Title

Identification of nonlinear systems using NARMAX model

Authors

Authors

A. Rahrooh;S. Shepard

Abbreviated Journal Title

Nonlinear Anal.-Theory Methods Appl.

Keywords

Nonlinear systems; Modeling; Identification; Narmax; NON-LINEAR SYSTEMS; Mathematics, Applied; Mathematics

Abstract

Most systems encountered in the real world are nonlinear in nature, and since linear models cannot capture the rich dynamic behavior of limit cycles, bifurcations, etc. associated with nonlinear systems, it is imperative to have identification techniques which are specific for nonlinear systems. The problem considered in this work is the modeling of nonlinear discrete systems based on the set of input-output data. This is often the only approach to modeling, as in most cases only external (i.e. input-output) data are available. This paper also discusses the practical aspects of identification of nonlinear systems. The NARMAX (Nonlinear Auto Regressive Moving Average with eXogenous input) model provides a unified representation for a wide class of nonlinear systems and has obvious advantages over functional series representations such as Volterra and Wiener series. This model is proven to provide a better parameter estimation and prediction accuracy than the linear model. (C) 2009 Published by Elsevier Ltd

Journal Title

Nonlinear Analysis-Theory Methods & Applications

Volume

71

Issue/Number

12

Publication Date

1-1-2009

Document Type

Article

Language

English

First Page

E1198

Last Page

E1202

WOS Identifier

WOS:000277952800021

ISSN

0362-546X

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