x = [0 1 0; 0 1 0; 1 1 0; 1 1 1; 0 1 1; 1 1 1; 0 1 1; 1 1 0]; % three samples from input training data t = [0 0 1; 1 0 0 ; 0 1 0; 0 0 0;0 0 0;0 0 0]; %three samples from target training data [ni N]=size(x); % ni= no of input neurons [no N]=size(t); %no= no of output neurons nh=8; % no of hidden neurons in hidden layer wih = 0.01*randn(nh,ni+1); %weight matrix (iput to hidden layer) who = 0.01*randn(no,nh+1); %weight matrix (hidden to output layer) c = 0; while(c < 1000) c = c+1;
for i=1:N
for j = 1:nh
netj(j) = wih(j,1:end-1)*x(:,i)+wih(j,end);
outj(j) = tansig(netj(j));
end
for k = 1:no
netk(k) = who(k,1:end-1)*outj' + who(k,end);
outk(k) = 1./(1+exp(-netk(k)));
delk(k) = outk(k)*(1-outk(k))*(t(k,i)-outk(k));
end
%back propagation
for j = 1:nh
s=0;
for k = 1:no
s = s + who(k,j)*delk(k);
end
delj(j) = outj(j)*(1-outj(j))*s;
end
for k = 1:no
for l = 1:nh
who(k,l) = who(k,l)+.5*delk(k)*outj(l);
end
who(k,l+1) = who(k,l+1)+1*delk(k)*1;
end
for j = 1:nh
for ii = 1:ni
wih(j,ii) = wih(j,ii)+.5*delj(j)*x(ii,i);
end
wih(j,ii+1) = wih(j,ii+1)+1*delj(j)*1;
end
end
end
h = tansig(wih*[x;ones(1,N)]);
y = logsig(who*[h;ones(1,N)]); y=round(y); e = t-y; % new iput to the network csr=[0 1 0 0 0 0 1 0]; % current sensor reading
i need to add more training samples. right now my question is how to predict the corresponding output for csr
