MATLAB: How to use narxnet for new set of data

Deep Learning Toolboxnarxnet

I trained a narxnet network with 4 inputs and 2 targets for system identification. The training performance (RMS) seems pretty good, but the problem is that I don't know how to use this net for new set of data. According to the Matlab Help, I should use closed loop form (netc) for doing this:
netc = closeloop(net);
view(netc);
[Xs,Xi,Ai,Ts] = preparets(netc,X,{},T);
y = netc(Xs,Xi,Ai);
In this process, the target value (T) is required which doesn't make sense because target is not available for system identification. How should I use this network for new set of input data while I don't have target values?

Best Answer

1. The best way to solve a problem is to use the MATLAB example data with which we are familiar
help nndata
2. It doesn't make sense to guess at what the delays should be. Find the statistically significant feedback delays indicated by the target autocorrelation function and the statistically significant input delays indicated by the input/target crosscorrelation function. If you don't have a correlation function algorithm in another toolbox use nncorr. However, it has a bug that yields symmetric crosscorrelations. Therefore you have to combine nncorr(x,t...) with nncorr(t,x,...) as illustrated in many of my posts. Search using
greg narxnet nncorr
3. It doesn't make sense to use the default datadivision setting 'dividerand' because it ruins the correlations found above. Use either 'divideblock' or 'divideind'.
4. Use as few hidden nodes as possible. I typically design 10 nets for each trial value of H less than the upperbound Hub that is determined using the number of weights used (including) delays.
5. See what I wrote previously re (a)retraining, (b) Using Xf,Af for new data.
Greg
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