how can find the optimal delays and number of hidden nodes in narnet for forecasting task?

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I have tried with these code but I haven't found solutions:
1/ Optimal timelags?
N = length(Target)
zy = zscore(Target,1);
autocorry = ifft( abs(fft(zy)).^2 )/N
%AUTOCORRELATION
ZT=zscore(Target,1);
autocorrT = nncorr(ZT,ZT,N-1,'biased')
figure(3)
plot(autocorrT)
%title('ACF')
2/Optimal hidden nodes???
%To find H
[I ,N]=size(Input);
[O ,N]=size(Target);
Neq=N*O;
Hub=floor((N-1)*O/(I+O+1)) %max H for Neq>=Nw
Thank you in advance.
  7 Commenti
Greg Heath
Greg Heath il 12 Ott 2015
Modificato: Greg Heath il 12 Ott 2015
Why are you posting questions about NARXNET in a NARNET thread ?
Search the NEWSGROUP and ANSWERS
NEWSGROUP ANSWERS
narxnet nncorr 7 42
narxnet xcorr 2 8
narxnet fft 1 13
Read the most recent NEWSGROUP posts first.
I searched the NEWSGROUP threads listed above. Only the "narxnet nncorr" search was fruitfull. Of the 7 threads, only 3 were useful. However, 8 messages in those threads are useful:
1. Subject: SIGNIFICANT AUTOCORRELATION DELAYS OF THE SIMPLENAR_DATASET
Date: 31 May, 2015
Messages: 12, 15, 19, 23
2. Subject: NARNET TUTORIAL ON MULTISTEP AHEAD PREDICTIONS
Date: 17 Dec, 2014
Messages: 1 , 7, 11
3. Subject: open-loop to closed-loop Narx NN in Matlab Help
Date: 21 Oct, 2012
Message: 4
Hope this helps.
Greg

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Greg Heath
Greg Heath il 8 Ott 2015
I have many posts regarding this.
Search both the NEWSGROUP and ANSWERS using subsets of the following search words
greg narnet nncorr siglag95 Hub Ntrials tutorial
Hope this helps.
Greg
  4 Commenti
coqui
coqui il 11 Ott 2015
Thanks Greg. We can not use this:
cor_seq=xcorr(X,T)% X is input and Y is the target
after, we can plot the cor_seq vs lags and we can find the lag which can define the symmetric part of the plot?????
Is right??????
Greg Heath
Greg Heath il 12 Ott 2015
No.
1. Find all NONNEGATIVE lags where the absolute value of the input/target crosscorrelation function exceeds a specified threshold.
2. For a threshold I use an estimate of the 95 per cent significance level of Gaussian noise.
3. Then choose a subset of the smallest significant lags to include in the design. For example, if the smallest significant input lags are 0 2 3 6 8, using ID = 0:3 might be a reasonable first try.
4. Similarly for the target autocorrelation function and significant feedback lags EXCEPT, the feedback lags must be POSITIVE.
5. Finally, given the choice of delays, use the double for loop (h = Hmin:dH:Hmax, i = 1:Ntrials) approach to determine the smallest number of hidden nodes that yields either an acceptable, or the best design.
Hope this helps.
Greg

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