计算机测量与控制
計算機測量與控製
계산궤측량여공제
COMPUTER MEASUREMENT & CONTROL
2010年
2期
407-410
,共4页
双链量子遗传算法%过程神经元网络%学习算法
雙鏈量子遺傳算法%過程神經元網絡%學習算法
쌍련양자유전산법%과정신경원망락%학습산법
quantum genetic algorithm with double chains%process neural networks%learning algorithm
基于函数正交基展开的过程神经元网络训练,由于参数较多BP算法不易收敛.针对这一问题,本文提出了一种基于双链量子遗传算法的解决方案.首先按权值参数的个数确定染色体上的基因数.完成种群编码,然后通过染色体评估获得当前最优染色体,以该染色体为目标,用量子旋转门完成种群中个体的更新,用量子非门实现个体变异增加种群多样性.在该方法中,每条染色体携带两条基因链,因此町扩展对解空间的遍历性,加速优化进程.以两组二维三角函数的模式分类问题为例,仿真结果表明该方法不仅收敛速度快,而且寻优能力强.
基于函數正交基展開的過程神經元網絡訓練,由于參數較多BP算法不易收斂.針對這一問題,本文提齣瞭一種基于雙鏈量子遺傳算法的解決方案.首先按權值參數的箇數確定染色體上的基因數.完成種群編碼,然後通過染色體評估穫得噹前最優染色體,以該染色體為目標,用量子鏇轉門完成種群中箇體的更新,用量子非門實現箇體變異增加種群多樣性.在該方法中,每條染色體攜帶兩條基因鏈,因此町擴展對解空間的遍歷性,加速優化進程.以兩組二維三角函數的模式分類問題為例,倣真結果錶明該方法不僅收斂速度快,而且尋優能力彊.
기우함수정교기전개적과정신경원망락훈련,유우삼수교다BP산법불역수렴.침대저일문제,본문제출료일충기우쌍련양자유전산법적해결방안.수선안권치삼수적개수학정염색체상적기인수.완성충군편마,연후통과염색체평고획득당전최우염색체,이해염색체위목표,용양자선전문완성충군중개체적경신,용양자비문실현개체변이증가충군다양성.재해방법중,매조염색체휴대량조기인련,인차정확전대해공간적편력성,가속우화진정.이량조이유삼각함수적모식분류문제위례,방진결과표명해방법불부수렴속도쾌,이차심우능력강.
For training of process neural networks based on the orthogonal basis expansion,it is difficult to converge for BP algorithm as more parameters.Aiming at the issue,this paper proposes a solution based on quantum genetic algorithm with double chains.Firstly'the number of genes is determined by the number of weight parameters,quantum chromosomes are constructed by qubits,and the current opti-mal chromosome is obtained with the help of colony assessment.Secondly,taking each qubit in this optimal chromosome as the goal,individ-uals are updated by quantum rotation gate,and mutated by quantum non-gate to increase the diversity of population.In this method, each chromosome carrying two chalns of genes,therefore it can extend ergodicity for solution space and accelerate optimization process.Taking the pattern classification of two groups of two-dimensional trigonometric functions as an example,the simulation results show that the meth-od not only has fast convergence,but also good optimization ability.
