MATLAB: Predicting microstructural properties using Neural Network: Backpropagation

MATLABneural network

I want to predict 3 microstructural properties by training neural network (backpropagation) with cooling rate of a alloy solidifying from a liquid as input and my target values as those 3 properties. I have 50 samples of cooling rate and corresponding 50 X 3 values of properties.
1) How should i set my neural network so that i could train it and reproduce the target results in the output ? 2) Also, i should be able to predict the value at any given cooling rate, obviously within a certain range it would be?

Best Answer

1. The scaling of the target data is unbalanced
 meant  = mean(t')'        % [ 36.5 1.08 -4.38 ]'
 vart      = var(t')'            % [  2.67    0.0012   44.4 ]'
Unbalanced scaling will cause NN training to neglect t(1:2,:)
To mitigate the unbalancing, either scale the variables or weight the targets.
Compare the MSE of the separate outputs to those of 3 separate networks.
Without scaling or weighting I get
 rng(0)
 for i = 1:20   
     net = train(fitnet,x,t);
     MSE(i) = perform(net,t,net(x))
 end
  minMSE    = min(MSE)          % 5.26
  medMSE   = median(MSE)   % 14.2
  meanMSE = mean(MSE)      % 14.3
  stdMSE     = std(MSE)           %  4.98
  maxMSE   = max(MSE)         %22.99
2. The input is not very predictive using a three output net without scaling or weighting.
Hope this helps.
Greg
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