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);
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1. The best way to solve a problem is to use the MATLAB example data with which we are familiarhelp nndata2. 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 usinggreg narxnet nncorr3. It doesn't make sense to use the default datadivision setting...
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