Identification of quasi-ARX neurofuzzy model by using SVR-based approach with input selection

Yu Cheng*, Lan Wang, Jing Zeng, Jinglu Hu

*この研究の対応する著者

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

Quasi-ARX neurofuzzy (Q-ARX-NF) models have shown great approximation ability and usefulness in nonlinear system identification and control. However, the incorporated neurofuzzy networks suffer from the curse-of-dimensionality problem, which may result in high computational complexity and over-fitting. In this paper, support vector regressor (SVR) based identification approach is used to reduce computational complexity with the help of transforming the original problem into Lagrange space, which is only sensitive to the number of data samples. Furthermore, to improve the generalization capability, a parsimonious model structure is obtained by eliminating insignificant input variables for the incorporated neurofuzzy network, which is implemented by genetic algorithm (GA) based input selection method with a novel fitness evaluation function. Two numerical simulations are tested to show the effectiveness of the proposed method.

本文言語English
ホスト出版物のタイトル2011 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2011 - Conference Digest
ページ1585-1590
ページ数6
DOI
出版ステータスPublished - 2011
イベント2011 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2011 - Anchorage, AK, United States
継続期間: 2011 10月 92011 10月 12

出版物シリーズ

名前Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
ISSN(印刷版)1062-922X

Other

Other2011 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2011
国/地域United States
CityAnchorage, AK
Period11/10/911/10/12

ASJC Scopus subject areas

  • 電子工学および電気工学
  • 制御およびシステム工学
  • 人間とコンピュータの相互作用

フィンガープリント

「Identification of quasi-ARX neurofuzzy model by using SVR-based approach with input selection」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル