John Williams answered . 2024-05-18 06:59:33

     lookfor autocorrelation

then use the help and doc commands on functions that are listed. For example

 help nncorr
 doc nncorr

     autocorrelation neural narnet nncorr greg

 net = narnet(FD,H);

 1. Using capitals to indicate cell variables.
 2. Using nncorr to find a reasonable subset of feedback delays
 3. Using a for loop to find a reasonable value for H (e.g., Hmin:dH:Hmax)
 4. Not using the default 'dividerand' because it destroys the correlations between the output and feedback signals
 5. For each of numH candidate values for H, training success depends on starting with a good set of random initial weights. The best way to find one or more is to have an inner for loop over Ntrial random weight initializations that are created by the configure function.
 6. Explicitly initializing the random number generator before the outer loop so that you can duplicate any of the numH*Ntrials designs
 7. Often closing the loop on an openloop design to obtain netc does not yield acceptable results when inputted with original data. Therefore, the closeloop net should be trained beginning with the weights obtained from the openloop design to obtain netc2.

% And I’m not sure what to do with the LHS syntax thing, I did have xs ts xi and ai in my workspace so I tried adding the piece of code you wrote: % % [ net tr Ys Es Xf Af ] = train( net, Xs, Ts, Xi, Ai );

This code replaces the 3 step script

 [ net tr  ]  = train( net, Xs, Ts, Xi, Ai );
 [ Ys Xf Af ] = net( Xs, Xi, Ai );
 Es          = gsubtract(net,Ts,Ys);

 Finally, to predict into the future M timesteps beyond the end of the target data

 Xic2 = Xf; 
 Aic2 = Af;
 Ypred = netc2( cell(1,M), Xic2, Aic2);

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