nnet.m

神经网络在matlab上的实现的源代码

%神经网络
clc;
clear;
load yangben_data;
load daipan_data;
zhibiao=[yangben(:,3:32);daipan(:,3:end)];
[m,n]=size(zhibiao);

%数据的预处理 
for j=1:n
    for i=1:m
     zhibiao1(i,j)=(zhibiao(i,j)-min(zhibiao(:,j)))/(max(zhibiao(:,j))-min(zhibiao(:,j)));
    end
end
[ptrans,transMat]=prepca(zhibiao1',0.001);%主元分析
p=ptrans(:,1:450);%输入数据
T=yangben(1:450,2)';%监督学习
Pr=minmax(p);
net=newff(Pr,[20,1],{'tansig','purelin'},'trainlm');
net.trainParam.epochs=1000;
net.trainParam.goal=0.00000001;
net.trainParam.show=1;
net.trainParam.lr=0.05;
net =train(net,p,T);
an=sim(net,ptrans(:,451:end));
figure(2)
plot(1:50,an(1:50),'ro-',1:50,yangben(451:end,2),'b*-');
title('50组试验数据判定结果与实际结果对比图(神经网络)');
xlabel('n(样本号)');
ylabel('R(0----B,1----M)');
legend('50组试验数据判定结果','实际结果')
[m,n]=size(zhibiao(451:end,2));
for i=1:m
    if(an(i)>0.5)
       an(i)=1;
   else
       an(i)=0;
   end
end
figure(3)
plot(1:50,an(1:50),'ro-',1:50,yangben(451:end,2),'b*-');
title('50组试验数据判定结果与实际结果对比图(神经网络)');
xlabel('n(样本号)');
ylabel('R(0----B,1----M)');
legend('50组试验数据判定结果','实际结果')