟɣɫɬɜɭɸɳɢɯ ɱɚɫɬɢɰ // 10 Ɂɢɦɧɹɹ ɲɤɨɥɚ ɩɨ ɦɟɯɚɧɢɤɟ ɫɩɥɨɲɧɵɯ ɫɪɟɞ (ɬɟɡɢɫɵ ɞɨɤɥɚɞɨɜ) / ȿɤɚɬɟɪɢɧɛɭɪɝ: ɍɪɈ ɊȺɇ, 1995. ɋ. 75-77. 74. Ƚɨɪɛɚɧɶ Ⱥ.ɇ., Ɇɢɪɤɟɫ ȿ.Ɇ. "Ʉɨɦɩɨɧɟɧɬɵ ɧɟɣɪɨɩɪɨɝɪɚɦɦ", ɬɟɡɢɫɵ ɞɨɤɥɚɞɨɜ III ȼɫɟɪɨɫɢɣɫɤɨɝɨ ɫɟɦɢɧɚɪɚ "ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ", Ʉɪɚɫɧɨɹɪɫɤ: ɂɡɞ-ɜɨ ɄȽɌɍ, ɫ. 17 75. Ƚɨɪɛɚɧɶ Ⱥ.ɇ., Ɇɢɪɤɟɫ ȿ.Ɇ. Ʌɨɝɢɱɟɫɤɢ ɩɪɨɡɪɚɱɧɵɟ ɧɟɣɪɨɧɧɵɟ ɫɟɬɢ ɞɥɹ ɩɪɨɢɡɜɨɞɫɬɜɚ ɡɧɚɧɢɣ ɢɡ ɞɚɧɧɵɯ. ȼɵɱɢɫɥɢɬɟɥɶɧɵɣ ɰɟɧɬɪ ɋɈ ɊȺɇ ɜ ɝ. Ʉɪɚɫɧɨɹɪɫɤɟ. Ʉɪɚɫɧɨɹɪɫɤ, 1997. 12 ɫ., ɛɢɛɥɢɨɝɪ. 12 ɧɚɡɜ. (Ɋɭɤɨɩɢɫɶ ɞɟɩ. ɜ ȼɂɇɂɌɂ 17.07.97, ʋ 2434-ȼ97) 76. Ƚɨɪɛɚɧɶ Ⱥ.ɇ., Ɇɢɪɤɟɫ ȿ.Ɇ. ɇɟɣɪɨɧɧɵɟ ɫɟɬɢ ɚɫɫɨɰɢɚɬɢɜɧɨɣ ɩɚɦɹɬɢ, ɮɭɧɤɰɢɨɧɢɪɭɸɳɢɟ ɜ ɞɢɫɤɪɟɬɧɨɦ ɜɪɟɦɟɧɢ. ȼɵɱɢɫɥɢɬɟɥɶɧɵɣ ɰɟɧɬɪ ɋɈ ɊȺɇ ɜ ɝ. Ʉɪɚɫɧɨɹɪɫɤɟ. Ʉɪɚɫɧɨɹɪɫɤ, 1997. 23 ɫ., ɛɢɛɥɢɨɝɪ. 8 ɧɚɡɜ. (Ɋɭɤɨɩɢɫɶ ɞɟɩ. ɜ ȼɂɇɂɌɂ 17.07.97, ʋ 2436-ȼ97) 77. Ƚɨɪɛɚɧɶ Ⱥ.ɇ., Ɇɢɪɤɟɫ ȿ.Ɇ. “ɂɧɮɨɪɦɚɰɢɨɧɧɚɹ ɟɦɤɨɫɬɶ ɬɟɧɡɨɪɧɵɯ ɫɟɬɟɣ”.- 4 ȼɫɟɪɨɫɫɢɣɫɤɢɣ ɪɚɛɨɱɢɣ ɫɟɦɢɧɚɪ “ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ”, 5-7 ɨɤɬɹɛɪɹ 1996 ɝ., Ɍɟɡɢɫɵ ɞɨɤɥɚɞɨɜ. Ʉɪɚɫɧɨɹɪɫɤ: ɢɡɞ. ɄȽɌɍ, 1996, ɫ. 22-23. 78. Ƚɨɪɛɚɧɶ Ⱥ.ɇ., Ɇɢɪɤɟɫ ȿ.Ɇ. “ɉɨɦɟɯɨɭɫɬɨɣɱɢɜɨɫɬɶ ɬɟɧɡɨɪɧɵɯ ɫɟɬɟɣ”.- 4 ȼɫɟɪɨɫɫɢɣɫɤɢɣ ɪɚɛɨɱɢɣ ɫɟɦɢɧɚɪ “ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ”, 5-7 ɨɤɬɹɛɪɹ 1996 ɝ., Ɍɟɡɢɫɵ ɞɨɤɥɚɞɨɜ. Ʉɪɚɫɧɨɹɪɫɤ: ɢɡɞ. ɄȽɌɍ, 1996, ɫ. 24-25.
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79. Ƚɨɪɛɚɧɶ Ⱥ.ɇ., Ɇɢɪɤɟɫ ȿ.Ɇ. “Ɍɟɧɡɨɪɧɵɟ ɫɟɬɢ ɚɫɫɨɰɢɚɬɢɜɧɨɣ ɩɚɦɹɬɢ”.- 4 ȼɫɟɪɨɫɫɢɣɫɤɢɣ ɪɚɛɨɱɢɣ ɫɟɦɢɧɚɪ “ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ”, 5-7 ɨɤɬɹɛɪɹ 1996 ɝ., Ɍɟɡɢɫɵ ɞɨɤɥɚɞɨɜ. Ʉɪɚɫɧɨɹɪɫɤ: ɢɡɞ. ɄȽɌɍ, 1996, ɫ. 20-21. 80. Ƚɨɪɛɚɧɶ Ⱥ.ɇ., Ɇɢɪɤɟɫ ȿ.Ɇ. Ʉɨɞɢɪɨɜɚɧɢɟ ɤɚɱɟɫɬɜɟɧɧɵɯ ɩɪɢɡɧɚɤɨɜ ɞɥɹ ɧɟɣɪɨɫɟɬɟɣ // Ɍɟɡɢɫɵ ɞɨɤɥɚɞɨɜ II ȼɫɟɪɨɫɫɢɣɫɤɨɝɨ ɪɚɛɨɱɟɝɨ ɫɟɦɢɧɚɪɚ "ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ" (Ʉɪɚɫɧɨɹɪɫɤ, 7-10 ɨɤɬɹɛɪɹ 1994 ɝ.) / Ʉɪɚɫɧɨɹɪɫɤ: ɂɡɞ-ɜɨ ɄȽɌɍ, 1994, ɫ. 29. 81. Ƚɨɪɛɚɧɶ Ⱥ.ɇ., Ɇɢɪɤɟɫ ȿ.Ɇ. Ʉɨɧɬɪɚɫɬɢɪɨɜɚɧɢɟ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ, ɬɟɡɢɫɵ ɞɨɤɥɚɞɨɜ III ȼɫɟɪɨɫɢɣɫɤɨɝɨ ɫɟɦɢɧɚɪɚ "ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ", Ʉɪɚɫɧɨɹɪɫɤ: ɂɡɞ-ɜɨ ɄȽɌɍ, ɫɫ. 78-79 82. Ƚɨɪɛɚɧɶ Ⱥ.ɇ., Ɇɢɪɤɟɫ ȿ.Ɇ. Ʌɨɝɢɱɟɫɤɢ ɩɪɨɡɪɚɱɧɵɟ ɧɟɣɪɨɧɧɵɟ ɫɟɬɢ, ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ: ɬɟɡɢɫɵ ɞɨɤɥɚɞɨɜ III ȼɫɟɪɨɫɢɣɫɤɨɝɨ ɫɟɦɢɧɚɪɚ , Ʉɪɚɫɧɨɹɪɫɤ: ɂɡɞ-ɜɨ ɄȽɌɍ, ɫ. 32 83. Ƚɨɪɛɚɧɶ Ⱥ.ɇ., Ɇɢɪɤɟɫ ȿ.Ɇ. ɇɟɣɪɨɫɟɬɟɜɨɟ ɪɚɫɩɨɡɧɚɜɚɧɢɟ ɜɢɡɭɚɥɶɧɵɯ ɨɛɪɚɡɨɜ "EYE" (ɨɩɢɫɚɧɢɟ ɩɚɤɟɬɚ ɩɪɨɝɪɚɦɦ) Ʉɪɚɫɧɨɹɪɫɤ: ɂɧ-ɬ ɛɢɨɮɢɡɢɤɢ ɋɈ ɊȺɇ, 1992. 36 c. ɉɪɟɩɪɢɧɬ ʋ 193 Ȼ. 84. Ƚɨɪɛɚɧɶ Ⱥ.ɇ., Ɇɢɪɤɟɫ ȿ.Ɇ. Ɉɰɟɧɤɢ ɢ ɢɧɬɟɪɩɪɟɬɚɬɨɪɵ ɨɬɜɟɬɚ ɞɥɹ ɫɟɬɟɣ ɞɜɨɣɫɬɜɟɧɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ. ȼɵɱɢɫɥɢɬɟɥɶɧɵɣ ɰɟɧɬɪ ɋɈ ɊȺɇ ɜ ɝ. Ʉɪɚɫɧɨɹɪɫɤɟ. Ʉɪɚɫɧɨɹɪɫɤ, 1997. 24 ɫ., ɛɢɛɥɢɨɝɪ. 8 ɧɚɡɜ. (Ɋɭɤɨɩɢɫɶ ɞɟɩ. ɜ ȼɂɇɂɌɂ 25.07.97, ʋ 2511-ȼ97) 85. Ƚɨɪɛɚɧɶ Ⱥ.ɇ., Ɇɢɪɤɟɫ ȿ.Ɇ. Ɏɭɧɤɰɢɨɧɚɥɶɧɵɟ ɤɨɦɩɨɧɟɧɬɵ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚ // ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ: Ɇɚɬɟɪɢɚɥɵ III ȼɫɟɪɨɫɫɢɣɫɤɨɝɨ ɫɟɦɢɧɚɪɚ, 6-8 ɨɤɬɹɛɪɹ 1995 ɝ. ɑ. 1/ɉɨɞ ɪɟɞ. Ⱥ.ɇ.Ƚɨɪɛɚɧɹ; Ʉɪɚɫɧɨɹɪɫɤ: ɂɡɞ-ɜɨ ɄȽɌɍ, 1995. ɋ. 79-90. 86. Ƚɨɪɛɚɧɶ Ⱥ.ɇ., Ɇɢɪɤɟɫ ȿ.Ɇ., ɋɜɢɬɢɧ Ⱥ.ɉ. Ɇɟɬɨɞ ɦɭɥɶɬɢɩɥɟɬɧɵɯ ɩɨɤɪɵɬɢɣ ɢ ɟɝɨ ɢɫɩɨɥɶɡɨɜɚɧɢɟ ɞɥɹ ɩɪɟɞɫɤɚɡɚɧɢɹ ɫɜɨɣɫɬɜ ɚɬɨɦɨɜ ɢ ɦɨɥɟɤɭɥ. - ɀɭɪɧɚɥ ɮɢɡ. ɯɢɦɢɢ, 1992. Ɍ. 66, ʋ 6. ɋ. 1503-1510. 87. Ƚɨɪɛɚɧɶ Ⱥ.ɇ., Ɇɢɪɤɟɫ ȿ.Ɇ., ɋɜɢɬɢɧ Ⱥ.ɉ. ɉɨɥɭɷɦɩɢɪɢɱɟɫɤɢɣ ɦɟɬɨɞ ɤɥɚɫɫɢɮɢɤɚɰɢɢ ɚɬɨɦɨɜ ɢ ɢɧɬɟɪɩɨɥɹɰɢɢ ɢɯ ɫɜɨɣɫɬɜ. // Ɇɚɬɟɦɚɬɢɱɟɫɤɨɟ ɦɨɞɟɥɢɪɨɜɚɧɢɟ ɜ ɯɢɦɢɢ ɢ ɛɢɨɥɨɝɢɢ. ɇɨɜɵɟ ɩɨɞɯɨɞɵ. ɇɨɜɨɫɢɛɢɪɫɤ : ɇɚɭɤɚ, 1992. ɋ. 204-220. 88. Ƚɨɪɛɚɧɶ Ⱥ.ɇ., ɇɨɜɨɯɨɞɶɤɨ Ⱥ.ɘ. ɇɟɣɪɨɧɧɵɟ ɫɟɬɢ ɜ ɡɚɞɚɱɟ ɬɪɚɧɫɩɨɧɢɪɨɜɚɧɧɨɣ ɪɟɝɪɟɫɫɢɢ. ȼɬɨɪɨɣ ɋɢɛɢɪɫɤɢɣ ɤɨɧɝɪɟɫɫ ɩɨ ɉɪɢɤɥɚɞɧɨɣ ɢ ɂɧɞɭɫɬɪɢɚɥɶɧɨɣ Ɇɚɬɟɦɚɬɢɤɟ, ɩɨɫɜɹɳɟɧɧɵɣ ɩɚɦɹɬɢ Ⱥ.Ⱥ.Ʌɹɩɭɧɨɜɚ (1911-1973), Ⱥ.ɉ.ȿɪɲɨɜɚ (1931-1988) ɢ ɂ.Ⱥ.ɉɨɥɟɬɚɟɜɚ (1915-1983). ɇɨɜɨɫɢɛɢɪɫɤ, ɢɸɧɶ 1996. Ɍɟɡɢɫɵ ɞɨɤɥɚɞɨɜ, ɱɚɫɬɶ 2. ɂɡɞ. ɂɧɫɬɢɬɭɬɚ ɦɚɬɟɦɚɬɢɤɢ ɋɈ ɊȺɇ. ɋ. 160-161. 89. Ƚɨɪɛɚɧɶ Ⱥ.ɇ., ɇɨɜɨɯɨɞɶɤɨ Ⱥ.ɘ., ɐɚɪɟɝɨɪɨɞɰɟɜ ȼ.Ƚ. “ɇɟɣɪɨɫɟɬɟɜɚɹ ɪɟɚɥɢɡɚɰɢɹ ɬɪɚɧɫɩɨɧɢɪɨɜɚɧɧɨɣ ɡɚɞɚɱɢ ɥɢɧɟɣɧɨɣ ɪɟɝɪɟɫɫɢɢ” .- 4 ȼɫɟɪɨɫɫɢɣɫɤɢɣ ɪɚɛɨɱɢɣ ɫɟɦɢɧɚɪ “ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ”, 5-7 ɨɤɬɹɛɪɹ 1996 ɝ., Ɍɟɡɢɫɵ ɞɨɤɥɚɞɨɜ. Ʉɪɚɫɧɨɹɪɫɤ: ɢɡɞ. ɄȽɌɍ, 1996, ɫ. 37-39. 90. Ƚɨɪɛɚɧɶ Ⱥ.ɇ., Ɋɨɫɫɢɟɜ Ⱦ.Ⱥ. ɇɟɣɪɨɧɧɵɟ ɫɟɬɢ ɧɚ ɩɟɪɫɨɧɚɥɶɧɨɦ ɤɨɦɩɶɸɬɟɪɟ // ɇɨɜɨɫɢɛɢɪɫɤ: ɇɚɭɤɚ, 1996.- 276 ɫ. 91. Ƚɨɪɛɚɧɶ Ⱥ.ɇ., Ɋɨɫɫɢɟɜ Ⱦ.Ⱥ., Ȼɭɬɚɤɨɜɚ ȿ.ȼ., Ƚɢɥɟɜ ɋ.ȿ., Ƚɨɥɨɜɟɧɤɢɧ ɋ.ȿ., Ⱦɨɝɚɞɢɧ ɋ.Ⱥ., Ʉɨɱɟɧɨɜ Ⱦ.Ⱥ., Ɇɚɫɥɟɧɢɤɨɜɚ ȿ.ȼ., Ɇɚɬɸɲɢɧ Ƚ.ȼ., Ɇɢɪɤɟɫ ȿ.Ɇ., ɇɚɡɚɪɨɜ Ȼ.ȼ., ɇɨɡɞɪɚɱɟɜ Ʉ.Ƚ., ɋɚɜɱɟɧɤɨ Ⱥ.Ⱥ., ɋɦɢɪɧɨɜɚ ɋ.ȼ., ɑɟɪɬɵɤɨɜ ɉ.Ⱥ., ɒɭɥɶɦɚɧ ȼ.Ⱥ. Ɇɟɞɢɰɢɧɫɤɢɟ ɢ ɮɢɡɢɨɥɨɝɢɱɟɫɤɢɟ ɩɪɢɦɟɧɟɧɢɹ ɧɟɣɪɨɢɦɢɬɚɬɨɪɚ "MultiNeuron" // ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ: Ɇɚɬɟɪɢɚɥɵ III ȼɫɟɪɨɫɫɢɣɫɤɨɝɨ ɫɟɦɢɧɚɪɚ, 6-8 ɨɤɬɹɛɪɹ 1995 ɝ. ɑ. 1/ɉɨɞ ɪɟɞ. Ⱥ.ɇ.Ƚɨɪɛɚɧɹ; Ʉɪɚɫɧɨɹɪɫɤ: ɂɡɞ-ɜɨ ɄȽɌɍ, 1995. ɋ. 101-113. 92. Ƚɨɪɛɚɧɶ Ⱥ.ɇ., Ɋɨɫɫɢɟɜ Ⱦ.Ⱥ., Ƚɨɥɨɜɟɧɤɢɧ ɋ.ȿ., ɒɭɥɶɦɚɧ ȼ.Ⱥ., Ɇɚɬɸɲɢɧ Ƚ.ȼ. ɇɟɣɪɨɫɢɫɬɟɦɚ ɩɪɨɝɧɨɡɢɪɨɜɚɧɢɹ ɨɫɥɨɠɧɟɧɢɣ ɢɧɮɚɪɤɬɚ ɦɢɨɤɚɪɞɚ. ȼɬɨɪɨɣ ɋɢɛɢɪɫɤɢɣ ɤɨɧɝɪɟɫɫ ɩɨ ɉɪɢɤɥɚɞɧɨɣ ɢ ɂɧɞɭɫɬɪɢɚɥɶɧɨɣ Ɇɚɬɟɦɚɬɢɤɟ, ɩɨɫɜɹɳɟɧɧɵɣ ɩɚɦɹɬɢ Ⱥ.Ⱥ.Ʌɹɩɭɧɨɜɚ (19111973), Ⱥ.ɉ.ȿɪɲɨɜɚ (1931-1988) ɢ ɂ.Ⱥ.ɉɨɥɟɬɚɟɜɚ (1915-1983). ɇɨɜɨɫɢɛɢɪɫɤ, ɢɸɧɶ 1996. Ɍɟɡɢɫɵ ɞɨɤɥɚɞɨɜ, ɱɚɫɬɶ 1. ɂɡɞ. ɂɧɫɬɢɬɭɬɚ ɦɚɬɟɦɚɬɢɤɢ ɋɈ ɊȺɇ. ɋ. 40. 93. Ƚɨɪɛɚɧɶ Ⱥ.ɇ., Ɋɨɫɫɢɟɜ Ⱦ.Ⱥ., Ʉɨɱɟɧɨɜ Ⱦ.Ⱥ. ɉɪɢɦɟɧɟɧɢɟ ɫɚɦɨɨɛɭɱɚɸɳɢɯɫɹ ɧɟɣɪɨɫɟɬɟɜɵɯ ɩɪɨɝɪɚɦɦ. Ɋɚɡɞɟɥ 1. ȼɜɟɞɟɧɢɟ ɜ ɧɟɣɪɨɩɪɨɝɪɚɦɦɵ: ɍɱɟɛɧɨ-ɦɟɬɨɞɢɱɟɫɤɨɟ ɩɨɫɨɛɢɟ ɞɥɹ ɫɬɭɞɟɧɬɨɜ ɫɩɟɰɢɚɥɶɧɨɫɬɟɣ 22.04 ɢ 55.28.00 ɜɫɟɯ ɮɨɪɦ ɨɛɭɱɟɧɢɹ. Ʉɪɚɫɧɨɹɪɫɤ: ɂɡɞ-ɜɨ ɋɌɂ, 1994. 24 ɫ. 94. Ƚɨɪɛɚɧɶ Ⱥ.ɇ., ɋɟɧɚɲɨɜɚ Ɇ.ɘ. Ɇɟɬɨɞ ɨɛɪɚɬɧɨɝɨ ɪɚɫɩɪɨɫɬɪɚɧɟɧɢɹ ɬɨɱɧɨɫɬɢ. ɉɪɟɩɪɢɧɬ ȼɐ ɋɈ ɊȺɇ, Ʉɪɚɫɧɨɹɪɫɤ, 1996, ʋ17, 8 ɫ. 95. Ƚɨɪɛɚɧɶ Ⱥ.ɇ., ɋɟɧɚɲɨɜɚ Ɇ.ɘ. ɉɨɝɪɟɲɧɨɫɬɢ ɜ ɧɟɣɪɨɧɧɵɯ ɫɟɬɹɯ. ȼɵɱɢɫɥɢɬɟɥɶɧɵɣ ɰɟɧɬɪ ɋɈ ɊȺɇ ɜ ɝ. Ʉɪɚɫɧɨɹɪɫɤɟ. Ʉɪɚɫɧɨɹɪɫɤ, 1997. 38 ɫ., ɛɢɛɥɢɨɝɪ. 8 ɧɚɡɜ. (Ɋɭɤɨɩɢɫɶ ɞɟɩ. ɜ ȼɂɇɂɌɂ 25.07.97, ʋ 2509-ȼ97)
