Identification of nonlinear systems using NARMAX model

    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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