OL Weights in CL Training: Optimizing Your Weight Vector

Using weights from OL in CL training; how should the weight vector

Staffan asked: 2021-07-28 neural network , narnet , time series prediction

Learn how to effectively use Online Learning (OL) weights in Closed-Loop (CL) training. Optimize your weight vector for enhanced model performance. Read more!

Expert Answer

Kshitij Singh answered . 2025-07-12 22:28:08

% Using weights from OL in CL training; how should the % weight vector(s)/cell matrices be formatted when used % as input in train() ? % 2 views (last 30 days) % Asked by Staffan 17MAY2016 % % I am trying to attach the weights obtained in OL in the CL % training. I can see that the amount of data contained in the % weight sets; .IW, .LW and .B are altered when going from % open loop to closed loop....still, the weight vector obtained % from getwb() have the same amount of data for both in % OL and CL. Any ideas how to format the weight vector (in % the code below the weight vector is designated EWc1) % before inserting this to train()? Is there any way that % preparets() (or a similar function) can handle this?

GEH0 =[ ' YOU HAVE CONFUSED NETWORK WEIGHT BIAS '... ' VECTORS, WB, FROM GETWB WITH ERROR ' ... ' WEIGHTS, EW, OF LENGTH N THAT ARE CHOSEN ' ... ' BY THE PROGRAMMER TO WEIGHT EACH TERM ' .. ' IN MEAN SQUARE ERROR ' ]

GEH1 = 'I REMOVED SOME ENDING SEMICOLONS BELOW TO CHECK RESULTS'

clc
% Code and error message:
 close all
 clear all
% format long

   T = simplenar_dataset; 
   [ I, N ] = size(T)   % [ 1 100 ]

   d = 5
   GEH2= ' WHY 5 ?'

   FD = 1:d; 
   H = 10;
   % open net number one, input for closed net number 
   % one and closed net number two
   neto1 = narnet( FD, H );
   neto1.divideFcn = 'divideblock';
   [ Xo1, Xoi1, Aoi1, To1] = preparets( neto1, {}, {}, T );
   to = cell2mat( To1 ); 
  %  zto = zscore(to,1); 
    varto1 = mean(var(to',1))  % 0.062747
%  minmaxto = minmax([ to ; zto ]);

   rng( 'default' )
  % [neto1,tro,Yo1,Eo1,Aof1,Xof1] = train( neto1, Xo1, To1, Xoi1, Aoi1 );
   GEH3 = ' ERROR1: SWITCH Aof1 and Xof1'
    [neto1,tro,Yo1,Eo1,Xof1,Aof1] = train( neto1, Xo1, To1, Xoi1, Aoi1);
   %[Yo1,Xof1,Aof] = neto1( Xo1, Xoi1, Aoi1 ); 
   GEH4 = 'ERROR: Aof1 not Aof'
   %Eo1 = gsubtract( To1, Yo1 );
   GEH5 = ' COMMENT ABOVE 2 REDUNDANT STATEMENTS'

   NMSEo1 = mse( Eo1 ) /varto1 %1.6546e-09
   GEH6 = ' ALWAYS MAKE SURE NMSEo1 IS ADEQUATE BEFORE CL'

   yo1 = cell2mat( Yo1 );
   netc1 = closeloop(neto1);  
   EWo1=getwb(neto1);
   EWc1=getwb(netc1);
   isequal( EWo1, EWc1) % 1
   GEH7 = [ 'INCORRECT NOTATION: EW IS RESERVED FOR MSE' ...
                  ' ERROR WEIGHTS. USE WBo1 AND WBc1 FOR WEIGHT '...
                  ' BIAS VECTORS ' ]

   %netc1.divideFcn = 'divideblock';
   GEH8 = 'ABOVE ASSIGNMENT IS UNNECESSARY'

   [ Xc1, Xci1, Aci1, Tc1, EWc1 ] = preparets( netc1, {}, {}, T, EWo1 ); %  1.232667933023756e-08
   GEH9 = 'ERROR: SEE GEH0'
   GEH10 = 'WHAT IN THE WORLD IS 1.232667933023756e-08 ???'

% isequal( EWo1, cell2mat(EWc1)); % 1 if EWo1 is included in preparets, 0 if EWo1 is NOT included in preparets % figure(1) % plot(1:length(EWo1),EWo1,1:length(cell2mat(EWc1)),cell2mat(EWc1))

   GEH11 = 'DELETE ABOVE 3 STATEMENTS'

   isequal( Tc1, To1);
   tc = to;
   [netc1,troc1,Yc1,Ec1,Acf1,Xcf1] = train( netc1, Xc1, Tc1, Xci1, Aci1, EWc1); 

   GEH12 = 'ERRORS: 1: SWITCH Acf1 AND Xcf1 2: REMOVE EWc1'

   GEH13 = 'I"LL STOP HERE'