bp.m

这是一个基于matlab的BP算法程序

function mian()
SamNum=400;
TestSamNum=201;
HiddenUnitNum=12;
InDim=1;
OutDim=1;

rand('state',sum(100*clock))   
NoiseVar=0.05;
Noise=NoiseVar*randn(1,SamNum);
SamIn=2*rand(1,SamNum)-1;
SamOutNoNoise=cos(2*pi*SamIn);
SamOut=SamOutNoNoise+Noise;

TestSamIn=-1:0.01:1;
TestSamOut=cos(2*pi*TestSamIn);

figure
hold on
grid
plot(SamIn,SamOut,'k+')
plot(TestSamIn,TestSamOut,'b--')
xlabel('Input x');
ylabel('Output y');

MaxEpochs=50000;
lr=0.002;
E0=1.0;

W1=0.2*rand(HiddenUnitNum,InDim)-0.1
B1=0.2*rand(HiddenUnitNum,1)-0.1
W2=0.2*rand(OutDim,HiddenUnitNum)-0.1
B2=0.2*rand(OutDim,1)-0.1

W1Ex=[W1 B1];
W2Ex=[W2 B2];

SamInEx = [SamIn' ones(SamNum,1)]';
ErrHistory = []; 

for i=1:MaxEpochs
   
   HiddenOut=logsig(W1Ex*SamInEx);
   HiddenOutEx=[HiddenOut' ones(SamNum,1)]';
   NetworkOut=W2Ex*HiddenOutEx;
   
   
   Error=SamOut-NetworkOut;
   SSE=sumsqr(Error);

   ErrHistory=[ErrHistory SSE];
   
   if SSE<E0, break, end
   
   
   Delta2=Error;
   Delta1=W2'*Delta2.*HiddenOut.*(1-HiddenOut);
   

   dW2Ex=Delta2*HiddenOutEx';
   dW1Ex=Delta1*SamInEx';
   

   W1Ex=W1Ex+lr*dW1Ex;
   W2Ex=W2Ex+lr*dW2Ex;
   

   W2 = W2Ex(:,1:HiddenUnitNum);
end

i
W1=W1Ex(:,1:InDim)
B1=W1Ex(:,InDim+1)
W2
B2=W2Ex(:,1+HiddenUnitNum)

TestHiddenOut=logsig(W1*TestSamIn+repmat(B1,1,TestSamNum));
TestNNOut=W2*TestHiddenOut+repmat(B2,1,TestSamNum);
plot(TestSamIn,TestNNOut,'r-')

TSE=sumsqr(TestSamOut-TestNNOut);

figure
hold on
grid
[xx,Num]=size(ErrHistory);
plot(1:Num,ErrHistory,'k-');
TSE
SSE