sjwbp.asv

matlab实现BP神经网络

S1=xlsread('d:\毕业论文材料\data/Kyangben.xls');%困境样本
S2=xlsread('D:\毕业论文材料\data/KTest.xls');%困境测试
S3=xlsread('D:\毕业论文材料\data/Zyangben.xls');%正常样本
S4=xlsread('D:\毕业论文材料\data/ZTest.xls');%正常测试
%输入
P1=S1(1:429,1:27);%困境
P3=S3(1:4195,1:27);%正常
P=[P1',P3'];
%目标输出
A=ones(429,429);%困境
A=A(1,:);
B=zeros(4195,4195);%正常
B=B(1,:);
T=[A,B];
%设计模型
net=newff(minmax(P),[2 1],{'tansig','purelin'},'trainrp');
%训练
net.trainParam.show=50;
net.trainParam.lr=0.1;
net.trainParam.epochs=1000;
net.trainParam.goal=1e-5;
[net,tr]=train(net,P,T);
%仿真
P=S2(1:100,1:27);
Y=sim(net,P');
Y=round(Y);
sortY=sort(Y(:));                                                              
uniqueY=([diff(sortY);1]>0);                                                   
count = [sortY(uniqueY) diff(find([1;uniqueY]))]