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96. Ƚɨɪɛɚɧɶ Ⱥ.ɇ., ɋɟɧɚɲɨɜɚ Ɇ.ɘ. ɉɨɝɪɟɲɧɨɫɬɢ ɜ ɧɟɣɪɨɧɧɵɯ ɫɟɬɹɯ. Ɋɭɤɨɩɢɫɶ ɞɟɩ. ɜ ȼɂɇɂɌɂ, 25.07.97, ʋ2509-ȼ97, 1997, 38 ɫ. 97. Ƚɨɪɛɚɧɶ Ⱥ.ɇ., Ɏɪɢɞɟɧɛɟɪɝ ȼ.ɂ. ɇɨɜɚɹ ɢɝɪɭɲɤɚ ɱɟɥɨɜɟɱɟɫɬɜɚ. ɆɂɊ ɉɄ, 1993, ʋ 9. ɋ. 111113. 98. Ƚɨɪɛɚɧɶ Ⱥ.ɇ., ɏɥɟɛɨɩɪɨɫ Ɋ.Ƚ. Ⱦɟɦɨɧ Ⱦɚɪɜɢɧɚ. ɂɞɟɹ ɨɩɬɢɦɚɥɶɧɨɫɬɢ ɢ ɟɫɬɟɫɬɜɟɧɧɵɣ ɨɬɛɨɪ. Ɇ.: ɇɚɭɤɚ, 1988. 208 ɫ. 99. Ƚɨɪɞɢɟɧɤɨ ɉ.ȼ. ɋɬɪɚɬɟɝɢɢ ɤɨɧɬɪɚɫɬɢɪɨɜɚɧɢɹ // ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ. Ɍɟɡɢɫɵ ɞɨɤɥɚɞɨɜ 5 ȼɫɟɪɨɫɫɢɣɫɤɨɝɨ ɫɟɦɢɧɚɪɚ, 3-5 ɨɤɬɹɛɪɹ 1997 ɝ. / ɉɨɞ ɪɟɞ. Ⱥ.ɇ.Ƚɨɪɛɚɧɹ. Ʉɪɚɫɧɨɹɪɫɤ: ɢɡɞ. ɄȽɌɍ, 1997. ɋ. 69. 100. Ƚɪɚɧɨɜɫɤɚɹ Ɋ.Ɇ., Ȼɟɪɟɡɧɚɹ ɂ.ə. ɂɧɬɭɢɰɢɹ ɢ ɢɫɤɭɫɫɬɜɟɧɧɵɣ ɢɧɬɟɥɥɟɤɬ.- Ʌ.: ɅȽɍ, 1991.- 272 ɫ. 101. Ƚɭɬɱɢɧ ɂ.Ȼ., Ʉɭɡɢɱɟɜ Ⱥ.ɋ. Ȼɢɨɧɢɤɚ ɢ ɧɚɞɟɠɧɨɫɬɶ. Ɇ.: ɇɚɭɤɚ, 1967. 102. Ⱦɟɦɢɞɟɧɤɨ ȿ.Ɂ. Ʌɢɧɟɣɧɚɹ ɢ ɧɟɥɢɧɟɣɧɚɹ ɪɟɝɪɟɫɫɢɹ.- Ɇ.: Ɏɢɧɚɧɫɵ ɢ ɫɬɚɬɢɫɬɢɤɚ, 1981.- 302 ɫ. 103. Ⱦɟɧɧɢɫ Ⱦɠ. ɦɥ., ɒɧɚɛɟɥɶ Ɋ. ɑɢɫɥɟɧɧɵɟ ɦɟɬɨɞɵ ɛɟɡɭɫɥɨɜɧɨɣ ɨɩɬɢɦɢɡɚɰɢɢ ɢ ɪɟɲɟɧɢɹ ɧɟɥɢɧɟɣɧɵɯ ɭɪɚɜɧɟɧɢɣ. Ɇ.: Ɇɢɪ, 1988. 440 ɫ. 104. Ⱦɟɪɬɨɭɡɨɫ Ɇ. ɉɨɪɨɝɨɜɚɹ ɥɨɝɢɤɚ. - Ɇ.: Ɇɢɪ, 1967. 105. Ⱦɠɨɪɞɠ Ɏ. Ɇɨɡɝ ɤɚɤ ɜɵɱɢɫɥɢɬɟɥɶɧɚɹ ɦɚɲɢɧɚ. Ɇ. ɂɡɞ-ɜɨ ɢɧɨɫɬɪ. ɥɢɬ., 1963. 528 ɫ. 106. Ⱦɢɚɧɤɨɜɚ ȿ.ȼ., Ʉɜɢɱɚɧɫɤɢɣ Ⱥ.ȼ., Ɇɭɯɚɦɚɞɢɟɜ Ɋ.Ɏ., Ɇɭɯɚɦɚɞɢɟɜɚ Ɍ.Ⱥ., Ɍɟɪɟɯɨɜ ɋ.Ⱥ. ɇɟɤɨɬɨɪɵɟ ɫɜɨɣɫɬɜɚ ɧɟɥɢɧɟɣɧɵɯ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ. ɂɫɫɥɟɞɨɜɚɧɢɟ ɬɪɟɯɧɟɣɪɨɧɧɨɣ ɦɨɞɟɥɢ// Ɍɟɡɢɫɵ ɞɨɤɥɚɞɨɜ III ȼɫɟɪɨɫɢɣɫɤɨɝɨ ɫɟɦɢɧɚɪɚ "ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ", Ʉɪɚɫɧɨɹɪɫɤ: ɂɡɞ-ɜɨ ɄȽɌɍ, 1995, ɫ. 86 107. Ⱦɢɫɤɭɫɢɹ ɨ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɚɯ / ɉɨɞ ɪɟɞ. ȼ.ɂ.Ʉɪɸɤɨɜɚ. ɉɭɳɢɧɨ, 1988. 108. Ⱦɨɪɨɮɟɸɤ Ⱥ.Ⱥ. Ⱥɥɝɨɪɢɬɦɵ ɚɜɬɨɦɚɬɢɱɟɫɤɨɣ ɤɥɚɫɫɢɮɢɤɚɰɢɢ (ɨɛɡɨɪ). - Ⱥɜɬɨɦɚɬɢɤɚ ɢ ɬɟɥɟɦɟɯɚɧɢɤɚ, 1971, ʋ 12. ɋ. 78-113. 109. Ⱦɨɪɪɟɪ Ɇ.Ƚ., Ƚɨɪɛɚɧɶ Ⱥ.ɇ., Ʉɨɩɵɬɨɜ Ⱥ.Ƚ., Ɂɟɧɤɢɧ ȼ.ɂ. ɉɫɢɯɨɥɨɝɢɱɟɫɤɚɹ ɢɧɬɭɢɰɢɹ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ // ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ: Ɇɚɬɟɪɢɚɥɵ III ȼɫɟɪɨɫɫɢɣɫɤɨɝɨ ɫɟɦɢɧɚɪɚ, 6-8 ɨɤɬɹɛɪɹ 1995 ɝ. ɑ. 1/ɉɨɞ ɪɟɞ. Ⱥ.ɇ.Ƚɨɪɛɚɧɹ; Ʉɪɚɫɧɨɹɪɫɤ: ɂɡɞ-ɜɨ ɄȽɌɍ, 1995. ɋ. 114-127. 110. Ⱦɭɞɚ Ɋ., ɏɚɪɬ ɉ. Ɋɚɫɩɨɡɧɚɜɚɧɢɟ ɨɛɪɚɡɨɜ ɢ ɚɧɚɥɢɡ ɫɰɟɧ.Ɇ.: Ɇɢɪ, 1976.- 512 ɫ. 111. Ⱦɭɧɢɧ-Ȼɚɪɤɨɜɫɤɢɣ ȼ.Ʌ. ɂɧɮɨɪɦɚɰɢɨɧɧɵɟ ɩɪɨɰɟɫɫɵ ɜ ɧɟɣɪɨɧɧɵɯ ɫɬɪɭɤɬɭɪɚɯ. - Ɇ.: ɇɚɭɤɚ, 1978. 112. Ⱦɭɧɢɧ-Ȼɚɪɤɨɜɫɤɢɣ ȼ.Ʌ. ɇɟɣɪɨɤɢɛɟɪɧɟɬɢɤɚ, ɧɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ, ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɵ / ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɇɨɜɨɫɢɛɢɪɫɤ: ɇɚɭɤɚ, ɋɢɛɢɪɫɤɚɹ ɢɡɞɚɬɟɥɶɫɤɚɹ ɮɢɪɦɚ ɊȺɇ, 1998. 113. Ⱦɭɧɢɧ-Ȼɚɪɤɨɜɫɤɢɣ ȼ.Ʌ. ɇɟɣɪɨɧɧɵɟ ɫɯɟɦɵ ɚɫɫɨɰɢɚɬɢɜɧɨɣ ɩɚɦɹɬɢ // Ɇɨɞɟɥɢɪɨɜɚɧɢɟ ɜɨɡɛɭɞɢɦɵɯ ɫɬɪɭɤɬɭɪ. ɉɭɳɢɧɨ: ɢɡɞ.ɐȻɂ, 1975. ɋ.90-141. 114. Ⱦɸɤ ȼ.Ⱥ. Ʉɨɦɩɶɸɬɟɪɧɚɹ ɩɫɢɯɨɞɢɚɝɧɨɫɬɢɤɚ.ɋɚɧɤɬ-ɉɟɬɟɪɛɭɪɝ: Ȼɪɚɬɫɬɜɨ, 1994.- 364 ɫ. 115. ȿɪɦɚɤɨɜ ɋ.ȼ., Ɇɵɲɨɜ Ʉ.Ⱦ., Ɉɯɨɧɢɧ ȼ.Ⱥ. Ʉ ɜɨɩɪɨɫɭ ɨ ɦɚɬɟɦɚɬɢɱɟɫɤɨɦ ɦɨɞɟɥɢɪɨɜɚɧɢɢ ɛɚɡɨɜɨɝɨ ɩɪɢɧɰɢɩɚ ɦɵɲɥɟɧɢɹ ɱɟɥɨɜɟɤɚ. ɉɪɟɩɪɢɧɬ ɂȻɎ ɋɈ ɊȺɇ, Ʉɪɚɫɧɨɹɪɫɤ, 1992, ʋ173Ȼ, 36 ɫ. 116. ɀɭɪɚɜɥɟɜ ɘ.ɂ. Ɉɛ ɚɥɝɟɛɪɚɢɱɟɫɤɨɦ ɩɨɞɯɨɞɟ ɤ ɪɟɲɟɧɢɸ ɡɚɞɚɱ ɪɚɫɩɨɡɧɚɜɚɧɢɹ ɢ ɤɥɚɫɫɢɮɢɤɚɰɢɢ // ɉɪɨɛɥɟɦɵ ɤɢɛɟɪɧɟɬɢɤɢ.- Ɇ.: ɇɚɭɤɚ, 1978, ɜɵɩ. 33.- ɋ. 5-68. 117. Ɂɚɝɨɪɭɣɤɨ ɇ.Ƚ. Ɇɟɬɨɞɵ ɪɚɫɩɨɡɧɚɜɚɧɢɹ ɢ ɢɯ ɩɪɢɦɟɧɟɧɢɟ.Ɇ.: ɋɨɜ. ɪɚɞɢɨ, 1972.- 206 ɫ. 118. Ɂɚɝɨɪɭɣɤɨ ɇ.Ƚ., ȿɥɤɢɧɚ ȼ.ɇ., Ʌɛɨɜ Ƚ.ɋ. Ⱥɥɝɨɪɢɬɦɵ ɨɛɧɚɪɭɠɟɧɢɹ ɷɦɩɢɪɢɱɟɫɤɢɯ ɡɚɤɨɧɨɦɟɪɧɨɫɬɟɣ.- ɇɨɜɨɫɢɛɢɪɫɤ: ɇɚɭɤɚ, 1985.- 110 ɫ. 119. Ɂɚɯɚɪɨɜɚ Ʌ.Ȼ., ɉɨɥɨɧɫɤɚɹ Ɇ.Ƚ., ɋɚɜɱɟɧɤɨ Ⱥ.Ⱥ. ɢ ɞɪ. Ɉɰɟɧɤɚ ɚɧɬɪɨɩɨɥɨɝɢɱɟɫɤɨɝɨ ɧɚɩɪɹɠɟɧɢɹ ɩɪɢɲɥɨɝɨ ɧɚɫɟɥɟɧɢɹ ɩɪɨɦɵɲɥɟɧɧɨɣ ɡɨɧɵ Ɂɚɩɨɥɹɪɶɹ (ɛɢɨɥɨɝɢɱɟɫɤɢɣ ɚɫɩɟɤɬ) // ɉɪɟɩɪɢɧɬ ɂȻɎ ɋɈ ɊȺɇ. Ʉɪɚɫɧɨɹɪɫɤ, 1989. ʋ 110Ȼ. 52 ɫ. 120. Ɂɚɯɚɪɨɜɚ Ʌ.Ɇ., Ʉɢɫɟɥɟɜɚ ɇ.ȿ. Ɇɭɱɧɢɤ ɂ.Ȼ., ɉɟɬɪɨɜɫɤɢɣ Ⱥ.Ɇ., ɋɜɟɪɱɢɧɫɤɚɹ Ɋ.Ȼ. Ⱥɧɚɥɢɡ ɪɚɡɜɢɬɢɹ ɝɢɩɟɪɬɨɧɢɱɟɫɤɨɣ ɛɨɥɟɡɧɢ ɩɨ ɷɦɩɢɪɢɱɟɫɤɢɦ ɞɚɧɧɵɦ. - Ⱥɜɬɨɦɚɬɢɤɚ ɢ ɬɟɥɟɦɟɯɚɧɢɤɚ, 1977, ʋ 9. ɋ. 114-122. 121. ɂɜɚɯɧɟɧɤɨ Ⱥ.Ƚ. "ɉɟɪɫɟɩɬɪɨɧɵ". - Ʉɢɟɜ: ɇɚɭɤɨɜɚ ɞɭɦɤɚ, 1974. 122. ɂɜɚɯɧɟɧɤɨ Ⱥ.Ƚ. ɋɚɦɨɨɛɭɱɚɸɳɢɟɫɹ ɫɢɫɬɟɦɵ ɪɚɫɩɨɡɧɚɜɚɧɢɹ ɢ ɚɜɬɨɦɚɬɢɱɟɫɤɨɝɨ ɪɟɝɭɥɢɪɨɜɚɧɢɹ.- Ʉɢɟɜ: Ɍɟɯɧɢɤɚ, 1969.- 392 ɫ. 123. ɂɫɤɭɫɫɬɜɟɧɧɵɣ ɢɧɬɟɥɥɟɤɬ: ȼ 3-ɯ ɤɧ. Ʉɧ. 1. ɋɢɫɬɟɦɵ ɨɛɳɟɧɢɹ ɢ ɷɤɫɩɟɪɬɧɵɟ ɫɢɫɬɟɦɵ: ɋɩɪɚɜɨɱɧɢɤ / ɩɨɞ ɪɟɞ. ɗ.ȼ.ɉɨɩɨɜɚ.- Ɇ.: Ɋɚɞɢɨ ɢ ɫɜɹɡɶ, 1990.- 464 ɫ. 124. ɂɫɤɭɫɫɬɜɟɧɧɵɣ ɢɧɬɟɥɥɟɤɬ: ȼ 3-ɯ ɤɧ. Ʉɧ. 2. Ɇɨɞɟɥɢ ɢ ɦɟɬɨɞɵ: ɋɩɪɚɜɨɱɧɢɤ / ɩɨɞ ɪɟɞ. Ⱦ.Ⱥ. ɉɨɫɩɟɥɨɜɚ.- Ɇ.: Ɋɚɞɢɨ ɢ ɫɜɹɡɶ, 1990.- 304 ɫ. 125. ɂɬɨɝɢ ɧɚɭɤɢ ɢ ɬɟɯɧɢɤɢ. ɋɟɪ. "Ɏɢɡ. ɢ Ɇɚɬɟɦ. ɦɨɞɟɥɢ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ" /ɉɨɞ ɪɟɞ. Ⱥ.Ⱥ.ȼɟɞɟɧɨɜɚ. - Ɇ.: ɂɡɞ-ɜɨ ȼɂɇɂɌɂ, 1990-92 - Ɍ. 1-5.
