neural network - Compute the mean square error at the end of each iteration and plot the same in matlab

Question

i have a dataset to classify, using perceptron learning rule . i've calculated the weight matrix but don't know how to plot MSE .{??1 = [ 1 1 ],??1 = [ 0 0 ]}, {??2 = [ 1 2 ],??2 = [ 0 0 ]}, {??3 = [ 2 ?1 ],??3 = [ 0 1 ]}, {??4 = [ 2 0 ],??4 = [ 0 1 ]}, {??5 = [ ?1 2 ],??5 = [ 1 0 ]}, {??6 = [ ?2 1 ],??6 = [ 1 0 ]}, {??7 = [ ?1 ?1 ],??7 = [ 1 1 ]}, {??8 = [ ?2 ?2 ],??8 = [ 1 1 ]}. This the dataset and w=[-2 0;0 -2],bias =[-1 0];

question from:https://stackoverflow.com/questions/65645908/compute-the-mean-square-error-at-the-end-of-each-iteration-and-plot-the-same-in

Answer 1

calculate weight change corresponding to a neuron , and apply the changed weights to all other inputs and find the mse.

 P=[p1,p2,...]
for i=1:number of inputs

    ///change weights here

   for j=1:number of inputs
       a=f(net);
      error[j] =target[j] -a[j];

     
   end
   mse=error *error' / numel(error);
   

end