Implementing initial weights and significant feedback delays in a NARNET

Prashant Kumar answered . 2025-07-10 21:05:12

1. Unfortunately, the form of NNCORR that you are using is BUGGY!
2.  

   PROOF:
   a. plot(-(N-1):N-1, autocorrT)
   b. minmax(autocorrT) = [ -2.3082  1.0134 ]
   c. sigacorr  = ones(1,41)

2. BETTER SOLUTION: Use the Fourier Method

   za = zscore(a,1); zb = zscore(b,1); % a,b are double (i.e., not cells)
   A  = fft(za);      B = fft(zb);

   CSDab         = A.*conj(B);            % Cross Spectral Density
   crosscorrFab = ifft(CSDab);            % F => Fourier method
   crosscorrFba = conj(crosscorrFab);

3. You might wish to compare this with the NNCORR documentation options

   help nncorr
   doc nncorr

% The optional FLAG determines how nncorr normalizes correlations.
%     'biased'     - scales the raw cross-correlation by 1/N.
%     'unbiased' - scales the raw correlation by 1/(N-abs(k)), where k 
%                       is the index into the result.
%     'coeff'       - normalizes the sequence so that the correlations at 
%                       zero lag are identically 1.0.
%     'none'      - no scaling (this is the default).

 crosscorrBab = nncorr( za, zb, N-1, 'biased' );   % B ==> "b"iased
 crosscorrNab = nncorr( za, zb, N-1, 'none' )/N;   % N ==> "n"one

 crosscorrUab  = nncorr( za, zb, N-1, 'unbiased' ); % U ==> "u"nbiased
 crosscorrtMab = nncorr( za, zb, N-1 );             % M ==> "m"issing flag

 % crosscorrCab = nncorr( za, zb, N-1, 'coeff' ); ERROR: BUG

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