close all
clear
clf reset
figure(gcf);
echo on
clc
%newff--生成一個(gè)新的bp前向神經(jīng)網(wǎng)絡(luò)
%train--訓(xùn)練
%sim－－仿真
pause%任意鍵開(kāi)始
clc
%定義訓(xùn)練樣本
%P為輸入矢量
P=[-1,-2,3,1;
   -1,1,5,-3];   ;
%T為目標(biāo)矢量
T=[-1,-1,1,1];
pause
clc
%創(chuàng)建一個(gè)新的bp前向神經(jīng)網(wǎng)絡(luò)
for j=1:1:4
    m=Z(j,1);
net=newff(minmax(P),[m,1],{'tansig','purelin'},'traingdm');
%
inputWeights=net.IW{1,1}
inputbias=net.b{1}

layerWeights=net.LW{2,1}
layerbias=net.b{2}

pause
clc
%
net.trainParam.show=50;
net.trainParam.lr=0.05;
net.trainParam.mc=0.9;
net.trainParam.epochs=1000;
net.trainParam.goal=1e-10;
pause
clc
%
tic
[net,tr]=train(net,P,T);
out=toc
t=out
pause
clc
%訓(xùn)練后網(wǎng)絡(luò)權(quán)值和偏差
inputWeights=net.IW{1,1}
inputbias=net.b{1}

layerWeights=net.LW{2,1}
layerbias=net.b{2}

pause
%
A=sim(net,P)
%計(jì)算仿真誤差

E=T-A
SSE=sse(E)
y=fitness(SSE,t)
end
pause
clc
echo off