bp_1.m

根据神经网络的相关原理

P=-1:0.1:1;
T=[-0.9602 -0.577 -0.0729 0.3771 0.6405 0.66 0.4609 0.1336 -0.2013 -0.4344 -0.5 ...
    -0.393 -0.1647 0.0988 0.3072 0.396 0.3449 0.1816 -0.0312 -0.2189 -0.3201];
    net=newff(minmax(P),[8,1],{'tansig','logsig'});
    net.trainParam.epochs=2000;
    net.trainParam.goal=0.001;
    net=train(net,P,T);
    y=sim(net,P);
    error=y-T;
    res(i)=norm(error)