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126. Ʉɜɢɱɚɧɫɤɢɣ Ⱥ.ȼ., Ⱦɢɚɧɤɨɜɚ ȿ.ȼ., Ɇɭɯɚɦɚɞɢɟɜ Ɋ.Ɏ., Ɇɭɯɚɦɚɞɢɟɜɚ Ɍ.Ⱥ. ɉɪɨɝɪɚɦɦɧɵɣ ɩɪɨɞɭɤɬ NNN ɞɥɹ ɢɫɫɥɟɞɨɜɚɧɢɹ ɫɜɨɣɫɬɜ ɧɟɥɢɧɟɣɧɵɯ ɫɟɬɟɣ ɜ ɤɨɦɩɶɸɬɟɪɧɨɦ ɷɤɫɩɟɪɢɦɟɧɬɟ // Ɍɟɡɢɫɵ ɞɨɤɥɚɞɨɜ III ȼɫɟɪɨɫɢɣɫɤɨɝɨ ɫɟɦɢɧɚɪɚ "ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ", Ʉɪɚɫɧɨɹɪɫɤ: ɂɡɞ-ɜɨ ɄȽɌɍ, 1995, ɫ. 10 127. Ʉɟɧɞɚɥɥ Ɇ., ɋɬɶɸɚɪɬ Ⱥ. ɋɬɚɬɢɫɬɢɱɟɫɤɢɟ ɜɵɜɨɞɵ ɢ ɫɜɹɡɢ. 11 Ɇ.: ɇɚɭɤɚ, 1973.- 900 ɫ. 128. Ʉɢɪɞɢɧ Ⱥ.ɇ., ɇɨɜɨɯɨɞɶɤɨ Ⱥ.ɘ., ɐɚɪɟɝɨɪɨɞɰɟɜ ȼ.Ƚ. ɋɤɪɵɬɵɟ ɩɚɪɚɦɟɬɪɵ ɢ ɬɪɚɧɫɩɨɧɢɪɨɜɚɧɧɚɹ ɪɟɝɪɟɫɫɢɹ/ ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɇɨɜɨɫɢɛɢɪɫɤ: ɇɚɭɤɚ, ɋɢɛɢɪɫɤɚɹ ɢɡɞɚɬɟɥɶɫɤɚɹ ɮɢɪɦɚ ɊȺɇ, 1998. 129. Ʉɨɫɬɸɤ Ɏ.Ɏ. ɂɧɮɚɪɤɬ ɦɢɨɤɚɪɞɚ. Ʉɪɚɫɧɨɹɪɫɤ: Ɉɮɫɟɬ, 1993. 224 ɫ. 130. Ʉɨɯɨɧɟɧ Ɍ. Ⱥɫɫɨɰɢɚɬɢɜɧɚɹ ɩɚɦɹɬɶ. - Ɇ.: Ɇɢɪ, 1980. 131. Ʉɨɯɨɧɟɧ Ɍ. Ⱥɫɫɨɰɢɚɬɢɜɧɵɟ ɡɚɩɨɦɢɧɚɸɳɢɟ ɭɫɬɪɨɣɫɬɜɚ. - Ɇ.: Ɇɢɪ, 1982. 132. Ʉɨɱɟɧɨɜ Ⱦ.Ⱥ., Ɇɢɪɤɟɫ ȿ.Ɇ. "Ɉɩɪɟɞɟɥɟɧɢɟ ɱɭɜɫɬɜɢɬɟɥɶɧɨɫɬɢ ɧɟɣɪɨɫɟɬɢ ɤ ɢɡɦɟɧɟɧɢɸ ɜɯɨɞɧɵɯ ɫɢɝɧɚɥɨɜ", ɬɟɡɢɫɵ ɞɨɤɥɚɞɨɜ III ȼɫɟɪɨɫɢɣɫɤɨɝɨ ɫɟɦɢɧɚɪɚ "ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ", Ʉɪɚɫɧɨɹɪɫɤ: ɂɡɞ-ɜɨ ɄȽɌɍ, ɫ. 61 133. Ʉɨɱɟɧɨɜ Ⱦ.Ⱥ., Ɇɢɪɤɟɫ ȿ.Ɇ. "ɋɢɧɬɟɡ ɭɩɪɚɜɥɹɸɳɢɯ ɜɨɡɞɟɣɫɬɜɢɣ", ɬɟɡɢɫɵ ɞɨɤɥɚɞɨɜ III ȼɫɟɪɨɫɢɣɫɤɨɝɨ ɫɟɦɢɧɚɪɚ "ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ", Ʉɪɚɫɧɨɹɪɫɤ: ɂɡɞ-ɜɨ ɄȽɌɍ, ɫ. 31 134. Ʉɨɱɟɧɨɜ Ⱦ.Ⱥ., Ɇɢɪɤɟɫ ȿ.Ɇ., Ɋɨɫɫɢɟɜ Ⱦ.Ⱥ. Ⱥɜɬɨɦɚɬɢɱɟɫɤɚɹ ɩɨɞɫɬɪɨɣɤɚ ɜɯɨɞɧɵɯ ɞɚɧɧɵɯ ɞɥɹ ɩɨɥɭɱɟɧɢɹ ɬɪɟɛɭɟɦɨɝɨ ɨɬɜɟɬɚ ɧɟɣɪɨɫɟɬɢ // ɉɪɨɛɥɟɦɵ ɢɧɮɨɪɦɚɬɢɡɚɰɢɢ ɪɟɝɢɨɧɚ. Ɍɪɭɞɵ ɦɟɠɪɟɝɢɨɧɚɥɶɧɨɣ ɤɨɧɮɟɪɟɧɰɢɢ (Ʉɪɚɫɧɨɹɪɫɤ, 27-29 ɧɨɹɛɪɹ 1995 ɝ.). Ʉɪɚɫɧɨɹɪɫɤ, 1995.ɋ.156. 135. Ʉɨɱɟɧɨɜ Ⱦ.Ⱥ., Ɇɢɪɤɟɫ ȿ.Ɇ., Ɋɨɫɫɢɟɜ Ⱦ.Ⱥ. Ɇɟɬɨɞ ɩɨɞɫɬɪɨɣɤɢ ɩɚɪɚɦɟɬɪɨɜ ɩɪɢɦɟɪɚ ɞɥɹ ɩɨɥɭɱɟɧɢɹ ɬɪɟɛɭɟɦɨɝɨ ɨɬɜɟɬɚ ɧɟɣɪɨɫɟɬɢ // ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ. Ɍɟɡ. ɞɨɤɥ. ȼɫɟɪɨɫɫɢɣɫɤɨɝɨ ɪɚɛɨɱɟɝɨ ɫɟɦɢɧɚɪɚ 7 - 10 ɨɤɬɹɛɪɹ 1994 ɝ. Ʉɪɚɫɧɨɹɪɫɤ.- 1994.- ɋ.39. 136. Ʉɨɱɟɧɨɜ Ⱦ.Ⱥ., Ɋɨɫɫɢɟɜ Ⱦ.Ⱥ. Ⱥɩɩɪɨɤɫɢɦɚɰɢɹ ɮɭɧɤɰɢɣ ɤɥɚɫɫɚ ɋ[a,b] ɧɟɣɪɨɫɟɬɟɜɵɦɢ ɩɪɟɞɢɤɬɨɪɚɦɢ // Ɍɟɡɢɫɵ ɞɨɤɥɚɞɨɜ ɪɚɛɨɱɟɝɨ ɫɟɦɢɧɚɪɚ "ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɵ", Ʉɪɚɫɧɨɹɪɫɤ, 8-11 ɨɤɬɹɛɪɹ 1993 ɝ., Ʉɪɚɫɧɨɹɪɫɤ.- 1993.- ɋ.13. 137. Ʉɪɚɣɡɦɟɪ Ʌ.ɉ., Ɇɚɬɸɯɢɧ ɋ.Ⱥ., Ɇɚɣɨɪɤɢɧ ɋ.Ƚ. ɉɚɦɹɬɶ ɤɢɛɟɪɧɟɬɢɱɟɫɤɢɯ ɫɢɫɬɟɦ (Ɉɫɧɨɜɵ ɦɧɟɦɨɥɨɝɢɢ). - Ɇ.: ɋɨɜ. ɪɚɞɢɨ, 1971. 138. Ʉɭɫɫɭɥɶ ɗ.Ɇ., Ȼɚɣɞɵɤ Ɍ.ɇ. Ɋɚɡɪɚɛɨɬɤɚ ɚɪɯɢɬɟɤɬɭɪɵ ɧɟɣɪɨɩɨɞɨɛɧɨɣ ɫɟɬɢ ɞɥɹ ɪɚɫɩɨɡɧɚɜɚɧɢɹ ɮɨɪɦɵ ɨɛɴɟɤɬɨɜ ɧɚ ɢɡɨɛɪɚɠɟɧɢɢ // Ⱥɜɬɨɦɚɬɢɤɚ.- 1990.- ʋ 5.- ɋ. 56-61. 139. Ʉɭɲɚɤɨɜɫɤɢɣ Ɇ.ɋ. Ⱥɪɢɬɦɢɢ ɫɟɪɞɰɚ. ɋɚɧɤɬ-ɉɟɬɟɪɛɭɪɝ: Ƚɢɩɩɨɤɪɚɬ, 1992. 544 ɫ. 140. Ʌɛɨɜ Ƚ.ɋ. Ɇɟɬɨɞɵ ɨɛɪɚɛɨɬɤɢ ɪɚɡɧɨɬɢɩɧɵɯ ɷɤɫɩɟɪɢɦɟɧɬɚɥɶɧɵɯ ɞɚɧɧɵɯ.- ɇɨɜɨɫɢɛɢɪɫɤ: ɇɚɭɤɚ, 1981.- 157 ɫ. 141. Ʌɨɭɥɢ Ⱦ., Ɇɚɤɫɜɟɥɥ Ⱥ. Ɏɚɤɬɨɪɧɵɣ ɚɧɚɥɢɡ ɤɚɤ ɫɬɚɬɢɫɬɢɱɟɫɤɢɣ ɦɟɬɨɞ.- Ɇ.: Ɇɢɪ, 1967.- 144 ɫ. 142. Ɇɚɡɭɪɨɜ ȼ.Ⱦ. Ɇɟɬɨɞ ɤɨɦɢɬɟɬɨɜ ɜ ɡɚɞɚɱɚɯ ɨɩɬɢɦɢɡɚɰɢɢ ɢ ɤɥɚɫɫɢɮɢɤɚɰɢɢ.- Ɇ.: ɇɚɭɤɚ, Ƚɥ. ɪɟɞ. ɮɢɡ.-ɦɚɬ. ɥɢɬ., 1990. 248 ɫ. 143. ɆɚɤɄɚɥɥɨɤ ɍ.ɋ., ɉɢɬɬɫ ȼ. Ʌɨɝɢɱɟɫɤɨɟ ɢɫɱɢɫɥɟɧɢɟ ɢɞɟɣ, ɨɬɧɨɫɹɳɢɯɫɹ ɤ ɧɟɪɜɧɨɣ ɚɤɬɢɜɧɨɫɬɢ // ɇɟɣɪɨɤɨɦɩɶɸɬɟɪ, 1992. ʋ|3, 4. ɋ. 40-53. 144. Ɇɚɫɚɥɨɜɢɱ Ⱥ.ɂ. Ɉɬ ɧɟɣɪɨɧɚ ɤ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɭ // ɀɭɪɧɚɥ ɞɨɤɬɨɪɚ Ⱦɨɛɛɚ.- 1992.- N.1.ɋ.20-24. 145. Ɇɢɧɫɤɢɣ Ɇ., ɉɚɣɩɟɪɬ ɋ. ɉɟɪɫɟɩɬɪɨɧɵ. - Ɇ.: Ɇɢɪ, 1971. 146. Ɇɢɪɤɟɫ ȿ.Ɇ. Ƚɥɨɛɚɥɶɧɵɟ ɢ ɥɨɤɚɥɶɧɵɟ ɨɰɟɧɤɢ ɞɥɹ ɫɟɬɟɣ ɞɜɨɣɫɬɜɟɧɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ. Ɍɟɡɢɫɵ ɞɨɤɥɚɞɨɜ III ȼɫɟɪɨɫɢɣɫɤɨɝɨ ɫɟɦɢɧɚɪɚ "ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ", Ʉɪɚɫɧɨɹɪɫɤ: ɂɡɞ-ɜɨ ɄȽɌɍ, ɫɫ. 76-77 147. Ɇɢɪɤɟɫ ȿ.Ɇ. ɂɫɩɨɥɶɡɨɜɚɧɢɟ ɜɟɫɨɜ ɩɪɢɦɟɪɨɜ ɩɪɢ ɨɛɭɱɟɧɢɢ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ. ɬɟɡɢɫɵ ɞɨɤɥɚɞɨɜ III ȼɫɟɪɨɫɫɢɣɫɤɨɝɨ ɫɟɦɢɧɚɪɚ "ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ", Ʉɪɚɫɧɨɹɪɫɤ: ɂɡɞ-ɜɨ ɄȽɌɍ, ɫ. 75 148. Ɇɢɪɤɟɫ ȿ.Ɇ. Ʌɨɝɢɱɟɫɤɢ ɩɪɨɡɪɚɱɧɵɟ ɧɟɣɪɨɧɧɵɟ ɫɟɬɢ ɢ ɩɪɨɢɡɜɨɞɫɬɜɨ ɹɜɧɵɯ ɡɧɚɧɢɣ ɢɡ ɞɚɧɧɵɯ / ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɇɨɜɨɫɢɛɢɪɫɤ: ɇɚɭɤɚ, ɋɢɛɢɪɫɤɚɹ ɢɡɞɚɬɟɥɶɫɤɚɹ ɮɢɪɦɚ ɊȺɇ, 1998. 149. Ɇɢɪɤɟɫ ȿ.Ɇ. ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɞɪɭɝɢɟ ɧɚɭɤɢ // ȼɟɫɬɧɢɤ ɄȽɌɍ, 1996, ɜɵɩ. 6, ɫ.5-33. 150. Ɇɢɪɤɟɫ ȿ.Ɇ. ɇɟɣɪɨɧɧɵɟ ɫɟɬɢ ɚɫɫɨɰɢɚɬɢɜɧɨɣ ɩɚɦɹɬɢ / ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɇɨɜɨɫɢɛɢɪɫɤ: ɇɚɭɤɚ, ɋɢɛɢɪɫɤɚɹ ɢɡɞɚɬɟɥɶɫɤɚɹ ɮɢɪɦɚ ɊȺɇ, 1998. 151. Ɇɢɪɤɟɫ ȿ.Ɇ. Ɉɛɭɱɟɧɢɟ ɫɟɬɟɣ ɫ ɩɨɪɨɝɨɜɵɦɢ ɧɟɣɪɨɧɚɦɢ. Ɍɟɡɢɫɵ ɞɨɤɥɚɞɨɜ III ȼɫɟɪɨɫɢɣɫɤɨɝɨ ɫɟɦɢɧɚɪɚ "ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ", Ʉɪɚɫɧɨɹɪɫɤ: ɂɡɞ-ɜɨ ɄȽɌɍ, ɫ. 72 152. Ɇɢɪɤɟɫ ȿ.Ɇ. Ɉɰɟɧɤɢ ɢ ɢɧɬɟɪɩɪɟɬɚɬɨɪɵ ɨɬɜɟɬɚ ɞɥɹ ɫɟɬɟɣ ɞɜɨɣɫɬɜɟɧɧɨɝɨ ɮɭɧɤɰɢɨɧɢɪɨɜɚɧɢɹ. Ɍɟɡɢɫɵ ɞɨɤɥɚɞɨɜ III ȼɫɟɪɨɫɫɢɣɫɤɨɝɨ ɫɟɦɢɧɚɪɚ "ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ", Ʉɪɚɫɧɨɹɪɫɤ: ɂɡɞ-ɜɨ ɄȽɌɍ, ɫɫ. 73-74
