Aiming at the disadvantage that the initial weights and thresholds of BP neural network are randomly selected, genetic algorithm is used to optimize the initial weights and thresholds of BP, and then BP training and testing are carried out. Genetic algorithm includes coding selection, crossover and mutation.

Filename	Size	Update
基于遗传算法优化BP神经网络的非线性预测	0	2019-01-13
基于遗传算法优化BP神经网络的非线性预测\BP.m	798	2019-01-13
基于遗传算法优化BP神经网络的非线性预测\Code.m	420	2009-08-16
基于遗传算法优化BP神经网络的非线性预测\Cross.m	1556	2009-08-31
基于遗传算法优化BP神经网络的非线性预测\Decode.m	1158	2009-08-31
基于遗传算法优化BP神经网络的非线性预测\Mutation.m	1602	2009-11-12
基于遗传算法优化BP神经网络的非线性预测\Select.m	825	2010-05-26
基于遗传算法优化BP神经网络的非线性预测\data.mat	46404	2009-09-18
基于遗传算法优化BP神经网络的非线性预测\fun.m	1049	2010-01-06
基于遗传算法优化BP神经网络的非线性预测\main_function.m	4331	2017-04-17
基于遗传算法优化BP神经网络的非线性预测\test.m	291	2009-08-31

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