First, the direct training of BP network with GA the weight algorithm Main program: gafault.m It includes the following routines: 1. BP network initialization: nninit.m-- given P, T, R, S1, S2 2. Fitness calculation functions: gabpEval.m 3. Of genetic algorithms for the BP network codec corresponding weights, the threshold function: gadecod.m Second, with the GA first aim at the weight of BP network, and then direct the training of pure BP mixture of BP algorithm GA-BP Main program: gabpfault.m It includes the following routines: 1. Network initialization: nninit.m-- given P, T, R, S1, S2 2. Fitness calculation functions: gabpEval.m 3. Of genetic algorithms for the BP network codec corresponding weights, the threshold function: gadecod.m 3, pure BP Main program: (1) bpfault.m on the MATLAB5.2 (2) bpfault.m the MATLAB6.5 for the subsequently added

GA训练BP网络的权重算法
......................\bpfault.m
......................\bppfault.m
......................\nninit.m
......................\readme.doc
......................\用GA先求BP网络的权重，再用纯BP直接训练BP的混合GA－BP算法
......................\........................................................\gabpEval.m
......................\........................................................\gabpfault.m
......................\........................................................\gadecod.m
......................\........................................................\nninit.m
......................\用GA直接训练BP网络的权重算法
......................\............................\gabpEval.m
......................\............................\gadecod.m
......................\............................\gafault.m
......................\............................\nninit.m
......................\遗传算法和神经网络在导弹测试设备故障诊断中的应用研究.doc

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