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

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
EventICROS-SICE International Joint Conference 2009, ICCAS-SICE 2009 - Fukuoka
Duration: 2009 Aug 182009 Aug 21

Other

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

Fingerprint

Nonlinear networks
Principal component analysis
Nonlinear systems

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., & Furuzuki, T. (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]

An improvement of quasi-ARX predictor to control of nonlinear systems using nonlinear PCA network. / Wang, Lan; Furuzuki, Takayuki.

ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings. 2009. p. 5095-5100 5334442.

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

Wang, L & Furuzuki, T 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., 5334442, pp. 5095-5100, ICROS-SICE International Joint Conference 2009, ICCAS-SICE 2009, Fukuoka, 09/8/18.
Wang L, Furuzuki T. 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. 2009. p. 5095-5100. 5334442
Wang, Lan ; Furuzuki, Takayuki. / An improvement of quasi-ARX predictor to control of nonlinear systems using nonlinear PCA network. ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings. 2009. pp. 5095-5100
@inproceedings{37828d65c1f94b4e8415c5aa709eb207,
title = "An improvement of quasi-ARX predictor to control of nonlinear systems using nonlinear PCA network",
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.",
keywords = "Neurofuzzy network, Nonlinear principal component analysis (NPCA), Nonlinear system, Quasi-ARX model",
author = "Lan Wang and Takayuki Furuzuki",
year = "2009",
language = "English",
isbn = "9784907764333",
pages = "5095--5100",
booktitle = "ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings",

}

TY - GEN

T1 - An improvement of quasi-ARX predictor to control of nonlinear systems using nonlinear PCA network

AU - Wang, Lan

AU - Furuzuki, Takayuki

PY - 2009

Y1 - 2009

N2 - 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.

AB - 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.

KW - Neurofuzzy network

KW - Nonlinear principal component analysis (NPCA)

KW - Nonlinear system

KW - Quasi-ARX model

UR - http://www.scopus.com/inward/record.url?scp=77951138885&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=77951138885&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:77951138885

SN - 9784907764333

SP - 5095

EP - 5100

BT - ICCAS-SICE 2009 - ICROS-SICE International Joint Conference 2009, Proceedings

ER -