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153. Ɇɢɪɤɟɫ ȿ.Ɇ., ɋɜɢɬɢɧ Ⱥ.ɉ. ɉɪɢɦɟɧɟɧɢɟ ɦɟɬɨɞɚ ɚɫɫɨɰɢɚɬɢɜɧɵɯ ɫɟɬɟɣ ɞɥɹ ɩɪɨɝɧɨɡɢɪɨɜɚɧɢɹ ɩɟɪɟɧɨɫɨɜ ɡɚɪɹɞɚ ɩɪɢ ɚɞɫɨɪɛɰɢɢ ɦɨɥɟɤɭɥ. // ɗɜɨɥɸɰɢɨɧɧɨɟ ɦɨɞɟɥɢɪɨɜɚɧɢɟ ɢ ɤɢɧɟɬɢɤɚ. ɇɨɜɨɫɢɛɢɪɫɤ: ɇɚɭɤɚ, 1992. ɋ.30-35. 154. Ɇɢɪɤɟɫ ȿ.Ɇ., ɋɜɢɬɢɧ Ⱥ.ɉ., Ɏɟɬ Ⱥ.ɂ. Ɇɚɫɫɨɜɵɟ ɮɨɪɦɭɥɵ ɞɥɹ ɚɬɨɦɨɜ. // Ɇɚɬɟɦɚɬɢɱɟɫɤɨɟ ɦɨɞɟɥɢɪɨɜɚɧɢɟ ɜ ɯɢɦɢɢ ɢ ɛɢɨɥɨɝɢɢ. ɇɨɜɵɟ ɩɨɞɯɨɞɵ. ɇɨɜɨɫɢɛɢɪɫɤ : ɇɚɭɤɚ, 1992. ɋ. 199204. 155. Ɇɢɪɤɢɧ Ȼ.Ƚ. Ⱥɧɚɥɢɡ ɤɚɱɟɫɬɜɟɧɧɵɯ ɩɪɢɡɧɚɤɨɜ ɢ ɫɬɪɭɤɬɭɪ.Ɇ.: ɋɬɚɬɢɫɬɢɤɚ, 1980.- 319 ɫ. 156. Ɇɤɪɬɱɹɧ ɋ.Ɉ. ɉɪɨɟɤɬɢɪɨɜɚɧɢɟ ɥɨɝɢɱɟɫɤɢɯ ɭɫɬɪɨɣɫɬɜ ɗȼɆ ɧɚ ɧɟɣɪɨɧɧɵɯ ɷɥɟɦɟɧɬɚɯ. - Ɇ.: ɗɧɟɪɝɢɹ, 1977. 157. Ɇɨɫɬɟɥɥɟɪ Ɏ., Ɍɶɸɤɢ Ⱦɠ. Ⱥɧɚɥɢɡ ɞɚɧɧɵɯ ɢ ɪɟɝɪɟɫɫɢɹ.- Ɇ.: Ɏɢɧɚɧɫɵ ɢ ɫɬɚɬɢɫɬɢɤɚ, 1982.239 ɫ. 158. Ɇɭɥɥɚɬ ɂ.ɗ. ɗɤɫɬɪɟɦɚɥɶɧɵɟ ɩɨɞɫɢɫɬɟɦɵ ɦɨɧɨɬɨɧɧɵɯ ɫɢɫɬɟɦ. I, II, III. - Ⱥɜɬɨɦɚɬɢɤɚ ɢ ɬɟɥɟɦɟɯɚɧɢɤɚ, 1976, ʋ 5. ɋ. 130-139; 1976, ʋ 8. ɋ. 169-178; 1977, ʋ 1. ɋ. 143-152. 159. Ɇɭɱɧɢɤ ɂ.Ȼ. Ⱥɧɚɥɢɡ ɫɬɪɭɤɬɭɪɵ ɷɤɫɩɟɪɢɦɟɧɬɚɥɶɧɵɯ ɝɪɚɮɨɜ. - Ⱥɜɬɨɦɚɬɢɤɚ ɢ ɬɟɥɟɦɟɯɚɧɢɤɚ, 1974, ʋ 9. ɋ. 62-80. 160. Ɇɵɡɧɢɤɨɜ Ⱥ.ȼ., Ɋɨɫɫɢɟɜ Ⱦ.Ⱥ., Ʌɨɯɦɚɧ ȼ.Ɏ. ɇɟɣɪɨɫɟɬɟɜɚɹ ɷɤɫɩɟɪɬɧɚɹ ɫɢɫɬɟɦɚ ɞɥɹ ɨɩɬɢɦɢɡɚɰɢɢ ɥɟɱɟɧɢɹ ɨɛɥɢɬɟɪɢɪɭɸɳɟɝɨ ɬɪɨɦɛɚɧɝɢɢɬɚ ɢ ɩɪɨɝɧɨɡɢɪɨɜɚɧɢɹ ɟɝɨ ɧɟɩɨɫɪɟɞɫɬɜɟɧɧɵɯ ɢɫɯɨɞɨɜ // Ⱥɧɝɢɨɥɨɝɢɹ ɢ ɫɨɫɭɞɢɫɬɚɹ ɯɢɪɭɪɝɢɹ.- 1995.- N 2.- ɋ.100. 161. Ɇɵɡɧɢɤɨɜ Ⱥ.ȼ., Ɋɨɫɫɢɟɜ Ⱦ.Ⱥ., Ʌɨɯɦɚɧ ȼ.Ɏ. ɉɪɨɝɧɨɡɢɪɨɜɚɧɢɟ ɧɟɩɨɫɪɟɞɫɬɜɟɧɧɵɯ ɪɟɡɭɥɶɬɚɬɨɜ ɥɟɱɟɧɢɹ ɨɛɥɢɬɟɪɢɪɭɸɳɟɝɨ ɬɪɨɦɛɚɧɝɢɢɬɚ ɫ ɩɨɦɨɳɶɸ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ // ȼ ɫɛ.: Ɇɨɥɨɞɵɟ ɭɱɟɧɵɟ - ɩɪɚɤɬɢɱɟɫɤɨɦɭ ɡɞɪɚɜɨɨɯɪɚɧɟɧɢɸ.Ʉɪɚɫɧɨɹɪɫɤ, 1994.- ɋ. 42. 162. ɇɚɡɚɪɨɜ Ȼ.ȼ. ɉɪɨɝɧɨɫɬɢɱɟɫɤɢɟ ɚɫɩɟɤɬɵ ɧɟɤɨɬɨɪɵɯ ɧɚɪɭɲɟɧɢɣ ɪɢɬɦɚ ɢ ɩɪɨɜɨɞɢɦɨɫɬɢ ɩɪɢ ɨɫɬɪɨɦ ɢɧɮɚɪɤɬɟ ɦɢɨɤɚɪɞɚ: Ⱥɜɬɨɪɟɮ. ɞɢɫ.... ɤɚɧɞ. ɦɟɞ. ɧɚɭɤ. ɇɨɜɨɫɢɛɢɪɫɤ, 1982. 22 ɫ. 163. ɇɚɡɢɦɨɜɚ Ⱦ.ɂ., ɇɨɜɨɯɨɞɶɤɨ Ⱥ.ɘ., ɐɚɪɟɝɨɪɨɞɰɟɜ ȼ.Ƚ. ɇɟɣɪɨɫɟɬɟɜɵɟ ɦɟɬɨɞɵ ɨɛɪɚɛɨɬɤɢ ɢɧɮɨɪɦɚɰɢɢ ɜ ɡɚɞɚɱɟ ɩɪɨɝɧɨɡɢɪɨɜɚɧɢɹ ɤɥɢɦɚɬɢɱɟɫɤɢɯ ɩɚɪɚɦɟɬɪɨɜ. // Ɇɚɬɟɦɚɬɢɱɟɫɤɢɟ ɦɨɞɟɥɢ ɢ ɦɟɬɨɞɵ ɢɯ ɢɫɫɥɟɞɨɜɚɧɢɹ: Ɇɟɠɞɭɧɚɪ. ɤɨɧɮɟɪɟɧɰɢɹ. Ɍɟɡɢɫɵ ɞɨɤɥɚɞɨɜ. Ʉɪɚɫɧɨɹɪɫɤ: ɢɡɞ. Ʉɪɚɫɧɨɹɪ. ɝɨɫ. ɭɧ-ɬɚ. ɋ. 135. 164. ɇɚɡɢɦɨɜɚ Ⱦ.ɂ., ɇɨɜɨɯɨɞɶɤɨ Ⱥ.ɘ., ɐɚɪɟɝɨɪɨɞɰɟɜ ȼ.Ƚ. ɇɟɣɪɨɫɟɬɟɜɵɟ ɦɟɬɨɞɵ ɨɛɪɚɛɨɬɤɢ ɢɧɮɨɪɦɚɰɢɢ ɜ ɡɚɞɚɱɟ ɜɨɫɫɬɚɧɨɜɥɟɧɢɹ ɤɥɢɦɚɬɢɱɟɫɤɢɯ ɞɚɧɧɵɯ // ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ. Ɍɟɡɢɫɵ ɞɨɤɥɚɞɨɜ 5 ȼɫɟɪɨɫɫɢɣɫɤɨɝɨ ɫɟɦɢɧɚɪɚ, 3-5 ɨɤɬɹɛɪɹ 1997 ɝ. / ɉɨɞ ɪɟɞ. Ⱥ.ɇ.Ƚɨɪɛɚɧɹ. Ʉɪɚɫɧɨɹɪɫɤ: ɢɡɞ. ɄȽɌɍ, 1997. ɋ. 124. 165. ɇɚɪɨɞɨɜ Ⱥ.Ⱥ., Ɋɨɫɫɢɟɜ Ⱦ.Ⱥ., Ɂɚɯɦɚɬɨɜ ɂ.Ƚ. Ɉɰɟɧɤɚ ɤɨɦɩɟɧɫɚɬɨɪɧɵɯ ɜɨɡɦɨɠɧɨɫɬɟɣ ɝɨɥɨɜɧɨɝɨ ɦɨɡɝɚ ɩɪɢ ɟɝɨ ɨɪɝɚɧɢɱɟɫɤɢɯ ɩɨɪɚɠɟɧɢɹɯ ɫ ɩɨɦɨɳɶɸ ɢɫɤɭɫɫɬɜɟɧɧɵɯ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ // ȼ ɫɛ.: Ɇɨɥɨɞɵɟ ɭɱɟɧɵɟ - ɩɪɚɤɬɢɱɟɫɤɨɦɭ ɡɞɪɚɜɨɨɯɪɚɧɟɧɢɸ.- Ʉɪɚɫɧɨɹɪɫɤ, 1994.ɋ.30. 166. ɇɚɭɱɧɨɟ ɨɬɤɪɵɬɢɟ ɜ Ɋɨɫɫɢɢ... . - Ʉɪɚɫɧɨɹɪɫɤɢɣ ɤɨɦɫɨɦɨɥɟɰ (ɝɚɡɟɬɚ), Ʉɪɚɫɧɨɹɪɫɤ, 1992, 11 ɚɜɝɭɫɬɚ, ʋ 86. 167. ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ / Ⱥ.ɇ.Ƚɨɪɛɚɧɶ, ȼ.Ʌ.Ⱦɭɧɢɧ-Ȼɚɪɤɨɜɫɤɢɣ, Ⱥ.ɇ.Ʉɢɪɞɢɧ, ȿ.Ɇ.Ɇɢɪɤɟɫ, Ⱥ.ɘ.ɇɨɜɨɯɨɞɶɤɨ, Ⱦ.Ⱥ.Ɋɨɫɫɢɟɜ, ɋ.Ⱥ.Ɍɟɪɟɯɨɜ, Ɇ.ɘ.ɋɟɧɚɲɨɜɚ, ȼ.Ƚ.ɐɚɪɟɝɨɪɨɞɰɟɜ. ɇɨɜɨɫɢɛɢɪɫɤ: ɇɚɭɤɚ, ɋɢɛɢɪɫɤɚɹ ɢɡɞɚɬɟɥɶɫɤɚɹ ɮɢɪɦɚ ɊȺɇ, 1998. 168. ɇɢɤɨɥɚɟɜ ɉ.ɉ. Ɇɟɬɨɞɵ ɩɪɟɞɫɬɚɜɥɟɧɢɹ ɮɨɪɦɵ ɨɛɴɟɤɬɨɜ ɜ ɡɚɞɚɱɟ ɤɨɧɫɬɚɧɬɧɨɝɨ ɡɪɢɬɟɥɶɧɨɝɨ ɜɨɫɩɪɢɹɬɢɹ / ɂɧɬɟɥɥɟɤɬɭɚɥɶɧɵɟ ɩɪɨɰɟɫɫɵ ɢ ɢɯ ɦɨɞɟɥɢɪɨɜɚɧɢɟ. ɉɪɨɫɬɪɚɧɫɬɜɟɧɧɨ-ɜɪɟɦɟɧɧɚɹ ɨɪɝɚɧɢɡɚɰɢɹ.- Ɇ.: ɇɚɭɤɚ, 1991.- ɋ. 146-173. 169. ɇɢɤɨɥɚɟɜ ɉ.ɉ. Ɍɪɢɯɪɨɦɚɬɢɱɟɫɤɚɹ ɦɨɞɟɥɶ ɤɨɧɫɬɚɧɬ ɜɨɫɩɪɢɹɬɢɹ ɨɤɪɚɫɤɢ ɨɛɴɟɤɬɨɜ // ɋɟɧɫɨɪɧɵɟ ɫɢɫɬɟɦɵ. 1990. Ɍ.4 ȼɵɩ. 4. ɋ. 421-442. 170. ɇɢɥɶɫɟɧ ɇ. Ɉɛɭɱɚɸɳɢɟɫɹ ɦɚɲɢɧɵ. - Ɇ.: Ɇɢɪ, 1967. 171. ɇɨɜɨɯɨɞɶɤɨ Ⱥ.ɘ., ɐɚɪɟɝɨɪɨɞɰɟɜ ȼ.Ƚ. ɇɟɣɪɨɫɟɬɟɜɨɟ ɪɟɲɟɧɢɟ ɬɪɚɧɫɩɨɧɢɪɨɜɚɧɧɨɣ ɡɚɞɚɱɢ ɥɢɧɟɣɧɨɣ ɪɟɝɪɟɫɫɢɢ // Ɍɟɡɢɫɵ ɞɨɤɥɚɞɨɜ ɱɟɬɜɟɪɬɨɣ ɦɟɠɞɭɧɚɪɨɞɧɨɣ ɤɨɧɮɟɪɟɧɰɢɢ "Ɇɚɬɟɦɚɬɢɤɚ, ɤɨɦɩɶɸɬɟɪ, ɨɛɪɚɡɨɜɚɧɢɟ". - Ɇɨɫɤɜɚ, 1997. 172. Ɉɯɨɧɢɧ ȼ.Ⱥ. ȼɚɪɢɚɰɢɨɧɧɵɣ ɩɪɢɧɰɢɩ ɜ ɬɟɨɪɢɢ ɚɞɚɩɬɢɜɧɵɯ ɫɟɬɟɣ. ɉɪɟɩɪɢɧɬ ɂɎ ɋɈ Ⱥɇ ɋɋɋɊ, Ʉɪɚɫɧɨɹɪɫɤ, 1987, ʋ61Ȼ, 18 ɫ. 173. ɉɚɪɢɧ ȼ.ȼ., Ȼɚɟɜɫɤɢɣ Ɋ.Ɇ. Ɇɟɞɢɰɢɧɚ ɢ ɬɟɯɧɢɤɚ.- Ɇ.: Ɂɧɚɧɢɟ, 1968.- ɋ.36-49. 174. ɉɟɪɟɜɟɪɡɟɜ-Ɉɪɥɨɜ ȼ.ɋ. ɋɨɜɟɬɱɢɤ ɫɩɟɰɢɚɥɢɫɬɚ. Ɉɩɵɬ ɪɚɡɪɚɛɨɬɤɢ ɩɚɪɬɧɟɪɫɤɨɣ ɫɢɫɬɟɦɵ // Ɇ.: ɇɚɭɤɚ, 1990.- 133 ɫ. 175. ɉɟɬɪɨɜ Ⱥ.ɉ. Ⱥɤɫɢɨɦɚɬɢɤɚ ɢɝɪɵ "ɜ ɩɪɹɬɤɢ" ɢ ɝɟɧɟɡɢɫ ɡɪɢɬɟɥɶɧɨɝɨ ɩɪɨɫɬɪɚɧɫɬɜɚ / ɂɧɬɟɥɥɟɤɬɭɚɥɶɧɵɟ ɩɪɨɰɟɫɫɵ ɢ ɢɯ ɦɨɞɟɥɢɪɨɜɚɧɢɟ. ɉɪɨɫɬɪɚɧɫɬɜɟɧɧɨ-ɜɪɟɦɟɧɧɚɹ ɨɪɝɚɧɢɡɚɰɢɹ.- Ɇ.: ɇɚɭɤɚ, 1991.- ɋ. 174-182.
