bp.m

这是一个BP神经网络的基本程序

t=0:1:79
P=[0.6154     0.7919    0.9218    0.7382    0.1763    0.4057    0.9355    0.9169    0.4103    0.8936    0.0579    0.3529    0.8132    0.0099    0.1389    0.2028    0.1987    0.6038    0.2722    0.1988    0.0153    0.7468    0.4451    0.9318    0.4660    0.4186    0.8462    0.5252    0.2026    0.6721    0.8381    0.0196    0.6813    0.3795    0.8318    0.5028    0.7095    0.4289    0.3046    0.1897    0.1934    0.6822    0.3028    0.5417    0.1509    0.6979    0.3784    0.8600    0.8537    0.5936    0.4966    0.8998    0.8216    0.6449    0.8180    0.6602    0.3420    0.2897    0.3412    0.5341    0.7271    0.3093    0.8385    0.5681    0.3704    0.7027    0.5466    0.4449    0.6946    0.6213    0.7948    0.9568    0.5226    0.8801    0.1730    0.9797    0.2714    0.2523    0.8757    0.7373;    0.1365    0.0118    0.8939    0.1991    0.2987   0.6614    0.2844    0.4692    0.0648    0.9883    0.5828    0.4235    0.5155    0.3340    0.4329    0.2259    0.5798    0.7604    0.5298    0.6405    0.2091    0.3798    0.7833    0.6808    0.4611    0.5678    0.7942    0.0592    0.6029    0.0503    0.4154    0.3050    0.8744    0.0150    0.7680    0.9708    0.9901    0.7889    0.4387    0.4983    0.2140    0.6435    0.3200    0.9601    0.7266    0.4120    0.7446    0.2679    0.4399    0.9334    0.6833    0.2126    0.8392    0.6288    0.1338    0.2071    0.6072    0.6299    0.3705    0.5751    0.4514    0.0439    0.0272    0.3127    0.0129    0.3840    0.6831    0.0928    0.0353    0.6124    0.6085    0.0158    0.0164    0.1901    0.5869    0.0576    0.3676    0.6315    0.7176    0.6927;    0.0841    0.4544    0.4418    0.3533    0.1536    0.6756    0.6992    0.7275    0.4784    0.5548    0.1210    0.4508    0.7159    0.8928    0.2731    0.2548    0.8656    0.2324    0.8049    0.9084    0.2319    0.2393    0.0498    0.0784    0.6408    0.1909    0.8439    0.1739    0.1708    0.9943    0.4398    0.3400    0.3142    0.3651    0.3932    0.5915    0.1197    0.0381    0.4586    0.8699    0.9342    0.2644    0.1603    0.8729    0.2379    0.6458    0.9669    0.6649    0.8704    0.0099    0.1370    0.8188    0.4302    0.8903    0.7349    0.6873    0.3461    0.1660    0.1556    0.1911    0.4225    0.8560    0.4902    0.8159    0.4608    0.4574    0.4507    0.4122    0.9016    0.0056    0.2974    0.0492    0.6932    0.6501    0.9830    0.5527    0.4001    0.1988    0.6252    0.7334];
T=[1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12 1.12];
%p=-1:0.1:1
%T=[-0.9602 -0.5770 -0.729 0.3771 0.6405 0.6600 0.4609 0.1336 -0.2013 -0.4344 -0.5000 -0.3930 -0.1647 0.0988 0.3072 0.3960 0.3449 0.1816 -0.0312 -0.2189 -0.3201]; 
%t=-1:0.05:1;
%p=[-0.001:0.00005:0.001];
%T=[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ]
net=newff(minmax(P),[25,1],{'tansig','purelin'},'trainlm');
net.trainParam.show=50;
net.trainParam.epochs=200;
net.trainParam.goal=0.001;
net=train(net,P,T);
y=sim(net,P);
 plot(t,T,'r');
    hold on
plot(t,y,'.');