Nonlinear System Identification Using Quasi-ARX RBFN Models with a Parameter-Classified Scheme

Lan Wang, Yu Cheng, Takayuki Furuzuki, Jinling Liang, Abdullah M. Dobaie

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Quasi-linear autoregressive with exogenous inputs (Quasi-ARX) models have received considerable attention for their usefulness in nonlinear system identification and control. In this paper, identification methods of quasi-ARX type models are reviewed and categorized in three main groups, and a two-step learning approach is proposed as an extension of the parameter-classified methods to identify the quasi-ARX radial basis function network (RBFN) model. Firstly, a clustering method is utilized to provide statistical properties of the dataset for determining the parameters nonlinear to the model, which are interpreted meaningfully in the sense of interpolation parameters of a local linear model. Secondly, support vector regression is used to estimate the parameters linear to the model; meanwhile, an explicit kernel mapping is given in terms of the nonlinear parameter identification procedure, in which the model is transformed from the nonlinear-in-nature to the linear-in-parameter. Numerical and real cases are carried out finally to demonstrate the effectiveness and generalization ability of the proposed method.

Original languageEnglish
Article number8197602
JournalComplexity
Volume2017
DOIs
Publication statusPublished - 2017 Jan 1

ASJC Scopus subject areas

  • General

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