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176. ɉɢɬɟɧɤɨ Ⱥ.Ⱥ. ɇɟɣɪɨɫɟɬɟɜɨɟ ɜɨɫɩɨɥɧɟɧɢɟ ɩɪɨɛɟɥɨɜ ɞɚɧɧɵɯ ɜ Ƚɂɋ // ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ. Ɍɟɡɢɫɵ ɞɨɤɥɚɞɨɜ 5 ȼɫɟɪɨɫɫɢɣɫɤɨɝɨ ɫɟɦɢɧɚɪɚ, 3-5 ɨɤɬɹɛɪɹ 1997 ɝ. / ɉɨɞ ɪɟɞ. Ⱥ.ɇ.Ƚɨɪɛɚɧɹ. Ʉɪɚɫɧɨɹɪɫɤ: ɢɡɞ. ɄȽɌɍ, 1997. ɋ. 140. 177. ɉɨɡɢɧ ɂ.ȼ. Ɇɨɞɟɥɢɪɨɜɚɧɢɟ ɧɟɣɪɨɧɧɵɯ ɫɬɪɭɤɬɭɪ. - Ɇ.: ɇɚɭɤɚ, 1970. 178. ɉɲɟɧɢɱɧɵɣ Ȼ.ɇ., Ⱦɚɧɢɥɢɧ ɘ.Ɇ. ɑɢɫɥɟɧɧɵɟ ɦɟɬɨɞɵ ɜ ɷɤɫɬɪɟɦɚɥɶɧɵɯ ɡɚɞɚɱɚɯ. Ɇ.:ɇɚɭɤɚ, 1975. 319 ɫ. 179. Ɋɚɫɩɨɡɧɚɜɚɧɢɟ ɨɛɪɚɡɨɜ ɢ ɦɟɞɢɰɢɧɫɤɚɹ ɞɢɚɝɧɨɫɬɢɤɚ / ɩɨɞ ɪɟɞ. ɘ.Ɇ. ɇɟɣɦɚɪɤɚ.- Ɇ.: ɇɚɭɤɚ, 1972.- 328 ɫ. 180. Ɋɨɡɟɧɛɥɚɬɬ Ɏ. ɉɪɢɧɰɢɩɵ ɧɟɣɪɨɞɢɧɚɦɢɤɢ. ɉɟɪɰɟɩɬɪɨɧ ɢ ɬɟɨɪɢɹ ɦɟɯɚɧɢɡɦɨɜ ɦɨɡɝɚ. Ɇ.: Ɇɢɪ, 1965. 480 ɫ. 181. Ɋɨɫɫɢɟɜ Ⱥ.Ⱥ. Ƚɟɧɟɪɚɬɨɪ 0-ɬɚɛɥɢɰ ɜ ɫɪɟɞɟ WINDOWS-95 // ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ. Ɍɟɡɢɫɵ ɞɨɤɥɚɞɨɜ 5 ȼɫɟɪɨɫɫɢɣɫɤɨɝɨ ɫɟɦɢɧɚɪɚ, 3-5 ɨɤɬɹɛɪɹ 1997 ɝ. / ɉɨɞ ɪɟɞ. Ⱥ.ɇ.Ƚɨɪɛɚɧɹ. Ʉɪɚɫɧɨɹɪɫɤ: ɢɡɞ. ɄȽɌɍ, 1997. ɋ. 151. 182. Ɋɨɫɫɢɟɜ Ⱦ.Ⱥ, Ɂɚɯɦɚɬɨɜ ɂ.Ƚ. Ɉɰɟɧɤɚ ɤɨɦɩɟɧɫɚɬɨɪɧɵɯ ɜɨɡɦɨɠɧɨɫɬɟɣ ɝɨɥɨɜɧɨɝɨ ɦɨɡɝɚ ɩɪɢ ɟɝɨ ɨɪɝɚɧɢɱɟɫɤɢɯ ɩɨɪɚɠɟɧɢɹɯ (ɨɩɵɬ ɩɪɢɦɟɧɟɧɢɹ ɧɟɣɪɨɫɟɬɟɜɨɝɨ ɜɟɤɬɨɪɧɨɝɨ ɩɪɟɞɢɤɬɨɪɚ) // ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ. Ɍɟɡ. ɞɨɤɥ. ȼɫɟɪɨɫɫɢɣɫɤɨɝɨ ɪɚɛɨɱɟɝɨ ɫɟɦɢɧɚɪɚ 7 - 10 ɨɤɬɹɛɪɹ 1994 ɝ. Ʉɪɚɫɧɨɹɪɫɤ.- 1994. 17 ɋ.42. 183. Ɋɨɫɫɢɟɜ Ⱦ.Ⱥ. Ɇɟɞɢɰɢɧɫɤɚɹ ɧɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ / ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɇɨɜɨɫɢɛɢɪɫɤ: ɇɚɭɤɚ, ɋɢɛɢɪɫɤɚɹ ɢɡɞɚɬɟɥɶɫɤɚɹ ɮɢɪɦɚ ɊȺɇ, 1998. 184. Ɋɨɫɫɢɟɜ Ⱦ.Ⱥ. ɇɟɣɪɨɫɟɬɟɜɵɟ ɫɚɦɨɨɛɭɱɚɸɳɢɟɫɹ ɷɤɫɩɟɪɬɧɵɟ ɫɢɫɬɟɦɵ ɜ ɦɟɞɢɰɢɧɟ // Ɇɨɥɨɞɵɟ ɭɱɟɧɵɟ - ɩɪɚɤɬɢɱɟɫɤɨɦɭ ɡɞɪɚɜɨɨɯɪɚɧɟɧɢɸ.- Ʉɪɚɫɧɨɹɪɫɤ, 1994.- ɋ.17. 185. Ɋɨɫɫɢɟɜ Ⱦ.Ⱥ., Ȼɭɬɚɤɨɜɚ ȿ.ȼ. ɇɟɣɪɨɫɟɬɟɜɚɹ ɞɢɚɝɧɨɫɬɢɤɚ ɢ ɞɢɮɮɟɪɟɧɰɢɚɥɶɧɚɹ ɞɢɚɝɧɨɫɬɢɤɚ ɡɥɨɤɚɱɟɫɬɜɟɧɧɵɯ ɨɩɭɯɨɥɟɣ ɫɨɫɭɞɢɫɬɨɣ ɨɛɨɥɨɱɤɢ ɝɥɚɡɚ // ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ: Ɇɚɬɟɪɢɚɥɵ III ȼɫɟɪɨɫɫɢɣɫɤɨɝɨ ɫɟɦɢɧɚɪɚ, 6-8 ɨɤɬɹɛɪɹ 1995 ɝ. ɑ. 1/ɉɨɞ ɪɟɞ. Ⱥ.ɇ.Ƚɨɪɛɚɧɹ; Ʉɪɚɫɧɨɹɪɫɤ: ɂɡɞ-ɜɨ ɄȽɌɍ, 1995. ɋ. 167-194. 186. Ɋɨɫɫɢɟɜ Ⱦ.Ⱥ., Ȼɭɬɚɤɨɜɚ ȿ.ȼ. Ɋɚɧɧɹɹ ɞɢɚɝɧɨɫɬɢɤɚ ɡɥɨɤɚɱɟɫɬɜɟɧɧɵɯ ɨɩɭɯɨɥɟɣ ɫɨɫɭɞɢɫɬɨɣ ɨɛɨɥɨɱɤɢ ɝɥɚɡɚ ɫ ɢɫɩɨɥɶɡɨɜɚɧɢɟɦ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ // ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ. Ɍɟɡ. ɞɨɤɥ. ȼɫɟɪɨɫɫɢɣɫɤɨɝɨ ɪɚɛɨɱɟɝɨ ɫɟɦɢɧɚɪɚ 7 10 ɨɤɬɹɛɪɹ 1994 ɝ. Ʉɪɚɫɧɨɹɪɫɤ.- 1994.- ɋ.44. 187. Ɋɨɫɫɢɟɜ Ⱦ.Ⱥ., ȼɢɧɧɢɤ ɇ.Ƚ. ɉɪɟɞɫɤɚɡɚɧɢɟ "ɭɞɚɱɧɨɫɬɢ" ɩɪɟɞɫɬɨɹɳɟɝɨ ɛɪɚɤɚ ɧɟɣɪɨɫɟɬɟɜɵɦɢ ɷɤɫɩɟɪɬɚɦɢ // ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ. Ɍɟɡ. ɞɨɤɥ. ȼɫɟɪɨɫɫɢɣɫɤɨɝɨ ɪɚɛɨɱɟɝɨ ɫɟɦɢɧɚɪɚ 7 - 10 ɨɤɬɹɛɪɹ 1994 ɝ. Ʉɪɚɫɧɨɹɪɫɤ.- 1994.ɋ.45. 188. Ɋɨɫɫɢɟɜ Ⱦ.Ⱥ., Ƚɢɥɟɜ ɋ.ȿ., Ʉɨɱɟɧɨɜ Ⱦ.Ⱥ. "MultiNeuron, ȼɟɪɫɢɢ 2.0 ɢ 3.0", ɬɟɡɢɫɵ ɞɨɤɥɚɞɨɜ III ȼɫɟɪɨɫɢɣɫɤɨɝɨ ɫɟɦɢɧɚɪɚ "ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ", Ʉɪɚɫɧɨɹɪɫɤ: ɂɡɞ-ɜɨ ɄȽɌɍ, ɫ. 14 189. Ɋɨɫɫɢɟɜ Ⱦ.Ⱥ., Ƚɢɥɟɜ ɋ.ȿ., Ʉɨɱɟɧɨɜ Ⱦ.Ⱥ.. ɇɟɣɪɨɷɦɭɥɹɬɨɪ "MultiNeuron".ȼɬɨɪɨɣ ɋɢɛɢɪɫɤɢɣ ɤɨɧɝɪɟɫɫ ɩɨ ɉɪɢɤɥɚɞɧɨɣ ɢ ɂɧɞɭɫɬɪɢɚɥɶɧɨɣ Ɇɚɬɟɦɚɬɢɤɟ, ɩɨɫɜɹɳɟɧɧɵɣ ɩɚɦɹɬɢ Ⱥ.Ⱥ.Ʌɹɩɭɧɨɜɚ (1911-1973), Ⱥ.ɉ.ȿɪɲɨɜɚ (1931-1988) ɢ ɂ.Ⱥ.ɉɨɥɟɬɚɟɜɚ (1915-1983). ɇɨɜɨɫɢɛɢɪɫɤ, ɢɸɧɶ 1996. Ɍɟɡɢɫɵ ɞɨɤɥɚɞɨɜ, ɱɚɫɬɶ 1. ɂɡɞ. ɂɧɫɬɢɬɭɬɚ ɦɚɬɟɦɚɬɢɤɢ ɋɈ ɊȺɇ. ɋ. 45. 190. Ɋɨɫɫɢɟɜ Ⱦ.Ⱥ., Ƚɨɥɨɜɟɧɤɢɧ ɋ.ȿ. ɉɪɨɝɧɨɡɢɪɨɜɚɧɢɟ ɨɫɥɨɠɧɟɧɢɣ ɢɧɮɚɪɤɬɚ ɦɢɨɤɚɪɞɚ ɫ ɩɨɦɨɳɶɸ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ // ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ. Ɍɟɡ. ɞɨɤɥ. ȼɫɟɪɨɫɫɢɣɫɤɨɝɨ ɪɚɛɨɱɟɝɨ ɫɟɦɢɧɚɪɚ 7 - 10 ɨɤɬɹɛɪɹ 1994 ɝ. Ʉɪɚɫɧɨɹɪɫɤ.- 1994.ɋ.40. 191. Ɋɨɫɫɢɟɜ Ⱦ.Ⱥ., Ƚɨɥɨɜɟɧɤɢɧ ɋ.ȿ., ɇɚɡɚɪɨɜ Ȼ.ȼ., ɒɭɥɶɦɚɧ ȼ.Ⱥ., Ɇɚɬɸɲɢɧ Ƚ.ȼ. Ɉɩɪɟɞɟɥɟɧɢɟ ɢɧɮɨɪɦɚɬɢɜɧɨɫɬɢ ɦɟɞɢɰɢɧɫɤɢɯ ɩɚɪɚɦɟɬɪɨɜ ɫ ɩɨɦɨɳɶɸ ɧɟɣɪɨɧɧɨɣ ɫɟɬɢ // Ⱦɢɚɝɧɨɫɬɢɤɚ, ɢɧɮɨɪɦɚɬɢɤɚ ɢ ɦɟɬɪɨɥɨɝɢɹ - 94.- Ɍɟɡ. ɧɚɭɱɧɨ-ɬɟɯɧɢɱɟɫɤɨɣ ɤɨɧɮɟɪɟɧɰɢɢ (ɝ. ɋɚɧɤɬɉɟɬɟɪɛɭɪɝ, 28-30 ɢɸɧɹ 1994 ɝ.).ɋ.-ɉɟɬɟɪɛɭɪɝ.- 1994.- ɋ.348. 192. Ɋɨɫɫɢɟɜ Ⱦ.Ⱥ., Ƚɨɥɨɜɟɧɤɢɧ ɋ.ȿ., ɒɭɥɶɦɚɧ ȼ.Ⱥ., Ɇɚɬɸɲɢɧ Ƚ.ȼ. ɂɫɩɨɥɶɡɨɜɚɧɢɟ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ ɞɥɹ ɩɪɨɝɧɨɡɢɪɨɜɚɧɢɹ ɜɨɡɧɢɤɧɨɜɟɧɢɹ ɢɥɢ ɭɫɭɝɭɛɥɟɧɢɹ ɡɚɫɬɨɣɧɨɣ ɫɟɪɞɟɱɧɨɣ ɧɟɞɨɫɬɚɬɨɱɧɨɫɬɢ ɭ ɛɨɥɶɧɵɯ ɫ ɧɚɪɭɲɟɧɢɹɦɢ ɪɢɬɦɚ ɫɟɪɞɰɚ // Ɍɟɡɢɫɵ ɞɨɤɥɚɞɨɜ ɪɚɛɨɱɟɝɨ ɫɟɦɢɧɚɪɚ "ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɵ", Ʉɪɚɫɧɨɹɪɫɤ, 8-11 ɨɤɬɹɛɪɹ 1993 ɝ., Ʉɪɚɫɧɨɹɪɫɤ.- 1993.- ɋ.16. 193. Ɋɨɫɫɢɟɜ Ⱦ.Ⱥ., Ƚɨɥɨɜɟɧɤɢɧ ɋ.ȿ., ɒɭɥɶɦɚɧ ȼ.Ⱥ., Ɇɚɬɸɲɢɧ Ƚ.ȼ. ɉɪɨɝɧɨɡɢɪɨɜɚɧɢɟ ɨɫɥɨɠɧɟɧɢɣ ɢɧɮɚɪɤɬɚ ɦɢɨɤɚɪɞɚ ɧɟɣɪɨɧɧɵɦɢ ɫɟɬɹɦɢ // ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ: Ɇɚɬɟɪɢɚɥɵ III ȼɫɟɪɨɫɫɢɣɫɤɨɝɨ ɫɟɦɢɧɚɪɚ, 6-8 ɨɤɬɹɛɪɹ 1995 ɝ. ɑ. 1/ɉɨɞ ɪɟɞ. Ⱥ.ɇ.Ƚɨɪɛɚɧɹ; Ʉɪɚɫɧɨɹɪɫɤ: ɂɡɞ-ɜɨ ɄȽɌɍ, 1995. ɋ. 128-166. 194. Ɋɨɫɫɢɟɜ Ⱦ.Ⱥ., Ⱦɨɝɚɞɢɧ ɋ.Ⱥ., Ɇɚɫɥɟɧɢɤɨɜɚ ȿ.ȼ., ɇɨɡɞɪɚɱɟɜ Ʉ.Ƚ., Ȼɨɪɢɫɨɜ Ⱥ.Ƚ. Ɉɛɭɱɟɧɢɟ ɧɟɣɪɨɫɟɬɟɣ ɜɵɹɜɥɟɧɢɸ ɧɚɤɨɩɥɟɧɧɨɣ ɞɨɡɵ ɪɚɞɢɨɚɤɬɢɜɧɨɝɨ ɨɛɥɭɱɟɧɢɹ // Ɍɟɡɢɫɵ ɞɨɤɥɚɞɨɜ ɪɚɛɨɱɟɝɨ ɫɟɦɢɧɚɪɚ "ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɵ", Ʉɪɚɫɧɨɹɪɫɤ, 8-11 ɨɤɬɹɛɪɹ 1993 ɝ., Ʉɪɚɫɧɨɹɪɫɤ.- 1993.- ɋ.15.
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195. Ɋɨɫɫɢɟɜ Ⱦ.Ⱥ., Ⱦɨɝɚɞɢɧ ɋ.Ⱥ., Ɇɚɫɥɟɧɧɢɤɨɜɚ ȿ.ȼ., ɇɨɡɞɪɚɱɟɜ Ʉ.Ƚ., Ȼɨɪɢɫɨɜ Ⱥ.Ƚ. ȼɵɹɜɥɟɧɢɟ ɧɚɤɨɩɥɟɧɧɨɣ ɞɨɡɵ ɪɚɞɢɨɚɤɬɢɜɧɨɝɨ ɨɛɥɭɱɟɧɢɹ ɫ ɩɨɦɨɳɶɸ ɧɟɣɪɨɫɟɬɟɜɨɝɨ ɤɥɚɫɫɢɮɢɤɚɬɨɪɚ // ɋɨɜɪɟɦɟɧɧɵɟ ɩɪɨɛɥɟɦɵ ɢ ɦɟɬɨɞɨɥɨɝɢɱɟɫɤɢɟ ɩɨɞɯɨɞɵ ɤ ɢɡɭɱɟɧɢɸ ɜɥɢɹɧɢɹ ɮɚɤɬɨɪɨɜ ɩɪɨɢɡɜɨɞɫɬɜɟɧɧɨɣ ɢ ɨɤɪɭɠɚɸɳɟɣ ɫɪɟɞɵ ɧɚ ɡɞɨɪɨɜɶɟ ɱɟɥɨɜɟɤɚ (Ɍɟɡ. ɞɨɤɥ. ɪɟɫɩɭɛɥɢɤɚɧɫɤɨɣ ɤɨɧɮ.).- Ⱥɧɝɚɪɫɤ-ɂɪɤɭɬɫɤ: ɢɡɞ. "Ʌɢɫɧɚ".1993.- ɋ.111-112. 196. Ɋɨɫɫɢɟɜ Ⱦ.Ⱥ., Ʉɨɱɟɧɨɜ Ⱦ.Ⱥ. ɉɚɤɟɬ ɩɪɨɝɪɚɦɦ "MultiNeuron" - "Configurator" - "Tester" ɞɥɹ ɤɨɧɫɬɪɭɢɪɨɜɚɧɢɹ ɧɟɣɪɨɫɟɬɟɜɵɯ ɩɪɢɥɨɠɟɧɢɣ // ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ. Ɍɟɡ. ɞɨɤɥ. ȼɫɟɪɨɫɫɢɣɫɤɨɝɨ ɪɚɛɨɱɟɝɨ ɫɟɦɢɧɚɪɚ 7 - 10 ɨɤɬɹɛɪɹ 1994 ɝ. Ʉɪɚɫɧɨɹɪɫɤ.- 1994.- ɋ.30. 197. Ɋɨɫɫɢɟɜ Ⱦ.Ⱥ., Ɇɵɡɧɢɤɨɜ Ⱥ.Ⱥ. ɇɟɣɪɨɫɟɬɟɜɨɟ ɦɨɞɟɥɢɪɨɜɚɧɢɟ ɥɟɱɟɧɢɹ ɢ ɩɪɨɝɧɨɡɢɪɨɜɚɧɢɟ ɟɝɨ ɧɟɩɨɫɪɟɞɫɬɜɟɧɧɵɯ ɪɟɡɭɥɶɬɚɬɨɜ ɭ ɛɨɥɶɧɵɯ ɨɛɥɢɬɟɪɢɪɭɸɳɢɦ ɬɪɨɦɛɚɧɝɢɢɬɨɦ // ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ: Ɇɚɬɟɪɢɚɥɵ III ȼɫɟɪɨɫɫɢɣɫɤɨɝɨ ɫɟɦɢɧɚɪɚ, 6-8 ɨɤɬɹɛɪɹ 1995 ɝ. ɑ. 1/ɉɨɞ ɪɟɞ. Ⱥ.ɇ.Ƚɨɪɛɚɧɹ; Ʉɪɚɫɧɨɹɪɫɤ: ɂɡɞ-ɜɨ ɄȽɌɍ, 1995. ɋ. 194-228. 198. Ɋɨɫɫɢɟɜ Ⱦ.Ⱥ., Ɇɵɡɧɢɤɨɜ Ⱥ.ȼ. ɉɪɨɝɧɨɡɢɪɨɜɚɧɢɟ ɧɟɩɨɫɪɟɞɫɬɜɟɧɧɵɯ ɪɟɡɭɥɶɬɚɬɨɜ ɥɟɱɟɧɢɹ ɨɛɥɢɬɟɪɢɪɭɸɳɟɝɨ ɬɪɨɦɛɚɧɝɢɢɬɚ ɫ ɩɨɦɨɳɶɸ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ // ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ. Ɍɟɡ. ɞɨɤɥ. ȼɫɟɪɨɫɫɢɣɫɤɨɝɨ ɪɚɛɨɱɟɝɨ ɫɟɦɢɧɚɪɚ 7 - 10 ɨɤɬɹɛɪɹ 1994 ɝ. Ʉɪɚɫɧɨɹɪɫɤ.- 1994.- ɋ.41. 199. Ɋɨɫɫɢɟɜ Ⱦ.Ⱥ., ɋɚɜɱɟɧɤɨ Ⱥ.Ⱥ., Ƚɢɥɟɜ ɋ.ȿ., Ʉɨɱɟɧɨɜ Ⱦ.Ⱥ. ɉɪɢɦɟɧɟɧɢɟ ɧɟɣɪɨɫɟɬɟɣ ɞɥɹ ɢɡɭɱɟɧɢɹ ɢ ɞɢɚɝɧɨɫɬɢɤɢ ɢɦɦɭɧɨɞɟɮɢɰɢɬɧɵɯ ɫɨɫɬɨɹɧɢɣ// ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɵ/ Ɍɟɡɢɫɵ ɞɨɤɥɚɞɨɜ ɪɚɛɨɱɟɝɨ ɫɟɦɢɧɚɪɚ 8-11 ɨɤɬɹɛɪɹ 1993 ɝ., Ʉɪɚɫɧɨɹɪɫɤ/ ɂɧɫɬɢɬɭɬ ɛɢɨɮɢɡɢɤɢ ɋɈ ɊȺɇ, 1993. ɋ. 32. 200. Ɋɨɫɫɢɟɜ Ⱦ.Ⱥ., ɋɭɯɚɧɨɜɚ ɇ.ȼ., ɒɜɟɰɤɢɣ Ⱥ.Ƚ. ɇɟɣɪɨɫɟɬɟɜɚɹ ɫɢɫɬɟɦɚ ɞɢɮɮɟɪɟɧɰɢɚɥɶɧɨɣ ɞɢɚɝɧɨɫɬɢɤɢ ɡɚɛɨɥɟɜɚɧɢɣ, ɩɪɨɹɥɹɸɳɢɯɫɹ ɫɢɧɞɪɨɦɨɦ "ɨɫɬɪɨɝɨ ɠɢɜɨɬɚ" // ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ. Ɍɟɡ. ɞɨɤɥ. ȼɫɟɪɨɫɫɢɣɫɤɨɝɨ ɪɚɛɨɱɟɝɨ ɫɟɦɢɧɚɪɚ 7 - 10 ɨɤɬɹɛɪɹ 1994 ɝ. Ʉɪɚɫɧɨɹɪɫɤ.- 1994.- ɋ.43. 201. ɋɚɜɱɟɧɤɨ Ⱥ.Ⱥ., Ⱦɨɝɚɞɢɧ ɋ.Ⱥ., Ɍɤɚɱɟɜ Ⱥ.ȼ., Ȼɨɣɤɨ ȿ.Ɋ., Ɋɨɫɫɢɟɜ Ⱦ.Ⱥ. Ɉɛɫɥɟɞɨɜɚɧɢɟ ɥɸɞɟɣ ɜ ɪɚɣɨɧɟ ɜɨɡɦɨɠɧɨɝɨ ɪɚɞɢɨɚɤɬɢɜɧɨɝɨ ɡɚɝɪɹɡɧɟɧɢɹ ɫ ɩɨɦɨɳɶɸ ɧɟɣɪɨɫɟɬɟɜɨɝɨ ɤɥɚɫɫɢɮɢɤɚɬɨɪɚ // ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ. Ɍɟɡ. ɞɨɤɥ. ȼɫɟɪɨɫɫɢɣɫɤɨɝɨ ɪɚɛɨɱɟɝɨ ɫɟɦɢɧɚɪɚ 7 - 10 ɨɤɬɹɛɪɹ 1994 ɝ. Ʉɪɚɫɧɨɹɪɫɤ.- 1994.- ɋ.46. 202. ɋɚɜɱɟɧɤɨ Ⱥ.Ⱥ., Ɇɢɬɪɨɲɢɧɚ Ʌ.ȼ., Ɋɨɫɫɢɟɜ Ⱦ.Ⱥ., Ⱦɨɝɚɞɢɧ ɋ.Ⱥ. Ɇɨɞɟɥɢɪɨɜɚɧɢɟ ɫ ɩɨɦɨɳɶɸ ɧɟɣɪɨɫɟɬɟɜɨɝɨ ɩɪɟɞɢɤɬɨɪɚ ɪɟɚɥɶɧɵɯ ɱɢɫɟɥ ɢɦɦɭɧɨɷɧɞɨɤɪɢɧɧɨɝɨ ɜɡɚɢɦɨɞɟɣɫɬɜɢɹ ɩɪɢ ɡɚɛɨɥɟɜɚɧɢɹɯ ɳɢɬɨɜɢɞɧɨɣ ɠɟɥɟɡɵ // Ɍɟɡɢɫɵ ɞɨɤɥɚɞɨɜ ɪɚɛɨɱɟɝɨ ɫɟɦɢɧɚɪɚ "ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɵ", Ʉɪɚɫɧɨɹɪɫɤ, 8-11 ɨɤɬɹɛɪɹ 1993 ɝ., Ʉɪɚɫɧɨɹɪɫɤ.1993.- ɋ.18. 