An efficient identification scheme for nonlinear polynomial NARX model

Yu Cheng, Miao Yu, Lan Wang, Jinglu Hu

研究成果: Conference contribution

抄録

Nonlinear polynomial NARX model identification often faces the problem of huge pool of candidate terms, which makes the evolutionary optimization based identification algorithm work with low efficiency. This paper proposes an efficient identification scheme with pre-processing to reduce the searching space effectively. Both the input selection and term selection are implemented to truncate the candidate pool with the help of correlation based orthogonal forward selection (COFS) algorithm and simplified orthogonal least square (OLS) algorithm, respectively. Then multi-objective evolutionary algorithm (MOEA) is used to identify the polynomial model in a relative small searching space.

本文言語English
ホスト出版物のタイトルProceedings of the 16th International Symposium on Artificial Life and Robotics, AROB 16th'11
ページ499-502
ページ数4
出版ステータスPublished - 2011 12 1
イベント16th International Symposium on Artificial Life and Robotics, AROB '11 - Beppu, Oita, Japan
継続期間: 2011 1 272011 1 29

出版物シリーズ

名前Proceedings of the 16th International Symposium on Artificial Life and Robotics, AROB 16th'11

Conference

Conference16th International Symposium on Artificial Life and Robotics, AROB '11
CountryJapan
CityBeppu, Oita
Period11/1/2711/1/29

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction

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