XOR using backpropagation in matlab (II)

%XOR using backpropagation (no bias version)
% training set
ts=[0 0;
    1 1;
    1 0;
    0 1];
d=[0 0 1 1];
% weights for hidden layers (each row represents wiegths for the corrosponding neuron
wh=[rand() rand();
    rand() rand()];
% wiegths from hidden layer to output layer
wo=[rand() rand()];
% output for neuron 1 and 2 and bias for neuron3
a=[1 1];
% output for neuron 3
a3=0;
% learning rate
n=0.1;
iteration=10;
i=0;
while (i<iteration || e~=0)
    for tindex=1:4
          for neuron=1:2
              a(neuron)= logsig(wh(neuron,:)*ts(neuron,:)');
          end
          a3 = hardlim(wo(1)*a(1)+wo(2)*a(2))
          e = d(tindex) - a3
          grad_out = a3*e;
          deltaW = n*grad_out*a;
          wo_new = wo+deltaW;
          grad_h = a(1)*(1-a(1)) * grad_out*wo(1)'
          deltaW = n*grad_h*wh(1,:);
          wh(1,:)= wh(1,:) +deltaW
          grad_h = a(2)*(1-a(2)) * grad_out*wo(2)'

          wo = wo_new;
          deltaW = n*grad_h*wh(2,:)
          wh(2,:) = wh(2,:) +deltaW

      end
      i=i+1;
  end