203. ɋɚɜɱɟɧɤɨ Ⱥ.Ⱥ., Ɋɨɫɫɢɟɜ Ⱦ.Ⱥ., Ⱦɨɝɚɞɢɧ ɋ.Ⱥ., Ƚɨɪɛɚɧɶ Ⱥ.ɇ. ɇɟɣɪɨɬɟɯɧɨɥɨɝɢɹ ɞɥɹ ɨɛɫɥɟɞɨɜɚɧɢɹ ɥɸɞɟɣ ɜ ɪɚɣɨɧɟ ɜɨɡɦɨɠɧɨɝɨ ɪɚɞɢɨɚɤɬɢɜɧɨɝɨ ɡɚɝɪɹɡɧɟɧɢɹ. ȼɬɨɪɨɣ ɋɢɛɢɪɫɤɢɣ ɤɨɧɝɪɟɫɫ ɩɨ ɉɪɢɤɥɚɞɧɨɣ ɢ ɂɧɞɭɫɬɪɢɚɥɶɧɨɣ Ɇɚɬɟɦɚɬɢɤɟ, ɩɨɫɜɹɳɟɧɧɵɣ ɩɚɦɹɬɢ Ⱥ.Ⱥ.Ʌɹɩɭɧɨɜɚ (1911-1973), Ⱥ.ɉ.ȿɪɲɨɜɚ (1931-1988) ɢ ɂ.Ⱥ.ɉɨɥɟɬɚɟɜɚ (1915-1983). ɇɨɜɨɫɢɛɢɪɫɤ, ɢɸɧɶ 1996. Ɍɟɡɢɫɵ ɞɨɤɥɚɞɨɜ, ɱɚɫɬɶ 1. ɂɡɞ. ɂɧɫɬɢɬɭɬɚ ɦɚɬɟɦɚɬɢɤɢ ɋɈ ɊȺɇ. ɋ. 46-47. 204. ɋɚɜɱɟɧɤɨ Ⱥ.Ⱥ., Ɋɨɫɫɢɟɜ Ⱦ.Ⱥ., Ɂɚɯɚɪɨɜɚ Ʌ.Ȼ. ɉɪɢɦɟɧɟɧɢɟ ɧɟɣɪɨɫɟɬɟɜɨɝɨ ɤɥɚɫɫɢɮɢɤɚɬɨɪɚ ɞɥɹ ɢɡɭɱɟɧɢɹ ɢ ɞɢɚɝɧɨɫɬɢɤɢ ɜɢɥɸɣɫɤɨɝɨ ɷɧɰɟɮɚɥɢɬɚ // Ɍɟɡɢɫɵ ɞɨɤɥɚɞɨɜ ɪɚɛɨɱɟɝɨ ɫɟɦɢɧɚɪɚ "ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɵ", Ʉɪɚɫɧɨɹɪɫɤ, 8-11 ɨɤɬɹɛɪɹ 1993 ɝ., Ʉɪɚɫɧɨɹɪɫɤ.1993.- ɋ.17. 205. ɋɚɜɱɟɧɤɨ Ⱥ.Ⱥ., Ɋɨɫɫɢɟɜ Ⱦ.Ⱥ., ɇɨɡɞɪɚɱɟɜ Ʉ.Ƚ., Ⱦɨɝɚɞɢɧ ɋ.Ⱥ. Ɉɛɭɱɟɧɢɟ ɧɟɣɪɨɫɟɬɟɜɨɝɨ ɤɥɚɫɫɢɮɢɤɚɬɨɪɚ ɞɢɮɮɟɪɟɧɰɢɪɨɜɚɬɶ ɩɨɥ ɱɟɥɨɜɟɤɚ ɩɨ ɦɟɬɚɛɨɥɢɱɟɫɤɢɦ ɢ ɝɨɪɦɨɧɚɥɶɧɵɦ ɩɨɤɚɡɚɬɟɥɹɦ // ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ. Ɍɟɡ. ɞɨɤɥ. ȼɫɟɪɨɫɫɢɣɫɤɨɝɨ ɪɚɛɨɱɟɝɨ ɫɟɦɢɧɚɪɚ 7 - 10 ɨɤɬɹɛɪɹ 1994 ɝ. Ʉɪɚɫɧɨɹɪɫɤ.- 1994.ɋ.47. 206. ɋɚɜɱɟɧɤɨ Ⱥ.Ⱥ., Ɋɨɫɫɢɟɜ Ⱦ.Ⱥ., ɇɨɡɞɪɚɱɟɜ Ʉ.Ƚ., Ⱦɨɝɚɞɢɧ ɋ.Ⱥ. ɉɨɞɬɜɟɪɠɞɟɧɢɟ ɫ ɩɨɦɨɳɶɸ ɧɟɣɪɨɫɟɬɟɜɨɝɨ ɤɥɚɫɫɢɮɢɤɚɬɨɪɚ ɫɭɳɟɫɬɜɨɜɚɧɢɹ ɝɨɦɟɨɫɬɚɬɢɱɟɫɤɢɯ ɭɪɨɜɧɟɣ ɜ ɝɪɭɩɩɟ ɩɪɚɤɬɢɱɟɫɤɢ ɡɞɨɪɨɜɵɯ ɥɸɞɟɣ // ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ. Ɍɟɡ. ɞɨɤɥ. ȼɫɟɪɨɫɫɢɣɫɤɨɝɨ ɪɚɛɨɱɟɝɨ ɫɟɦɢɧɚɪɚ 7 - 10 ɨɤɬɹɛɪɹ 1994 ɝ. Ʉɪɚɫɧɨɹɪɫɤ.- 1994.- ɋ.49. 207. ɋɚɜɱɟɧɤɨ Ⱥ.Ⱥ., Ɋɨɫɫɢɟɜ Ⱦ.Ⱥ., ɇɨɡɞɪɚɱɟɜ Ʉ.Ƚ., Ⱦɨɝɚɞɢɧ ɋ.Ⱥ., Ƚɢɥɟɜ ɋ.ȿ. ɇɟɣɪɨɤɥɚɫɫɢɮɢɤɚɬɨɪ, ɞɢɮɮɟɪɟɧɰɢɪɭɸɳɢɣ ɩɨɥ ɱɟɥɨɜɟɤɚ ɩɨ ɦɟɬɚɛɨɥɢɱɟɫɤɢɦ ɢ ɝɨɪɦɨɧɚɥɶɧɵɦ ɩɨɤɚɡɚɬɟɥɹɦ. ȼɬɨɪɨɣ ɋɢɛɢɪɫɤɢɣ ɤɨɧɝɪɟɫɫ ɩɨ ɉɪɢɤɥɚɞɧɨɣ ɢ ɂɧɞɭɫɬɪɢɚɥɶɧɨɣ Ɇɚɬɟɦɚɬɢɤɟ, ɩɨɫɜɹɳɟɧɧɵɣ ɩɚɦɹɬɢ Ⱥ.Ⱥ.Ʌɹɩɭɧɨɜɚ (1911-1973), Ⱥ.ɉ.ȿɪɲɨɜɚ (1931-1988) ɢ ɂ.Ⱥ.ɉɨɥɟɬɚɟɜɚ (1915-1983). ɇɨɜɨɫɢɛɢɪɫɤ, ɢɸɧɶ 1996. Ɍɟɡɢɫɵ ɞɨɤɥɚɞɨɜ, ɱɚɫɬɶ 1. ɂɡɞ. ɂɧɫɬɢɬɭɬɚ ɦɚɬɟɦɚɬɢɤɢ ɋɈ ɊȺɇ. ɋ. 47. 208. ɋɚɜɱɟɧɤɨ Ⱥ.Ⱥ., ɋɦɢɪɧɨɜɚ ɋ.ȼ., Ɋɨɫɫɢɟɜ Ⱦ.Ⱥ. ɉɪɢɦɟɧɟɧɢɟ ɧɟɣɪɨɫɟɬɟɜɨɝɨ ɤɥɚɫɫɢɮɢɤɚɬɨɪɚ ɞɥɹ ɢɡɭɱɟɧɢɹ ɢ ɞɢɚɝɧɨɫɬɢɤɢ ɚɥɥɟɪɝɢɱɟɫɤɢɯ ɢ ɩɫɟɜɞɨɚɥɥɟɪɝɢɱɟɫɤɢɯ ɪɟɚɤɰɢɣ //
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ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ. Ɍɟɡ. ɞɨɤɥ. ȼɫɟɪɨɫɫɢɣɫɤɨɝɨ ɪɚɛɨɱɟɝɨ ɫɟɦɢɧɚɪɚ 7 - 10 ɨɤɬɹɛɪɹ 1994 ɝ. Ʉɪɚɫɧɨɹɪɫɤ.- 1994.ɋ. 48. 209. ɋɟɧɚɲɨɜɚ Ɇ.ɘ. Ɇɟɬɨɞ ɨɛɪɚɬɧɨɝɨ ɪɚɫɩɪɨɫɬɪɚɧɟɧɢɹ ɬɨɱɧɨɫɬɢ ɫ ɭɱɟɬɨɦ ɧɟɡɚɜɢɫɢɦɨɫɬɢ ɩɨɝɪɟɲɧɨɫɬɟɣ ɫɢɝɧɚɥɨɜ ɫɟɬɢ // Ɍɟɡ.ɤɨɧɮ.ɦɨɥɨɞɵɯ ɭɱɟɧɵɯ Ʉɪɚɫɧɨɹɪɫɤɨɝɨ ɧɚɭɱɧɨɝɨ ɰɟɧɬɪɚ. – Ʉɪɚɫɧɨɹɪɫɤ, ɉɪɟɡɢɞɢɭɦ Ʉɇɐ ɋɈ ɊȺɇ, 1996, ɫɫ.96-97. 210. ɋɟɧɚɲɨɜɚ Ɇ.ɘ. Ɇɟɬɨɞ ɨɛɪɚɬɧɨɝɨ ɪɚɫɩɪɨɫɬɪɚɧɟɧɢɹ ɬɨɱɧɨɫɬɢ. // ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ. Ɍɟɡ. ɞɨɤɥ. IV ȼɫɟɪɨɫɫɢɣɫɤɨɝɨ ɫɟɦɢɧɚɪɚ, 5 - 7 ɨɤɬɹɛɪɹ, 1996 ɝ. Ʉɪɚɫɧɨɹɪɫɤ; ɄȽɌɍ. 1996, ɫ.47 211. ɋɟɧɚɲɨɜɚ Ɇ.ɘ. ɉɨɝɪɟɲɧɨɫɬɢ ɜ ɧɟɣɪɨɧɧɵɯ ɫɟɬɹɯ / ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɇɨɜɨɫɢɛɢɪɫɤ: ɇɚɭɤɚ, ɋɢɛɢɪɫɤɚɹ ɢɡɞɚɬɟɥɶɫɤɚɹ ɮɢɪɦɚ ɊȺɇ, 1998. 212. ɋɟɧɚɲɨɜɚ Ɇ.ɘ. ɍɩɪɨɳɟɧɢɟ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ: ɩɪɢɛɥɢɠɟɧɢɟ ɡɧɚɱɟɧɢɣ ɜɟɫɨɜ ɫɢɧɚɩɫɨɜ ɩɪɢ ɩɨɦɨɳɢ ɰɟɩɧɵɯ ɞɪɨɛɟɣ. ȼɵɱɢɫɥɢɬɟɥɶɧɵɣ ɰɟɧɬɪ ɋɈ ɊȺɇ ɜ ɝ. Ʉɪɚɫɧɨɹɪɫɤɟ. Ʉɪɚɫɧɨɹɪɫɤ, 1997. 11 ɫ., ɛɢɛɥɢɨɝɪ. 6 ɧɚɡɜ. (Ɋɭɤɨɩɢɫɶ ɞɟɩ. ɜ ȼɂɇɂɌɂ 25.07.97, ʋ 2510-ȼ97) 213. ɋɟɧɚɲɨɜɚ. Ɇ.ɘ. ɍɩɪɨɳɟɧɢɟ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ. ɂɫɩɨɥɶɡɨɜɚɧɢɟ ɰɟɩɧɵɯ ɞɪɨɛɟɣ ɞɥɹ ɩɪɢɛɥɢɠɟɧɢɹ ɜɟɫɨɜ ɫɢɧɚɩɫɨɜ. // ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ: Ɍɟɡɢɫɵ ɞɨɤɥɚɞɨɜ V ȼɫɟɪɨɫɫɢɣɫɤɨɝɨ ɫɟɦɢɧɚɪɚ, 3-5 ɨɤɬɹɛɪɹ, 1997 ɝ., Ʉɪɚɫɧɨɹɪɫɤ; ɄȽɌɍ. 1997, ɫ. 165-166. 214. ɋɨɤɨɥɨɜ ȿ.ɇ., ȼɚɣɬɤɹɜɢɱɭɫ Ƚ.Ƚ. ɇɟɣɪɨɢɧɬɟɥɥɟɤɬ: ɨɬ ɧɟɣɪɨɧɚ ɤ ɧɟɣɪɨɤɨɦɩɶɸɬɟɪɭ. Ɇ.: ɇɚɭɤɚ, 1989. 238 ɫ. 215. ɋɬɟɩɚɧɹɧ Ⱥ.Ⱥ., Ⱥɪɯɚɧɝɟɥɶɫɤɢɣ ɋ.ȼ. ɉɨɫɬɪɨɟɧɢɟ ɥɨɝɢɱɟɫɤɢɯ ɫɯɟɦ ɧɚ ɩɨɪɨɝɨɜɵɯ ɷɥɟɦɟɧɬɚɯ. Ʉɭɣɛɵɲɟɜɫɤɨɟ ɤɧɢɠɧɨɟ ɢɡɞ-ɜɨ, 1967. 216. ɋɭɞɚɪɢɤɨɜ ȼ.Ⱥ. ɂɫɫɥɟɞɨɜɚɧɢɟ ɚɞɚɩɬɢɜɧɵɯ ɧɟɣɪɨɫɟɬɟɜɵɯ ɚɥɝɨɪɢɬɦɨɜ ɪɟɲɟɧɢɹ ɡɚɞɚɱ ɥɢɧɟɣɧɨɣ ɚɥɝɟɛɪɵ // ɇɟɣɪɨɤɨɦɩɶɸɬɟɪ, 1992. ʋ 3,4. ɋ. 13-20. 217. Ɍɚɪɚɫɨɜ Ʉ.ȿ., ȼɟɥɢɤɨɜ ȼ.Ʉ., Ɏɪɨɥɨɜɚ Ⱥ.ɂ. Ʌɨɝɢɤɚ ɢ ɫɟɦɢɨɬɢɤɚ ɞɢɚɝɧɨɡɚ (ɦɟɬɨɞɨɥɨɝɢɱɟɫɤɢɟ ɩɪɨɛɥɟɦɵ).- Ɇ.: Ɇɟɞɢɰɢɧɚ, 1989.- 272 ɫ. 218. Ɍɟɪɟɯɨɜ ɋ.Ⱥ. ɇɟɣɪɨɫɟɬɟɜɵɟ ɢɧɮɨɪɦɚɰɢɨɧɧɵɟ ɦɨɞɟɥɢ ɫɥɨɠɧɵɯ ɢɧɠɟɧɟɪɧɵɯ ɫɢɫɬɟɦ / ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɇɨɜɨɫɢɛɢɪɫɤ: ɇɚɭɤɚ, ɋɢɛɢɪɫɤɚɹ ɢɡɞɚɬɟɥɶɫɤɚɹ ɮɢɪɦɚ ɊȺɇ, 1998. 219. Ɍɪɚɧɫɩɶɸɬɟɪɧɵɟ ɢ ɧɟɣɪɨɧɧɵɟ ɗȼɆ. /ɉɨɞ ɪɟɞ. ȼ.Ʉ.Ʌɟɜɢɧɚ ɢ Ⱥ.ɂ.Ƚɚɥɭɲɤɢɧɚ - Ɇ.: Ɋɨɫɫɢɣɫɤɢɣ Ⱦɨɦ ɡɧɚɧɢɣ, 1992. 220. ɍɢɞɪɨɭ Ȼ., ɋɬɢɪɧɡ ɋ. Ⱥɞɚɩɬɢɜɧɚɹ ɨɛɪɚɛɨɬɤɚ ɫɢɝɧɚɥɨɜ. Ɇ.: Ɇɢɪ, 1989. 440 ɫ. 221. ɍɨɫɫɟɪɦɟɧ Ɏ. ɇɟɣɪɨɤɨɦɩɶɸɬɟɪɧɚɹ ɬɟɯɧɢɤɚ.- Ɇ.: Ɇɢɪ, 1992. 222. Ɏɟɞɨɬɨɜ ɇ.Ƚ. Ɇɟɬɨɞɵ ɫɬɨɯɚɫɬɢɱɟɫɤɨɣ ɝɟɨɦɟɬɪɢɢ ɜ ɪɚɫɩɨɡɧɚɜɚɧɢɢ ɨɛɪɚɡɨɜ. - Ɇ.: Ɋɚɞɢɨ ɢ ɫɜɹɡɶ, 1990.- 144 ɫ. 223. Ɏɨɪ Ⱥ. ȼɨɫɩɪɢɹɬɢɟ ɢ ɪɚɫɩɨɡɧɚɜɚɧɢɟ ɨɛɪɚɡɨɜ.- Ɇ.: Ɇɚɲɢɧɨɫɬɪɨɟɧɢɟ, 1989.- 272 ɫ. 224. Ɏɪɨɥɨɜ Ⱥ.Ⱥ., Ɇɭɪɚɜɶɟɜ ɂ.ɉ. ɂɧɮɨɪɦɚɰɢɨɧɧɵɟ ɯɚɪɚɤɬɟɪɢɫɬɢɤɢ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ. - Ɇ.: ɇɚɭɤɚ, 1988. 225. Ɏɪɨɥɨɜ Ⱥ.Ⱥ., Ɇɭɪɚɜɶɟɜ ɂ.ɉ. ɇɟɣɪɨɧɧɵɟ ɦɨɞɟɥɢ ɚɫɫɨɰɢɚɬɢɜɧɨɣ ɩɚɦɹɬɢ.- Ɇ.: ɇɚɭɤɚ, 1987.160 ɫ. 226. Ɏɭ Ʉ. ɋɬɪɭɤɬɭɪɧɵɟ ɦɟɬɨɞɵ ɜ ɪɚɫɩɨɡɧɚɜɚɧɢɢ ɨɛɪɚɡɨɜ.- Ɇ.: Ɇɢɪ, 1977.- 320 ɫ. 227. Ɏɭɤɭɧɝɚ Ʉ. ȼɜɟɞɟɧɢɟ ɜ ɫɬɚɬɢɫɬɢɱɟɫɤɭɸ ɬɟɨɪɢɸ ɪɚɫɩɨɡɧɚɜɚɧɢɹ ɨɛɪɚɡɨɜ.- Ɇ.: ɇɚɭɤɚ, 1979.367 ɫ. 228. ɏɚɪɬɦɚɧ Ƚ. ɋɨɜɪɟɦɟɧɧɵɣ ɮɚɤɬɨɪɧɵɣ ɚɧɚɥɢɡ.- Ɇ.: ɋɬɚɬɢɫɬɢɤɚ, 1972.- 486 ɫ. 229. ɏɢɦɦɟɥɶɛɥɚɭ Ⱦ. ɉɪɢɤɥɚɞɧɨɟ ɧɟɥɢɧɟɣɧɨɟ ɩɪɨɝɪɚɦɦɢɪɨɜɚɧɢɟ. Ɇ.: Ɇɢɪ, 1975. 534 ɫ. 230. ɏɢɧɬɨɧ Ⱦɠ.ȿ. Ɉɛɭɱɟɧɢɟ ɜ ɩɚɪɚɥɥɟɥɶɧɵɯ ɫɟɬɹɯ / Ɋɟɚɥɶɧɨɫɬɶ ɢ ɩɪɨɝɧɨɡɵ ɢɫɤɭɫɫɬɜɟɧɧɨɝɨ ɢɧɬɟɥɥɟɤɬɚ.- Ɇ.: Ɇɢɪ, 1987.- ɋ. 124-136. 231. ɐɚɪɟɝɨɪɨɞɰɟɜ ȼ.Ƚ. Ɍɪɚɧɫɩɨɧɢɪɨɜɚɧɧɚɹ ɥɢɧɟɣɧɚɹ ɪɟɝɪɟɫɫɢɹ ɞɥɹ ɢɧɬɟɪɩɨɥɹɰɢɢ ɫɜɨɣɫɬɜ ɯɢɦɢɱɟɫɤɢɯ ɷɥɟɦɟɧɬɨɜ // ɇɟɣɪɨɢɧɮɨɪɦɚɬɢɤɚ ɢ ɟɟ ɩɪɢɥɨɠɟɧɢɹ. Ɍɟɡɢɫɵ ɞɨɤɥɚɞɨɜ 5 ȼɫɟɪɨɫɫɢɣɫɤɨɝɨ ɫɟɦɢɧɚɪɚ, 3-5 ɨɤɬɹɛɪɹ 1997 ɝ. / ɉɨɞ ɪɟɞ. Ⱥ.ɇ.Ƚɨɪɛɚɧɹ. Ʉɪɚɫɧɨɹɪɫɤ: ɢɡɞ. ɄȽɌɍ, 1997. ɋ. 177-178. 232. ɐɵɝɚɧɤɨɜ ȼ.Ⱦ. ɇɟɣɪɨɤɨɩɶɸɬɟɪ ɢ ɟɝɨ ɩɪɢɦɟɧɟɧɢɟ.- Ɇ.: "ɋɨɥ ɋɢɫɬɟɦ", 1993. 233. ɐɵɩɤɢɧ ə.Ɂ. Ɉɫɧɨɜɵ ɬɟɨɪɢɢ ɨɛɭɱɚɸɳɢɯɫɹ ɫɢɫɬɟɦ. Ɇ.: ɇɚɭɤɚ, 1970. 252 ɫ. 234. ɒɚɣɞɭɪɨɜ ȼ.ȼ. Ɇɧɨɝɨɫɟɬɨɱɧɵɟ ɦɟɬɨɞɵ ɤɨɧɟɱɧɵɯ ɷɥɟɦɟɧɬɨɜ. - Ɇ.: ɇɚɭɤɚ, 1989. 235. ɒɜɚɪɰ ɗ., Ɍɪɢɫ Ⱦ. ɉɪɨɝɪɚɦɦɵ, ɭɦɟɸɳɢɟ ɞɭɦɚɬɶ // Ȼɢɡɧɟɫ ɍɢɤ.- 1992.- N.6.- ɋ.15-18. 236. ɒɟɧɤ Ɋ., ɏɚɧɬɟɪ Ʌ. ɉɨɡɧɚɬɶ ɦɟɯɚɧɢɡɦɵ ɦɵɲɥɟɧɢɹ / Ɋɟɚɥɶɧɨɫɬɶ ɢ ɩɪɨɝɧɨɡɵ ɢɫɤɭɫɫɬɜɟɧɧɨɝɨ ɢɧɬɟɥɥɟɤɬɚ.- Ɇ.: Ɇɢɪ, 1987.- ɋ. 15-26. 237. ɓɟɪɛɚɤɨɜ ɉ.ɋ. Ȼɢɛɥɢɨɝɪɚɮɢɱɟɫɤɚɹ ɛɚɡɚ ɞɚɧɧɵɯ ɩɨ ɦɟɬɨɞɚɦ ɧɚɫɬɪɨɣɤɢ ɧɟɣɪɨɧɧɵɯ ɫɟɬɟɣ // ɇɟɣɪɨɤɨɦɩɶɸɬɟɪ, 1993. ʋ 3,4. ɋ.5-8. 238. Aleksander I., Morton H. The logic of neural cognition // Adv. Neural Comput.- Amsterdam etc., 1990.- PP. 97-102. 239. Alexander S. Th. Adaptive Signal Processing. Theory and Applications. Springer. 1986. 179 p.
CHAPLIT.DOC
182
