How to Loop the neural network training to choose the best performance?

Hi, I need some help on how to train a network for different value of Neurons, and save the MSE then choose the best MSE to select the best trained network.

I am using the fitnet as follows:

trainFcn = 'trainlm';  % Levenberg-Marquardt backpropagation.

% Create a Fitting Network & set number of neurons
hiddenLayerSize = 30; 
net = fitnet(hiddenLayerSize,trainFcn);

[net,tr] = train(net,X,T);

testX = X(:,tr.testInd);
testT = T(:,tr.testInd);
testY = net(testX);
perf = mse(net,testT,testY)

Answer 1

[KSSV]

Here it is: https://in.mathworks.com/help/nnet/ug/improve-neural-network-generalization-and-avoid-overfitting.html

[Chin Kui Ku]

Thank you, how about if want to loop for different number of neurons for eg, 5, 10, 15, 20, 25, 30, 35, 40. ?

[KSSV]

      N = [ 5, 10, 15, 20, 25, 30, 35, 40. ];
     NN = cell(length(N),1) ;
     P = zeros(length(N),1) ;
      for i = 1:length(N)
      trainFcn = 'trainlm';  % Levenberg-Marquardt backpropagation.
      % Create a Fitting Network & set number of neurons
      hiddenLayerSize = N(i); 
      net = fitnet(hiddenLayerSize,trainFcn);
      [net,tr] = train(net,X,T);
      NN{i} = net ;
      testX = X(:,tr.testInd);
      testT = T(:,tr.testInd);
      testY = net(testX);
      perf = mse(net,testT,testY) ;
      P(i) = perf ;
    end

[Chin Kui Ku]

Thank you very much!