p=[46.21 11.27 33.04 8.52 0.614;
41.12 32.51 14.45 8.36 0.52;
33.36 29.32 32.89 27.28 2.25;
46.13 11.57 33.14 8.52 0.63;
40.26 25.11 18.61 16.02 0;
14.92 21.98 17.15 46.12 0;
0.872 43.18 27.14 26.88 0;
35.35 51.17 8.01 5.47 0;
0.98 43.83 26.8 28.39 0;
12.15 40.5 18.9 28.35 0.09;
89.99 5.35 1.96 2.7 0;
37.98 30.85 7.57 23.01 0;
10.99 21.29 11.3 52.98 2.38;
0.958 16.01 2.89 58.01 1.16;
11.03 22.51 3.31 57.96 1.13;
43.92 24.41 6.62 23.91 0.531;
14.71 12.56 12.44 60.29 0;
1.38 6.15 9.21 76.63 6.63;
11.31 21.83 11.24 53.14 2.46;
33.66 2.97 33.17 27.72 2.48;
85.86 6.98 4.52 2.56 0;
58.03 18.56 4.58 8.62 9.78;
83.68 7.95 5.15 3.02 0.56;
20.23 16.96 1.68 24.52 35.74;
26.76 16.56 2.98 38.76 13.61;
48.02 10.27 4.52 22.36 23.62;
17.01 13.76 3.07 39.57 26.59;
39.18 24.5 18.37 11.43 6.53;];
pnew=[11.18 41.92 21.41 15.51 10.25;
7.01 26.66 3.28 48.76 14.28]';
p=p';
t=[1 0 0 0 0;
1 0 0 0 0;
1 0 0 0 0
1 0 0 0 0;
1 0 0 0 0;
0 1 0 0 0;
0 1 0 0 0;
0 1 0 0 0;
0 1 0 0 0;
0 1 0 0 0;
0 1 0 0 0;
0 0 1 0 0;
0 0 1 0 0;
0 0 1 0 0;
0 0 1 0 0;
0 0 1 0 0;
0 0 1 0 0;
0 0 1 0 0;
0 0 1 0 0;
0 0 0 1 0;
0 0 0 1 0;
0 0 0 0 1;
0 0 0 0 1;
0 0 0 0 1;
0 0 0 0 1;
0 0 0 0 1;
0 0 0 0 1;
0 0 0 0 1;];
tnew=[0 0 0 0 1;
0 0 0 0 1]';
t=t';
[pn,min,max]=premnmx(p);
net=newff(minmax(pn),[40,5],{'tansig','purelin'},'trainlm');
net.trainParam.goal=0.00001;
net.trainParam.epochs=1000;
[net,tr]=train(net,pn,t);
%plot(tr.epoch,tr.perf,'-')
A=sim(net,pn);
A=compet(A)
% ac=zeros(size(A));
% ac(find(compet(A)))=1;
% ac
%新数据
[pr]=tramnmx(pnew,min,max)
%验证新数据输出
a=sim(net,pr);
NewResult=compet(a)
% ac=zeros(size(a));
% ac(find(compet(a)))=1;
% ac