240. Allen J., Murray A.. Development of a neural network screening aid for diagnosing lower limb peripheral vascular disease from photoelectric plethysmography pulse waveforms // Physiol. Meas.- 1993.- V.14, N.1.- P.13-22. 241. Amari Sh., Maginu K. Statistical Neurodynamics of Associative Memory // Neural Networks, 1988. V.1. N1. P. 63-74. 242. Arbib M.A. Brains, Machines, and Mathematics. Springer, 1987. 202 p. 243. Astion M.L., Wener M.H., Thomas R.G., Hunder G.G., Bloch D.A. Application of neural networks to the classification of giant cell arteritis // Arthritis Reum.- 1994.- V.37, N.5.- P.760770. 244. Aynsley M., Hofland A., Morris A.J. et al. Artificial intelligence and the supervision of bioprocesses (real-time knowledge-based systems and neural networks) // Adv. Biochem. Eng. Biotechnol.- 1993.- N.48.- P.1-27. 245. Baba N. New Topics in Learning Automate Theory and Applications. Springer, 1985. 131 p. (Lec. Not. Control and Information, N71). 246. Barschdorff D., Ester S., Dorsel T et al. Phonographic diagnostic aid in heart defects using neural networks // Biomed. Tech. Berlin.- 1990.- V.35, N.11.- P.271-279. 247. Bartsev S.I., Okhonin V.A. Optimization and Monitoring Needs: Possible Mechanisms of Control of Ecological Systems. Nanobiology, 1993, v.2, p.165-172. 248. Bartsev S.I., Okhonin V.A. Self-learning neural networks playing "Two coins"// Proc. of International Workshop "Neurocomputers and attention II", Manchester Univ.Press, 1991, p.453458. 249. Bartsev S.I., Okhonin V.A. The algorithm of dual functioning (back-propagation): general approuch, versions and applications. Preprint of Biophysics Institute SB AS USSR, Krasnoyarsk, 1989, ʋ107B, 16 p. 250. Bartsev S.I., Okhonin V.A. Variation principle and algorithm of dual functioning: examples and applications// Proc. of International Workshop "Neurocomputers and attention II", Manchester Univ.Press, 1991, p.445-452. 251. Baxt W.G. A neural network trained to identify the presence of myocardial infarction bases some decisions on clinical associations that differ from accepted clinical teaching // Med. Decis. Making.- 1994.- V.14, N.3.- P.217-222. 252. Baxt W.G. Analysis of the clinical variables driving decision in an artificial neural network trained to identify the presence of myocardial infarction // Ann. Emerg. Med.- 1992.- V.21, N.12.P.1439-1444. 253. Baxt W.G. Complexity, chaos and human physiology: the justification for non-linear neural computational analysis // Cancer Lett.- 1994.- V.77, N.2-3.- P.85-93. 254. Baxt W.G. Use of an artificial neural network for the diagnosis of myocardial infarction // Ann. Intern. Med.- 1991.- V.115, N.11.- P.843-848. 255. Borisov A.G., Gilev S.E., Golovenkin S.E., Gorban A.N., Dogadin S.A., Kochenov D.A., Maslennikova E.V., Matyushin G.V., Mirkes Ye.M., Nozdrachev K.G., Rossiyev D.A., Savchenko A.A., Shulman V.A. "MultiNeuron" neural simulator and its medical applications // Modelling, Measurement & Control, C.- 1996.- V.55, N.1.- P.1-5. 256. Bruck J., Goodman J. W. On the power of neural networks for solving hard problems // J. Complex.- 1990.- 6, ʋ 2.PP. 129-135. 257. Budilova E.V., Teriokhin A.T. Endocrine networks // The RNNS/IEEE Symposium on Neuroinformatics and Neurocomputers, Rostov-on-Don, Russia, October 7-10, 1992.Rostov/Don, 1992.- V.2.- P.729-737. 258. Carpenter G.A., Grossberg S. A Massivly Parallel Architecture for a Self-Organizing Neural Pattern Recognition Machine. - Computer Vision, Graphics, and Image Processing, 1987. Vol. 37. PP. 54-115. 259. Connectionism in Perspective/Ed. by R. Pfeifer, Z. Schreter, F.Fogelman-Soulie and L. Steels. North-Holland, 1989. 518 p. 260. Cybenko G. Approximation by superposition of a sigmoidal function. - Mathematics of Control, Signals, and Systems, 1989. Vol. 2. PP. 303 - 314. 261. Diday E., Simon J.C. Clustering analysis, (dans Digital Pattern Recognition), Redacteur: K.S.F.U., Springer Verlag, Berlin, 1980, P. 47-93. 262. Disordered Systems and biological Organization/Ed. by Bienenstock F., Fogelman-Soulie G. Weisbuch. Springer, 1986. 405 p.
CHAPLIT.DOC
183
263. Dorrer M.G., Gorban A.N., Kopytov A.G., Zenkin V.I. Psychological intuition of neural networks. Proceedings of the WCNN'95 (World Congress on Neural Networks'95, Washington DC, July 1995). PP. 193-196. 264. Dorrer M.G., Gorban A.N., Zenkin V.I. Neural networks in psychology: classical explicit diagnoses // Neuroinformatics and Neurocomputers, Proceedings of the second RNNS-IEEE Simposium, Rostov-na-Donu, September 1995. PP. 281-284. 265. Draper N. R. Applied regression analysis bibliographi update 1988-89 // Commun. Statist. Theory and Meth.- 1990.1990.- 19, ʋ 4.- PP. 1205-1229. 266. Ercal F., Chawla A., Stoeker W.V. et al. Neural network diagnosis of malignant melanoma from color images // IEEE Trans. Biomed. Eng.- 1994.- V.41, N.9.- P.837-845. 267. Ferretti C., Mauri G. NNET: some tools for neural Networks simulation // 9th Annu. Int. Phoenix Conf. Comput. and Commun., Scottsdate, Ariz., March 21-23, 1990.- Los Alamitos (Calif.) etc., 1990.- PP. 38-43. 268. Filho E.C.D.B.C., Fairhurst M.C., Bisset D.L. Adaptive pattern recognition using goal seeking neurons // Pattern Recogn. Lett.- 1991.- 12, ʋ 3.- PP. 131-138. 269. Floyd C.E.Jr., Lo J.Y., Yun A.J. et al. Prediction of breast cancer malignancy using an artificial neural network // Cancer.- 1994.- V.74, N.11.- P.2944-2948. 270. Forbes A.B., Mansfield A.J. Neural implementation of a method for solving systems of linear algebraic equations // Nat. Phys. Lab. Div. Inf. Technol. and Comput. Rept.- 1989.ʋ 155.- PP. 114. 271. Fu H.C., Shann J.J. A fuzzy neural network for knowledge learning // Int. J. Neural Syst.- 1994.V.5, N.1.- P.13-22. 272. Fukushima K. Neocognitron: A self-organizing Neural Network model for a Mechanism of Pattern Recognition uneffected by shift in position // Biological Cybernetics.1980. V. 36, ʋ 4. PP. 193202. 273. Fulcher J. Neural networks: promise for the future? // Future Generat. Comput. Syst.- 1990-1991.6, ʋ 4.- PP. 351-354. 274. Gallant A.R., White H. There exist a neural network that does not make avoidable mistakes. IEEE Second International Coferense on Neural Networks, San Diego, CA, New York: IEEE Press, vol. 1, 1988. PP. 657 - 664. 275. Gecseg F. Products of Automata. Springer, 1986. 107 p. 276. Gemignani M. C. Liability for malfunction of an expert system // IEEE Conf. Manag. Expert Syst. Program and Proj., Bethesda, Md. Sept. 10-12, 1990: Proc.- Los Alamitos (Calif.) etc., 1990.- PP. 8-15. 277. Genis C. T. Relaxation and neural learning: points of convergence and divergence // J. Parallel and Distrib. Comput.- 1989.- 6, ʋ 2.- PP. 217-244. 278. George N., Wang hen-ge, Venable D.L. Pattern recognition using the ring-wedge detector and neural-network software: [Pap.] Opt. Pattern Recogn. II: Proc. Meet., Paris, 26-27 Apr., 1989 // Proc. Soc. Photo-Opt. Instrum. Eng.- 1989.- PP. 96-106. 279. Gilev S.E. A non-back-propagation method for obtaining the gradients of estimate function // Advances in Modelling & Analysis, A, AMSE Press, 1995. Vol. 29, ʋ 1. PP. 51-57. 280. Gilev S.E., Gorban A.N. On Completeness of the Class of Functions Computable by Neural Networks, Proc. of the World Congress on Neural Networks, Sept. 15-18, 1996, San Diego, CA, Lawrence Erlbaum Associates, 1996, pp. 984-991. 281. Gilev S.E., Gorban A.N., Kochenov D.A., Mirkes Ye.M., Golovenkin S.E., Dogadin S.A., Nozdrachev K.G., Maslennikova E.V., Matyushin G.V., Rossiev D.A., Shulman V.A., Savchenko A.A. "MultiNeuron" neural simulator and its medical applications // Proceedings of International Conference on Neural Information Processing, Oct. 17-20, 1994, Seoul, Korea.- V.2.- P.12611264. 282. Gilev S.E., Gorban A.N., Mirkes E.M. Internal Conflicts in Neural Networks // Transactions of IEEE-RNNS Simposium (Rostov-on-Don, September 1992). V.1. PP. 219-226. 283. Gilev S.E., Gorban A.N., Mirkes E.M. Several Methods for Accelerating the Traning Process of Neural Networks in Pattern Recognition. - Advances in Modelling & Analysis, A, AMSE Press, V. 12, No. 4, 1992, pp. 29-53. 284. Gilev S.E., Gorban A.N., Mirkes E.M. Small Experts and Internal Conflicts in Leanable Neural Networks // Advances in Modelling & Analysis.- AMSE Press.- 1992.- V.24, No. 1.P.45-50. 285. Gileva L.V., Gilev S.E. Neural Networks for binary classification// AMSE Transaction, Scientific Siberian, A, 1993, Vol. 6. Neurocomputing, pp. 135-167.
