GENETIC ALGORITHMS AND ARTIFICIAL NEURAL NETWORK(Ⅰ)──TRAINING ARTIFICIAL NEURAL NETWORK WITH EXTENDED GENETIC ALGORITHMS

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- GENETIC ALGORITHMS AND ARTIFICIAL NEURAL NETWORK(Ⅰ)──TRAINING ARTIFICIAL NEURAL NETWORK WITH EXTENDED GENETIC ALGORITHMS
- CIESC Journal Issue 3, (1996)
- 作者机构：
  
  清华大学化学工程系!北京100084,清华大学化学工程系!北京100084,清华大学化学工程系,北京,100084
- 作者简介：
- 基金信息：
- DOI：
  CLC： 224
- Online First：25 June 1996，
  
  Published：25 June 1996
- 稿件说明：
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Chen Fangze, Chen Bingzhen, He Xiaorong(Department of Chemical Engineering. GENETIC ALGORITHMS AND ARTIFICIAL NEURAL NETWORK(Ⅰ)──TRAINING ARTIFICIAL NEURAL NETWORK WITH EXTENDED GENETIC ALGORITHMS[J]. CIESC Journal, 1996, (3).
DOI：

Chen Fangze, Chen Bingzhen, He Xiaorong(Department of Chemical Engineering. GENETIC ALGORITHMS AND ARTIFICIAL NEURAL NETWORK(Ⅰ)──TRAINING ARTIFICIAL NEURAL NETWORK WITH EXTENDED GENETIC ALGORITHMS[J]. CIESC Journal, 1996, (3). DOI：

针对BP（backpropagation）神经网络训练中的局部最优问题，提出了由改进的遗传算法EGA训练BP神经网络的新方法．该方法克服了经典遗传算法的不足，通过自适应多点变异操作比较有效地克服了收敛过程中的振荡问题，因而提高了BP网络训练的速度，并且找到了合理的变异因子范围．

In order to overcome a local optimal solution which often occurs in the training process of artificial neural networks

a new algorithm called EGA is proposed.An adaptive multipoint mutation operator

which can effectively extinguish the swing problem in convergent process

is proposed in this algorithm.So the speed of BP training is improved.A reasonable mutation factor range is found.As a result

the speed and accuracy of training artificial neural networks are much improved.

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