MATLAB: Training a neural network

Deep Learning Toolboxneural networktraining

Hi,
I am trying to develop a neural network which predicts an output based on 4 inputs, one of which is the output of the previous step. Currently I am just using a standard function fitting network (not a time-series prediction).
The neural network works really well (r squared approx. 0.98 – 0.99) when the output of the previous step is given independent of the neural network result.
However, when I use the neural network predicted output as the input to the next prediction, the neural network result is virtually worthless. Also, the results differ greatly every time I re-train the network – i.e. it seems the results are very dependent on the initial weights.
I am not sure if this is a problem of overtraining? Any help would be greatly appreciated.
Sam

Best Answer

Sam harris on 2 Jul 2012
 % Create a Nonlinear Autoregressive Network with External Input
 % inputDelays = 1:1; feedbackDelays = 1:1; hiddenLayerSize = 10;
1. What makes you think these are appropriate inputs??
 % net =narxnet(inputDelays,feedbackDelays,hiddenLayerSize); 
 % net.inputs{1}.processFcns = {'removeconstantrows','mapminmax'};
 % net.inputs{2}.processFcns = {'removeconstantrows','mapminmax'};
2. Why bother? The last 2 statements are defaults.
 % [inputs,inputStates,layerStates,targets] = preparets(net,inputSeries,{},targetSeries); 
 % net.divideFcn = 'dividerand'; % Divide data randomly
 % net.divideMode = 'value'; % Divide up every value
3. The last 2 statements are inappropriate for time series
 % net.divideParam.trainRatio = 70/100; net.divideParam.valRatio = 15/100;
 % net.divideParam.testRatio = 15/100; 
 % net.trainFcn = 'trainlm'; % Levenberg-Marquardt 
 % net.performFcn = 'mse'; % Mean squared error 
 % net.plotFcns = {'plotperform','plottrainstate','plotresponse', ...
 % 'ploterrcorr', 'plotinerrcorr'};
4. Why bother? The last 6 statements are defaults.
 % % Train the Network 
 % [net,tr] =train(net,inputs,targets,inputStates,layerStates);
 % if true % code 
 % end
5. What does "if true ...etc... end" suppose to do?
6. You still have to close the loop and continue training.
7. See
http://www.mathworks.com/matlabcentral/newsreader/view_thread/332147#912806
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