Data-Driven Simulation for NARX Systems

Vikas K. Mishra, Ivan Markovsky, Antonio Fazzi, Philippe Dreesen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

Abstract

Nonlinear phenomena can be represented as nonlinear autoregressive exogenous (NARX) systems. NARX systems can be seen as a nonlinear version of linear infinite impulse response filter. Data-driven approaches are witnessing considerable interests in recent times and they are well-flourished for linear time-invariant systems. However, for nonlinear systems, they are still limited and attempts have been made to generalize the results for linear systems to nonlinear systems. In this paper, we study the problem of data-driven simulation for NARX systems: compute the output trajectory from a given input trajectory and initial conditions without explicitly identifying the parametric model; the model is implicitly identified by observed trajectory. Next, we develop an algorithm for the implementation of our approach. Finally, we illustrate the developed algorithm by numerical experiments.

Original languageEnglish
Title of host publication29th European Signal Processing Conference, EUSIPCO 2021 - Proceedings
PublisherEuropean Signal Processing Conference, EUSIPCO
Pages1055-1059
Number of pages5
ISBN (Electronic)9789082797060
DOIs
StatePublished - 1 Jan 2021
Externally publishedYes
Event29th European Signal Processing Conference, EUSIPCO 2021 - Dublin, Ireland
Duration: 23 Aug 202127 Aug 2021

Publication series

NameEuropean Signal Processing Conference
Volume2021-August
ISSN (Print)2219-5491

Conference

Conference29th European Signal Processing Conference, EUSIPCO 2021
Country/TerritoryIreland
CityDublin
Period23/08/2127/08/21

Keywords

  • Data-driven simulation
  • NARX
  • System identification

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering

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