%獲得網絡的輸入樣本P和目標樣本T
P=[1 0.6 0.1 0 0 0 0   1 0.5 0 0 0 0 0;
1 0.6 0.1 0 0 0 0   0 0.5 1 0.4 0 0 0;
1 0.6 0.1 0 0 0 0   0 0 0 0.4 1 0.6 0;
1 0.6 0.1 0 0 0 0   0 0 0 0 0 0 0.8;
0 0.6 0.6 0 0 0 0   1 0.5 0 0 0 0 0;
0 0.6 0.6 0 0 0 0   0 0.5 1 0.4 0 0 0;
0 0.6 0.6 0 0 0 0   0 0 0 0.4 1 0.6 0;
0 0.6 0.6 0 0 0 0   0 0 0 0 0 0 0.8;
0 0 0.6 1 0 0 0    1 0.5 0 0 0 0 0;
0 0 0.6 1 0 0 0    0 0.5 1 0.4 0 0 0;
0 0 0.6 1 0 0 0    0 0 0 0.4 1 0.6 0;
0 0 0.6 1 0 0 0    0 0 0 0 0 0 0.8;
0 0 0 0 1 0.6 0    1 0.5 0 0 0 0 0;
0 0 0 0 1 0.6 0    0 0.5 1 0.4 0 0 0;
0 0 0 0 1 0.6 0    0 0 0 0.4 1 0.6 0;
0 0 0 0 1 0.6 0    0 0 0 0 0 0 0.8]';
T=[1 0.5 0 0 0 0 0 0;
0 0 0 0.2 0.6 1 0.6 0.2;
0 0 0 0.2 0.6 1 0.6 0.2;
0 0 0 0.2 0.6 1 0.6 0.2;
0.4 0.8 1 0.8 0.4 0.2 0 0;
0 0 0 0.2 0.6 1 0.6 0.2;
0 0 0 0 0 0.2 0.5 0.8;
0 0 0 0.2 0.6 1 0.6 0.2;
0 0 0 0.2 0.6 1 0.6 0.2;
0 0 0 0.2 0.6 1 0.6 0.2;
0 0 0 0 0 0.2 0.5 0.8;
0 0 0 0 0 0.2 0.5 0.8;
0 0 0 0.2 0.6 1 0.6 0.2;
0 0 0 0.2 0.6 1 0.6 0.2;
0 0 0 0 0 0.2 0.5 0.8;
0 0 0 0 0 0.2 0.5 0.8]';
%根據Kolmogorov定理，輸入層有14個節點，所以中間層有29個節點
%中間層神經元的傳遞函數為'tansig'
%輸出層有8個節點，其神經元傳遞函數為logsig
%訓練函數采用traingdx
net=newff(minmax(P),[29,8],{'tansig','logsig'},'traingdx');
%訓練步數為1000次
%訓練目標誤差為0.001
net.trainParam.epochs=1000;
net.trainParam.goal=0.001;
net=train(net,P,T);
Y=sim(net,P);
%求訓練值在每一個點上的誤差
for i=1:16
    x(i)=norm(Y(:,i));
end
plot(1:16,x);