bp神经网络源程序.txt

bp神经网络源程序前向型神经网络

                  

                  clear
                  c=load('d:\c.txt');
                  a=load('d:\a.txt');
                  c=c';
                  a=a';
                  n=0;
                  dsum=1;
                  v=abs(randn(3,6)); %输入层至隐含层连接权
                  z1=randn(3,1);%输入层阈值
                  z=zeros(3,5);
                  z(:,1)=z1;
                  z(:,2)=z1;
                  z(:,3)=z1;
                  z(:,4)=z1;
                  z(:,5)=z1;
                  s=zeros(3,5);
                  b=zeros(3,5);
                  %隐含层至输出层
                  w=abs(randn(2,3)); %隐含层至输出层连接权
                  zy1=randn(2,1);%隐含层阈值
                  zy(:,1)=zy1;
                  zy(:,2)=zy1;
                  zy(:,3)=zy1;
                  zy(:,4)=zy1;
                  zy(:,5)=zy1;
                  l=zeros(2,5);
                  cc=zeros(2,5);
                  con=[1 1 1 1 1; 1 1 1 1 1];
                  d=zeros(2,5);
                  con2=[1 1 1 1 1;1 1 1 1 1;1 1 1 1 1];
                  SL1=randn(1,1);
                  SL2=randn(1,1);
                  while dsum>0.00001
                  s=v*a+z;
                  b=1./(1+exp(-s) );
                  %隐含层至输出层
                  l=w*b+zy;
                  cc=1./(1+exp(-l));
                  %计算误差
                  j=c-cc; 
                  d=j.*c.*(con-c);
                  e=w'*d.*b.*(con2-b);
                  SL1=abs(randn(1,1));
                  SL2=abs(randn(1,1));
                  w=w+SL1*d*b';
                  zy=zy+SL1*d;
                  v=v+SL2*e*a';
                  z=z+SL2*e;
                  dsum=0;
                  for i=1:2
                  for j=1:5
                  dsum=dsum+abs(d(i,j));
                  end
                  end
                  n=n+1;
                  error=dsum;
                  end
                  n1=1:n;
                  plot(n1,error)