An efficient identification scheme for a nonlinear polynomial NARX model

Yu Cheng, Lan Wang, Miao Yu, Takayuki Furuzuki

研究成果: Article

5 引用 (Scopus)

抄録

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.

元の言語English
ページ(範囲)70-73
ページ数4
ジャーナルArtificial Life and Robotics
16
発行部数1
DOI
出版物ステータスPublished - 2011 6

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Statistical Models
Evolutionary algorithms
Identification (control systems)
Polynomials
Computer simulation

ASJC Scopus subject areas

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

これを引用

An efficient identification scheme for a nonlinear polynomial NARX model. / Cheng, Yu; Wang, Lan; Yu, Miao; Furuzuki, Takayuki.

:: Artificial Life and Robotics, 巻 16, 番号 1, 06.2011, p. 70-73.

研究成果: Article

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