CHAPLIT.DOC
184
286. Gindi G.R., Darken C.J., O’Brien K.M. et al. Neural network and conventional classifiers for fluorescence-guided laser angioplasty // IEEE Trans. Biomed. Eng.- 1991.- V.38, N.3.- P.246-252. 287. Gluck M.A., Parker D.B., Reifsnider E.S. Some Biological Implications of a Differential-Hebbian Learning Rule. - Psychobiology, 1988. Vol. 16. No. 3. PP. 298-302. 288. Golub D.N. and Gorban A.N. Multi-Particle Networks for Associative Memory, Proc. of the World Congress on Neural Networks, Sept. 15-18, 1996, San Diego, CA, Lawrence Erlbaum Associates, 1996, pp. 772-775. 289. Gorban A.N., Novokhodko A.Yu.. Neural Networks In Transposed Regression Problem, Proc. of the World Congress on Neural Networks, Sept.15-18, 1996, San Diego, CA, Lawrence Erlbaum Associates, 1996, pp. 515-522. 290. Gorban A.N. Neurocomputing in Siberia // Advances in Modelling & Analysis.- AMSE Press.1992.- V.34(2).P.21-28. 291. Gorban A.N. Systems with inheritance and effects of selection.- Scientific Siberian A, Volume 1. Ecology, AMSE Press, ISBN: 2-909214-04-4, 1992, pp. 82-126 292. Gorban A.N., Mirkes Ye.M. and Wunsch D.C. II High order ortogonal tensor networks: Information capacity and reliability // ICNN97 (The 1997 IEEE International Conference on Neural Networks), Houston, IEEE, 1997. PP. 1311-1314. 293. Gorban A.N., Mirkes Ye.M. Functional Components of Neurocomputer. Ɍɪɭɞɵ ɬɪɟɬɶɟɣ ɦɟɠɞɭɧɚɪɨɞɧɨɣ ɤɨɧɮɟɪɟɧɰɢɢ "Ɇɚɬɟɦɚɬɢɤɚ, ɤɨɦɩɶɸɬɟɪ, ɨɛɪɚɡɨɜɚɧɢɟ". - Ɇɨɫɤɜɚ, 1996. ɫ.352-359. 294. Gorban A.N., Mirkes Ye.M. Functional components of neurocomputer. 3-d International conference "Mathematics, computer, education", Dubna, Jan. 1996. Abstracts, p. 160. 295. Gorban A.N., Rossiev D.A., Butakova E.V., Gilev S.E., Golovenkin S.E., Dogadin S.A., Dorrer M.G., Kochenov D.A., Kopytov A.G., Maslennikova E.V., Matyushin G.V., Mirkes Ye.M., Nazarov B.V., Nozdrachev K.G., Savchenko A.A., Smirnova S.V., Shulman V.A., Zenkin V.I. Medical, psychological and physiological applications of MultiNeuron neural simulator. Neuroinformatics and Neurocomputers, Proceedings of the second RNNS-IEEE Simposium, Rostov-na-Donu, September 1995.- PP. 7-14. 296. Gorban A.N., Rossiev D.A., Gilev S.E. et al. “NeuroComp” group: neural-networks software and its application // Russian Academy of Sciences, Krasnoyarsk Computing Center, Preprint N 8.Krasnoyarsk, 1995.- 38 p. 297. Gorban A.N., Rossiev D.A., Gilev S.E. et al. Medical and physiological applications of MultiNeuron neural simulator // Proceedings of World Congress on Neural Networks-1995 (WCNN’95).- Washington, 1995.- V.1, P.170-175. 298. Gorban A.N., Rossiev D.A., Gilev S.E., Dorrer M.A., Kochenov D.A., Mirkes Ye.M., Golovenkin S.E., Dogadin S.A., Nozdrachev K.G., Matyushin G.V., Shulman V.A., Savchenko A.A. Medical and physiological applications of MultiNeuron neural simulator // Proceedings of World Congress on Neural Networks - 1995 (WCNN'95).- PP. 170-175. 299. Gorban A.N., Waxman C. How many neurons are sufficient to elect the U.S.A. President?// AMSE Transaction, Scientific Siberian, A, 1993, Vol. 6. Neurocomputing, pp. 168-188. 300. Gorban A.N., Waxman C. How many Neurons are Sufficient to Elect the U.S.A. President? TWO! (Siberian neurocomputer forecasts results of U.S.A. Presidential elections) Krasnoyarsk, Institute of Biophysics, Russian Academy of Sciences Siberian Branch, 1992 - 29 pp. (preprint Russian Academy of Sciences Siberian Branch, Institute of Biophysics N 191 Ȼ). 301. Gorban A.N., Waxman C. Neural networks for political forecast. Proceedings of the WCNN'95 (World Congress on Neural Networks'95, Washington DC, July 1995). PP. 179-184. 302. Gordienko P. Construction of efficient neural networks // Proceedings of the International Conference on Neural Information Processing (Oct. 17-20, 1994, Seoul, Korea) V.1. PP. 366-371. 303. Gordienko P. How to obtain a maximum of skills with minimum numbers of connections// AMSE Transaction, Scientific Siberian, A, 1993, Vol. 6. Neurocomputing, pp.204-208. 304. Gross G.W., Boone J.M., Greco-Hunt V. et al. Neural networks in radiologic diagnosis. II. Interpretation of neonatal chest radiographs // Invest. Radiol.- 1990.- V.25, N.9.- P.1017-1023. 305. Grossberg S. Nonlinear Neural Networks: Principles, Mechanism and Architectures// Neural Networks, 1988. V.1. N1. P. 17-62. 306. Guo Z., Durand L.G., Lee H.C. et al. Artificial neural networks in computer-assisted classification of heart sounds in patients with porcine bioprosthetic valves // Med. Biol. Eng. Comput.- 1994.V.32, N.3.- P.311-316. 307. Hecht-Nielsen R. Neurocomputing: Picking the Human Brain/IEEE Spectrum, 1988. March. P. 36-41.
CHAPLIT.DOC
185
308. Heht-Nielsen R. Theory of the backpropagation neural network. - Neural Networks for Human and Mashine Perception. H.Wechsler (Ed.). Vol. 2. Boston, MA: Academic Press, 1992. PP. 65 - 93. 309. Hod H., Lew A.S., Keltai M. et al. Early atrial fibrilla tion during evolving myocardial infarction: a consequence of impaired left atrial perfusion // Circulation, 1987. V.75, N.1. PP. 146-150. 310. Hoher M., Kestler H.A., Palm G. et al. Neural network based QRS classification of the signal averaged electrocardiogram // Eur. Heart J.- 1994.- V.15.- Abstr. Supplement XII-th World Congress Cardiology (734).- P.114. 311. Hopfield J.J. Neural Networks and physical systems with emergent collective computational abilities//Proc. Nat. Sci. USA. 1982. V.79. P. 2554-2558. 312. Hornik K., Stinchcombe M., White H. Multilayer Feedforward Networks are Universal Approximators. - Neural Networks. 1989. Vol. 2,. PP. 359 - 366. 313. Jeffries C. Code recognition with neural network dynamical systems // SIAM Rev.- 1990.- 32, ʋ 4.- PP. 636-651. 314. Kalman R.E. A theory for the identification of linear relations // Frontiers Pure and Appl. Math.: Collect. Pap. Dedicat. Jacques-Louis Lions Occas. His 60th Birthday: Sci. Meet., Paris, 6-10 June, 1988.- Amsterdam etc., 1991.- PP. 117-132. 315. Keller J.M., Yager R.R., Tahani H. Neural network implementation of fuzzy logic // Fuzzy Sets and Syst.1992.- 45, ʋ 1. PP. 1-12. 316. Kirdin A.N., Rossiev D.A., Dorrer M.G. Neural Networks Simulator for Medical, Physiological and Psychological Applications. Ɍɪɭɞɵ ɬɪɟɬɶɟɣ ɦɟɠɞɭɧɚɪɨɞɧɨɣ ɤɨɧɮɟɪɟɧɰɢɢ "Ɇɚɬɟɦɚɬɢɤɚ, ɤɨɦɩɶɸɬɟɪ, ɨɛɪɚɡɨɜɚɧɢɟ". - Ɇɨɫɤɜɚ, 1996. ɫ.360-367. 317. Kirdin A.N., Rossiev D.A.. Neural-networks simulator for medical and physiological applications.3-d International conference "Mathematics, computer, education", Dubna, Jan. 1996. Abstracts, p. 162. 318. Kochenov D.A., Rossiev D.A. Approximations of functions of C[A,B] class by neural-net predictors (architectures and results)// AMSE Transaction, Scientific Siberian, A, 1993, Vol. 6. Neurocomputing. PP. 189-203. 319. Koopmans T. Serial correlation and quadratic forms in normal variates // Ann. Math. Statist, 1942. V. 13. PP. 14-33. 320. Korver M., Lucas P.J. Converting a rule-based expert system into a belief network // Med. Inf. Lond.- 1993.- V.18, N.3.- P.219-241. 321. Kosko B. Bidirectional Associative Memories. - IEEE Transactions on Systems, Man, and Cybernetics, Jan. 1988. Vol. SMC-18. PP.49-60. 322. Lee H.-L., Suzuki S., Adachi Y. et al. Fuzzy Theory in Traditional Chinese Pulse Diagnosis // Proceedings of 1993 International Joint Conference on Neural Networks, Nagoya, Japan, October 25-29, 1993.- Nagoya, 1993.- V.1.- P.774-777. 323. Levine D.S., Parks R.W., Prueitt P.S. Methodological and theoretical issues in neural network models of frontal cognitive functions // Int. J. Neurosci.- 1993.- V.72, N.3-4.- P.209-233. 324. Lichtman A.J., Keilis-Borok V.I., Pattern Recognition as Applied to Presidential Elections in U.S.A., 1860-1980; Role of Integral Social, Economic and Political Traits, Contribution No. 3760. 1981, Division of Geological and Planetary Sciences, California Institute of Technology. 325. Maclin P.S., Dempsey J. Using an artificial neural network to diagnose hepatic masses // J. Med. Syst.- 1992.- V.16, N.5.- P.215-225. 326. Macukow B. Robot control with neural networks // Artif. Intell. and Inf.-Contr. Syst. Rob.-89: Proc. 5th Int. Conf., Strbske Pleso, 6-10 Nov., 1989.- Amsterdam etc., 1989.- PP. 373-376. 327. Mirkes E.M., Svitin A.P. The usage of adaptive neural networks for catalytic activity predictions // CHISA - 10th Int. Congr. of chem. eng., chem. equipment design and automation. Praha, 1990. Prepr. B3.80 [1418]. 7 pp. 328. Modai I., Stoler M., Inbar-Saban N. et al. Clinical decisions for psychiatric inpatients and their evaluation by a trained neural network // Methods Inf. Med.- 1993.- V.32, N.5.- P.396-399. 329. Modha D.S., Heht-Nielsen R. Multilayer Functionals. Mathematical Approaches to Neural Networks. J.G.Taylor (Ed.). Elsevier, 1993. PP. 235 - 260. 330. Nakajima H., Anbe J., Egoh Y. et al. Evaluation of neural network rate regulation system in dual activity sensor rate adaptive pacer // European Journal of Cardiac Pacing and Electrophysiology.Abstracts of 9th International Congress, Nice Acropolis - French, Rivera, June 15-18, (228), 1994.- Rivera, 1994.- P.54. 331. Narendra K.S., Amnasway A.M. A stable Adaptive Systems. Prentice-Hall, 1988. 350 p. 332. Neural Computers/Ed. by R. Eckmiller, Ch. Malsburg. Springer, 1989. 556 p.
CHAPLIT.DOC
186
333. Okamoto Y., Nakano H., Yoshikawa M. et al. Study on decision support system for the interpretation of laboratory data by an artificial neural network // Rinsho. Byori.- 1994.- V.42, N.2.- P.195-199. 334. Pedrycz W. Neurocomputations in relational systems // IEEE Trans. Pattern Anal. and Mach. Intell.- 1991.- 13, ʋ 3.- PP. 289-297. 335. Pham D.T., Liu X. Statespace identification of dynamic systems using neural networks // Eng. Appl. Artif. Intell.1990.- 3, ʋ 3.- PP. 198-203. 336. Pineda F.J. Recurrent bakpropagation and the dynamical approach to adaptive neural computation. - Neural Comput., 1989. Vol. 1. PP.161 - 172. 337. Poli R., Cagnoni S., Livi R. et al. A Neural Network Expert System for Diagnosing and Treating Hypertension // Computer.- 1991.- N.3.- P.64-71. 338. Real Brains, Artificial Minds/Ed. by J.L. Casti, A. Karlqvist. Norton-Holland, 1987. 226 p. 339. Reinbnerger G., Weiss G., Werner-Felmayer G. et al. Neural networks as a tool for utilizing laboratory information: comparison with linear discriminant analysis and with classification and regression trees // Proc. Natl. Acad. Sci., USA.- 1991.- V.88, N.24.- P.11426-11430. 340. Rinast E., Linder R., Weiss H.D. Neural network approach for computer-assisted interpretation of ultrasound images of the gallbladder // Eur. J. Radiol.- 1993.- V.17, N.3.- P.175-178. 341. Rossiev D.A., Golovenkin S.E., Shulman V.A., Matyushin G.V. Forecasting of myocardial infarction complications with the help of neural networks // Proceedings of the WCNN'95 (World Congress on Neural Networks'95, Washington DC, July 1995). PP. 185-188. 342. Rossiev D.A., Golovenkin S.E., Shulman V.A., Matyushin G.V. Neural networks for forecasting of myocardial infarction complications // Proceedings of the Second IEEE RNNS International Symposium on Neuroinformatics and Neurocomputers, September 20-23, 1995, Rostov-on-Don. PP 292-298. 343. Rossiev D.A., Golovenkin S.E., Shulman V.A., Matyushin G.V. The employment of neural networks to model implantation of pacemaker in patients with arrhythmias and heart blocks // Modelling, Measurument & Control, C, 1995. Vol. 48, ʋ 2. PP. 39-46. 344. Rossiev D.A., Golovenkin S.E., Shulman V.A., Matyushin G.V. The employment of neural networks to model implantation of pacemaker in patients with arrhythmias and heart blocks // Proceedings of International Conference on Neural Information Processing, Oct. 17-20, 1994, Seoul, Korea.V.1.- PP.537-542. 345. Rossiev D.A., Savchenko A.A., Borisov A.G., Kochenov D.A. The employment of neural-network classifier for diagnostics of different phases of immunodeficiency // Modelling, Measurement & Control.- 1994.- V.42.- N.2. P.55-63. 346. Rozenbojm J., Palladino E., Azevedo A.C. An expert clinical diagnosis system for the support of the primary consultation // Salud. Publica Mex.- 1993.- V.35, N.3.- P.321-325. 347. Rumelhart D.E., Hinton G.E., Williams R.J. Learning internal representations by error propagation. - Parallel Distributed Processing: Exploration in the Microstructure of Cognition, D.E.Rumelhart and J.L.McClelland (Eds.), vol. 1, Cambridge, MA: MIT Press, 1986. PP. 318 362. 348. Rummelhart D.E., Hinton G.E., Williams R.J. Learning representations by back-propagating errors // Nature, 1986. V. 323. P. 533-536. 349. Saaf L. A., Morris G. M. Filter synthesis using neural networks: [Pap.] Opt. Pattern Recogn. II: Proc. Meet., Paris, 26-27 Apr., 1989 // Proc. Soc. Photo-Opt. Instrum. Eng.- 1989.- 1134.- PP. 1216. 350. Sandberg I.W. Approximation for Nonlinear Functionals. - IEEE Transactions on Circuits and Systems - 1: Fundamental Theory and Applications, Jan. 1992. Vol.39, No 1. PP.65 67. 351. Savchenko A.A., Zakharova L.B., Rossiev D.A. The employment of neural networks for investigation & diagnostics of Viliuisk encephalomyelitis // Modelling, Measurement & Control, C.- 1995.- V.48, N.4.- P.1-15. 352. Senashova M.Yu., Gorban A.N. and. Wunsch D.C. II. Back-propagation of accuracy // ICNN97 (The 1997 IEEE International Conference on Neural Networks), Houston, IEEE, 1997. PP. 19982001. 353. Senna A.L., Junior W.M., Carvallo M.L.B., Siqueira A.M. Neural Networks in Biological Taxonomy // Proceedings of 1993 International Joint Conference on Neural Networks, Nagoya, Japan, October 25-29, 1993.- Nagoya, 1993.- V.1.- P.33-36. 354. Stefanuk V.L. Expert systems and its applications // The lectures of Union's workshop on the main problems of artificial intillegence and intellectual systems. Part 2, Minsk, 1990.- P.36-55.
CHAPLIT.DOC
187
355. Sussman H.J. Uniqueness of the weigts for minimal feedforward nets wits a given input - output map. Neural Networks, 1992, No. 5. PP. 589 - 593. 356. Sweeney J.W.P., Musavi M.T., Guidi J.N. Probabilistic Neural Network as Chromosome Classifier // Proceedings of 1993 International Joint Conference on Neural Networks, Nagoya, Japan, October 25-29, 1993.- Nagoya, 1993.-V.1.- P.935-938. 357. Tabatabai A., Troudet T. P. A neural net based architecture for the segmentation of mixed graylevel and binary pictures // IEEE Trans. Circuits and Syst.- 1991. 31 38, ʋ 1.- PP. 66-77. 358. Tao K.M., Morf M. A lattice filter type of neuron model for faster nonlinear processing // 23th Asilomar Conf. Signals, Syst. and Comput., Pasific Grove, Calif. Oct. 30-Nov. 1, 1989: Conf. Rec. Vol. 1.- San Jose (Calif.), 1989.- PP. 123-127. 359. The Adaptive Brain/ S. Grossberg (Ed.). North-Holland, 1987. V.1. Cognition, Learning, Reforcement, and Rhythm. 498 p. V.2. Vision, Speech, Language, and Motor Control. 514 p. 360. The Computer and the Brain. Perspectives of Human and Artificial Intelligence/Ed. by J.R. Brinc, C.R. Haden, C. Burava. North-Holland, 1989. 300 p. 361. Vakhrushev S.G., Rossiev D.A., Burenkov G.I., Toropova L.A. Neural network forecasting of optimal parameters of laserotherapy in patients after tonsillectomy // Proceedings of World Congress on Neural Networks - 1995 (WCNN'95).- P. 176-178. 362. Van Leeuwen J.L. Neural network simulations of the nervous system // Eur. J. Morphol.- 1990.V.28, N.2-4.- P.139-147. 363. Varela F.J., Coutinho A., Dupire B. et al. Cognitive networks: immune, neural and otherwise // Teoretical immunology. Ed. by Perelson A.- Addison Wesley, 1988.- Part 2.- P.359-375. 364. Waxman C. Neurocomputers in the human sciences: program: predictions of US presidential elections// Modelling, Measurement & Control, D, Vol.5, No.1, 1992, pp.41-53 365. Weckert J. How expert can expert systems really be? // Libr. and Expert Syst.: Proc. Conf. and Workshop [Centre Inf. Stud.], Riverina, July, 1990.- London, 1991.- PP. 99-114. 366. Wiedermann J. On the computation efficiency of symmetric neural networks // Theor. Comput. Sci.- 1991.- 80, ʋ 2.- PP. 337-345. 367. Wong K.Y.M., Kahn P.E., Sherrington D. A neural network model of working memory exhibiting primacy and recency // J. Phys. A.- 1991.- 24, ʋ 5.- PP. 1119-1133. 368. Yang T.-F., Devine B., Macfarlane P.W. Combination of artificial neural networks and deterministic logic in the electrocardiogram diagnosis of inferior myocardial infarction // Eur. Heart J.- 1994.- V.15.- Abstr. Supplement XII-th World Congress Cardiology (2408).- P.449.
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СОДЕРЖАНИЕ
Введение
3
1. Функциональные компоненты
5
2. Общий стандарт
9
3. Задачник и обучающее множество
39
4. Предобработчик
53
5.1. Конструирование нейронных сетей
73
5.2. Примеры сетей и алгоритмов их обучения
82
5.3. Стандарт первого уровня компонента сеть
88
6. Оценка и интерпретатор ответа
113
7. Исполнитель
136
8. Учитель
141
9. Контрастер
159
Список литературы
173
