XOR using backpropagation in matlab (III)

%XOR calculator ( biases as weights )
% training set
% (the last number is bias coefficent which is always 1
ts=[0 0 1;1 1 1;
    1 0 1;0 1 1];
% targets or desired outputs
d=[0 0 1 1];
% weights for hidden layers
% bias ,weight1, weigth2
wh=[rand() rand() rand() ;
    rand() rand() rand()];
% wiegths from hidden layer to output layer
% bias, weight1, weigth2
wo=[rand() 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=100;
i=0;
while (i<iteration || e~=0)
    for tindex=1:4
          for neuron=1:2
              a(neuron)= logsig(wh(neuron,:)*ts(neuron,:)');
          end
          output_neuronsInput = wo(1)*a(1)+wo(2)*a(2)+wo(3)*1;
          a3 = hardlim(output_neuronsInput);
          e = d(tindex) - a3
          % neuron 3(output)
          localgrad_out = a3*e;
          deltaW = n * localgrad_out * output_neuronsInput;
          wo_new = wo + deltaW;
          % neuron 1
          grad_h = a(1)*(1-a(1)) * localgrad_out * wo(1)'
          deltaW = n * grad_h * wh(1,:);
          wh(1,:) = wh(1,:) + deltaW
          % neuron 2
          grad_h = a(2)*(1-a(2)) * localgrad_out*wo(2)'
          wo = wo_new;
          deltaW = n * grad_h * wh(2,:)
          wh(2,:) = wh(2,:) + deltaW
      end
      i=i+1;
  end