An improvement of quasi-ARX predictor to control of nonlinear systems using nonlinear PCA network

Lan Wang, Jinglu Hu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, a nonlinear principal component analysis (NPCA) is introduced to improve the quasi-ARX modeling. One part of the quasi-ARX model is an ordinary neurofuzzy network to parameterize the coefficients which faces to a problem of high dimension. NPCA is used for this part to deal with the problem. The processes of modeling, parameter estimating and control are given detailedly. Some simulations of systems controlling are provided to illustrate the effectiveness of the proposed modeling approach.

Original languageEnglish
Title of host publicationICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings
Pages5095-5100
Number of pages6
Publication statusPublished - 2009 Dec 1
EventICROS-SICE International Joint Conference 2009, ICCAS-SICE 2009 - Fukuoka, Japan
Duration: 2009 Aug 182009 Aug 21

Publication series

NameICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings

Other

OtherICROS-SICE International Joint Conference 2009, ICCAS-SICE 2009
CountryJapan
CityFukuoka
Period09/8/1809/8/21

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Keywords

  • Neurofuzzy network
  • Nonlinear principal component analysis (NPCA)
  • Nonlinear system
  • Quasi-ARX model

ASJC Scopus subject areas

  • Information Systems
  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering

Cite this

Wang, L., & Hu, J. (2009). An improvement of quasi-ARX predictor to control of nonlinear systems using nonlinear PCA network. In ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings (pp. 5095-5100). [5334442] (ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings).