基于遗传算法优化BP神经网络的非线性预测

说明：  针对BP神经网络的初始权值和阈值是随机选取的弊端，采用遗传算法寻优BP的初始权值和阈值，然后进行BP训练和测试。遗传算法包括编码 选择 交叉 和变异等操作(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.)

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