SI - sieć neuronowa

clear all
nntwarn off
format compact
P = -1:.1:-.5;
T = [-.9602 -.5770 -.0729 .3771 .6405 .6600];
S1 = 25;
[R, Q]=size(P);
[S2, Q]=size(T);

[W1, B1]=nwtan(S1, R);
[W2, B2]=rands(S2, S1);
disp_freq=100;
max_epoch=20000;
err_goal=0.1;
lr=0.001;
error=[];

A1 =tansig(W1*P, B1);
A2=purelin(W2*A1,B2);
E= T-A2;
SSE=sumsqr(E);

for epoch=1:max_epoch
    if (SSE < err_goal)
        epoch=epoch-1;
        break;
    end
    D2=deltalin(A2,E);
    D1=deltatan(A1,D2,W2);
    [dW1,dB1]=learnbp(P,D1,lr);
    [dW2,dB2]=learnbp(A1,D2,lr);
    W1=W1+dW1;
    B1=B1+dB1;
    W2=W2+dW2;
    B2=B2+dB2;
A1=tansig(W1*P,B1);
A2=purelin(W2*A1,B2);
E= T-A2;
SSE=sumsqr(E);
error=[error SSE];
if(rem(epoch,disp_freq)==0)
epoch
SSE
plot(P,T,'r',P,A2,'g')
pause(1e-50)
end
end