Cluster analysis of NARMAX models for signal-dependent systems

Author

Aguirre, L.A. ; Jacome, C.R.F.

Author_Institution

Dept. de Engenharia Electron., Univ. Fed. de Minas Gerais, Belo Horizonte, Brazil

Volume

145

Issue

4

fYear

1998

fDate

7/1/1998 12:00:00 AM

Firstpage

409

Lastpage

414

Abstract

The structure of NARMAX models is described. No new algorithm for structure selection is proposed, but rather the paper investigates how different model structures are produced by a large class of nonlinearities in the system which generates the data. The concept of term clusters is used to understand how different types of terms are required to model nonlinear systems. A term cluster generating mechanism is suggested, this can be used not only to understand how certain types of terms appear in NARMAX models but also, in the case of prior knowledge, such a mechanism can serve as an aid to select the structure of nonlinear models. The results are quite general and can be applied to polynomial, rational and extended-set NARMAX representations

Keywords

autoregressive moving average processes; identification; nonlinear systems; pattern recognition; polynomials; NARMAX models; cluster analysis; extended-set NARMAX representations; nonlinear systems; nonlinearities; polynomial representations; rational representations; signal-dependent systems; term clusters;

fLanguage

English

Journal_Title

Control Theory and Applications, IEE Proceedings -

Publisher

iet

ISSN

1350-2379

Type

jour

DOI

10.1049/ip-cta:19982112

Filename

708548