MATLAB BPNN EXAMPLE BP神经网络实例

%BP neural network加动量项的算法 clear inputNums=5;%输入节点数 outputNums=5;%输出节点数 hideNums=15;%隐层节点数 maxcount=10000;%最大训练次数 samplenum=7;%总样本组数 precision=0.001;%精度 alpha=0.5;%学习率设定值 a=0.5;%动量系数设定值 count=1; error=zeros(1,count); errorp=zeros(1,samplenum); v=rand(inputNums,hideNums); deltv=zeros(inputNums,hideNums); dv=zeros(inputNums,hideNums); st1=zeros(1,hideNums); w=rand(hideNums,outputNums); deltw=zeros(hideNums,outputNums); dw=zeros(hideNums,outputNums); st2=zeros(1,outputNums); %samplelist=[1,0,0;0,1,0;0,0,1]; %expectlist=[1,0,0;0,1,0;0,0,1]; samplelist=[0,0.8823,0,1,0; 0.9910,0.0176,0.9230,0,0.8174; 1,1,0.9030,1,0.9588; 0.0020,0.8823,0.0437,1,0.1582; 1,0.8823,0.3569,1,0.6622; 0.9535,0.0963,0.009,0.5386,0.0593; 1,0.9983,0,1,0]; expectlist=[1,0,0,0,0,0,0,0,0; 0,1,0,0,0,0,0,0,0; 0,0,1,0,0,0,0,0,0; 0,0,0,1,0,0,0,0,0; 0,0,0,0,1,0,0,0,0; 0,0,0,0,0,1,0,0,0; 0,0,0,0,0,0,1,0,0]; while (count<=maxcount) c=1; while (c<=samplenum) for k=1:outputNums d(k)=expectlist(c,k);%获得期望输出的向量 end for i=1:inputNums x(i)=samplelist(c,i);%获得输入的向量（数据） end %Forward(); for j=1:hideNums net=0.0; for i=1:inputNums net=net+x(i)*v(i,j); end net=net-st1(j); y(j)=1/(1+exp(-net)); end for k=1:outputNums net=0.0; for j=1:hideNums net=net+y(j)*w(j,k); end net=net-st2(k); o(k)=1/(1+exp(-net)); end %BpError(c); errortmp=0.0; for k=1:outputNums errortmp=errortmp+(d(k)-o(k))^2; end errorp(c)=0.5*errortmp; %end %Backward(); for k=1:outputNums yitao(k)=(d(k)-o(k))*o(k)*(1-o(k)); end for j=1:hideNums tem=0.0; for k=1:outputNums tem=tem+yitao(k)*w(j,k); end yitay(j)=tem*y(j)*(1-y(j)); end %调整各层权值 for j=1:hideNums for k=1:outputNums deltw(j,k)=alpha*yitao(k)*y(j); w(j,k)=w(j,k)+deltw(j,k)+a*dw(j,k); dw(j,k)=deltw(j,k); end end for k=1:outputNums st2(k)=st2(k)+alpha*yitao(k); end for i=1:inputNums for j=1:hideNums deltv(i,j)=alpha*yitay(j)*x(i); v(i,j)=v(i,j)+deltv(i,j)+a*dv(i,j); dv(i,j)=deltv(i,j); end end for j=1:hideNums st1(j)=st1(j)+alpha*yitay(j); end %end c=c+1; end double tmp; tmp=0.0; for i=1:samplenum tmp=tmp+errorp(i)*errorp(i); end tmp=tmp/c; error(count)=sqrt(tmp); if (error(count)<precision) break; end count=count+1;%训练次数加1 end p=1:count-1; plot(p,error(p),'-');