How to train NARX neural network in closed loop

Kshitij Singh answered . 2025-07-05 10:11:23

 close all, clear all, clc
disp('DIRECT TRAINING OF A CLOSELOOP NARXNET')
load('maglev_dataset');
whos
%   Name              Size      Bytes   Class 
%   maglevInputs      1x4001    272068  cell                
%   maglevTargets     1x4001    272068  cell                
X   = maglevInputs; 
T   = maglevTargets;
ID  = 1:2, FD = 1:2, H  = 10   % Default values
netc                    = closeloop(narxnet(ID,FD,H));
view(netc)
netc.divideFcn          = 'divideblock';
[ Xcs, Xci, Aci, Tcs ]  = preparets( netc, X, {}, T );
tcs                     = cell2mat(Tcs);
whos X T Xcs Xci Aci Tcs tcs
%  Name     Size     Bytes   Class
%   Aci     2x2         416  cell                
%   T       1x4001   272068  cell                
%   Tcs     1x3999   271932  cell                
%   X       1x4001   272068  cell                
%   Xci     1x2         136  cell                
%   Xcs     1x3999   271932  cell                
%   tcs     1x3999    31992  double              

 MSE00cs = var(tcs,1)  % 2.0021 ( 1-dim MSE reference)

 rng(4151941)
tic
[netc trc Ycs Ecs Xcf Acf ] = train(netc,Xcs,Tcs,Xci,Aci);
toc                           % 197 sec
view(netc)
whos Ycs Ecs Xcf Acf
%   Name    Size     Bytes     Class
%   Acf     2x2         416    cell               
%   Ecs     1x3999   271932    cell               
%   Xcf     1x2         136    cell               
%   Ycs     1x3999   271932    cell               

 stopcriterion  = trc.stop                    % Validation stop
bestepoch      = trc.best_epoch               % 4
ecs            = cell2mat(Ecs);
NMSEcs         = mse(ecs)/MSE00cs             %  1.2843
tcstrn         = tcs(trc.trainInd);
tcsval         = tcs(trc.valInd);
tcstst         = tcs(trc.testInd);
NMSEcstrn      = trc.best_perf/var(tcstrn,1)  %  1.3495
NMSEcsval      = trc.best_vperf/var(tcsval,1) %  0.9325
NMSEcstst      = trc.best_tperf/var(tcstst,1) %  1.6109

Not satisfied with the answer ?? ASK NOW