NARX Tapped Delay Line

I
Ilyas · Aug 3, 2021 · 1.9K views
Question
What does the Tapped Delay Line (TDL) in NARX NN exactly do? 1- Does it sum the previous values (either input or target) and use as input?   Because that's what I understood from the research I have made. This is a concept related to digital signal processing, and it is defined like so.   2- Does it input the previous values as individual points?   This makes more sense to me, as this will keep more information in the data and should improve prediction ability.   Thinking about it, is there a way to determine what we want, by coding? Because both methods could be useful under different circumstances.
Expert Answer
Profile picture of John Williams
John Williams PhD Expert
Answered Aug 26, 2026
Consider this STATIC APPROXIMATOR to the OL (Open Loop) NARXNET:
 
 
 clear all, close all,clc, plt=0
[  INPUT TARGET  ] = simplenarx_dataset;
input  = cell2mat(INPUT);
target = cell2mat(TARGET);
[ I N ] = size(input)  % [ 1 100 ]
[ O N ] = size(target) % [ 1 100 ]
plt = plt+1, figure(plt), hold on
plot( input,  'k', 'LineWidth', 2 )
plot( target, 'b', 'LineWidth', 2 )

 net0 = narxnet(1:2,1:2,10); % default OL time-series configuration
net = fitnet(10);          % static OL approximator
x   = [ input(1:end-2);input(2:end-1); ...
         target(1:end-2); target(2:end-1)];
t   = target(3:end);
rng(4151941)
[ net tr y e ] = train(net,x,t );
NMSE = mse(e)/var(t',1) %  3.1731e-07
Rsq     = 1 -  NMSE     % 1

 plt = plt+1, figure(plt), hold on
plot( 1:N-2, x, 'k', 'LineWidth',2)
plot( 3:N,   t, 'b', 'LineWidth',2)
plot( 3:N,   y, 'ro', 'LineWidth',2)
legend('INPUT', 'TARGET','OUTPUT')
PS: It seems to me that a STEPWISE QUASI-STATIC APPROXIMATOR to the CL (Closed Loop) NARXNET should be possible by using the function ADAPT (or even TRAIN?) in a loop that uses y(n-2:n-1) to predict y(n).
 
100% Run Guarantee 3-Hour Fast-Track Delivery

Need a Custom Version or Complete Simulation for This Problem?

Our 500+ PhD engineers build, debug, and optimize working MATLAB scripts and Simulink (.slx) models tailored to your exact assignment rubrics with zero plagiarism.

Tested on MATLAB R2024b / R2026a
Turnitin 0% Plagiarism Report
Free 7-Day Revisions Guarantee
Have a different question? Ask here

Get a Free Consultation or a Sample Assignment Review!