Odd data preparation with NARX network

Nikita OKS asked: 2021-08-05 neural networks , time series , data

Learn odd data preparation techniques for NARX networks with MATLAB. Improve your models accuracy & performance. Get started today and unlock valuable insights

Expert Answer

Prashant Kumar answered . 2025-07-10 00:20:21

% You say that you have no delays. If you have no delays you should be using fitnet or patternnet.

% However, your code shows 1 input delay and 1 feedback delay.

% Your use of v for both input and target is confusing.

% In addition, your 3-D inputs should not be represented as row vectors

% Taking advantage of defaults , using the RNG seed 4151941, and using the simplenarx_dataset with the input tripled yields the following code. Are you able to use the following to better explain your concerns?

 close all, clear all, clc
[ X0 T ] = simplenarx_dataset;
x0        = cell2mat( X0 );
X         = con2seq( [ x0; x0; x0 ] );
x          = cell2mat(X);
whos
% Name   Size     Bytes   Class     
%   T       1x100   12000   cell                
%   X       1x100   13600   cell                
%   X0     1x100   12000   cell 
%   x       3x100     2400   double    
%   x0      1x100      800   double              

 ID = 1, FD = 1, H  = 8
neto = narxnet( ID, FD, H );
[ Xo, Xoi, Aoi, To ] = preparets( neto, X, {}, T );
to = cell2mat(To); MSE00o = var(to,1); %  0.10199
net.divideFcn  = 'divideblock'; 
rng(415941)
[ neto, tro, Yo, Eo, Xof Aof ] = train( neto, Xo, To, Xoi, Aoi );
% [ Yo Xof Aof ] = neto( Xo, Xoi, Ai); Eo = gsubtract(To,Yo);
NMSEo     = mse(Eo)/MSE00o           % 0.15076
NMSEtrno = tro.best_perf/MSE00o    % 0.12489
NMSEvalo = tro.best_vperf/MSE00o   %  0.18163
NMSEtsto  = tro.best_tperf/MSE00o   %  0.23894

% Check: 0.7*0.12489+0.15*(0.18163+0.23894) = 0.15051

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