An efficient identification scheme for a nonlinear polynomial NARX model

Yu Cheng, Lan Wang, Miao Yu, Jinglu Hu

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

Nonlinear polynomial NARX (nonlinear autoregressive with exogenous inputs) model identification often faces the problem of the huge size of the candidate pool, which makes the "wrapper" structure selection algorithm worked at low efficiency. In this article, a correlation-based orthogonal forward selection (COFS) algorithm is proposed to select the necessary input variables so that the candidate pool thus formed becomes tractable. What is more, it is trunked by an importance index-based term selection method, where a multiobjective evolutionary algorithm (MOEA)-based structure selection algorithm could be used to identify the polynomial model efficiently. Two numerical simulations are carried out to show the effectiveness of the proposed method.

Original languageEnglish
Pages (from-to)70-73
Number of pages4
JournalArtificial Life and Robotics
Volume16
Issue number1
DOIs
Publication statusPublished - 2011 Jun 1

Keywords

  • Efficient
  • Input selection
  • Nonlinear polynomial model identification

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Artificial Intelligence

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