MATLAB: After training (nftool) a Neural network using the neural network toolbox, how to test the trained network with new input data that doesn’t have any target values

neural networktesting neural network

I have created a trained network for gas turbine diagnostics using implanted degradation. Now i want to give more inputs inorder to predict the degradation. Im new to this, can anyone please help me.

Best Answer

output = sim(net,input); % = net(input)

If you have no target, the only way to test it is to plot the output and look at it.

Hope this helps.

Thank you for formally accepting my answer

Greg

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MATLAB: How to train a feedfordward neural network with error weights

 Use the command window
 help mse
 doc mse

Hope this helps.

Thank you for formally accepting my answer

Greg

MATLAB: Neural Network tool nftool

Not enough information.

 1. Your spelling of outputs is inconsistent
 2. [ I N ] = size(inputs) =?   
 3  [ O N ] = size(outputs) = ?
 4. MSE00 = mean(var(outputs'),1) = ?   % Reference MSE 
 5. Ntrn = N - 2*(0.15*N)   % Default No. of training examples 
 6. Ntrneq = Ntrn*O         % No. of training equations
 7. H = 10 % default No. of hidden nodes
 8. Nw = (I+1)*H+(H+1)*O % No. of unknown weights to be estimated from Ntrneq
 9. In order to avoid a poor set of initial random weights, obtain Ntrials = 10 
separate designs by looping over i = 1:Ntrials
 10. Pick the best design with the lowest normalized VALIDATION MSE
     NMSEval = tr.best_vperf/MSE00   % Desire 0 < NMSEval << 1

Hope this helps.

Thank you for formally accepting my answer

Greg

P.S. Search on greg i=1:Ntrials

Best Answer

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MATLAB: How to train a feedfordward neural network with error weights

MATLAB: Neural Network tool nftool

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