A two-step scheme for polynomial NARX model identification based on MOEA with prescreening process

Yu Cheng, Lan Wang, Jinglu Hu

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

Polynomial NARX (nonlinear autoregressive with exogenous) model identification has received considerable attention in last three decades. However, in a high-order nonlinear system, it is very difficult to obtain the model structure directly even with state-of-art algorithms, because the number of candidate monomial terms is huge and increases drastically as the model order increases. Motivated by this fact, in this research, the identification is performed in two steps: firstly a prescreening process is carried out to select a reasonable number of important monomial terms based on two kinds of the importance indices. Then, in the reduced searching space with only the selected important terms, multi-objective evolutionary algorithm (MOEA) is applied to determine a set of significant terms to be included in the polynomial model with the help of independent validation data. In this way, the whole identification algorithm is implemented efficiently. Numerical simulations are carried out to demonstrate the effectiveness of the proposed identification method.

Original languageEnglish
Pages (from-to)253-259
Number of pages7
JournalIEEJ Transactions on Electrical and Electronic Engineering
Volume6
Issue number3
DOIs
Publication statusPublished - 2011 May

Keywords

  • Multi-objective optimization
  • Polynomial NARX model
  • System identification
  • Two-step scheme

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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