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

Author

Wang, Lan ; Hu, Jinglu

Author_Institution

Grad. Sch. of Inf., Production & Syst., Waseda Univ., Kitakyushu, Japan

fYear

2009

fDate

18-21 Aug. 2009

Firstpage

5095

Lastpage

5100

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

autoregressive processes; fuzzy control; fuzzy neural nets; neurocontrollers; nonlinear control systems; parameter estimation; principal component analysis; nonlinear PCA network; nonlinear system control; ordinary neurofuzzy network; parameter estimation; principal component analysis; quasi-ARX predictor; Artificial neural networks; Control system synthesis; Control systems; Input variables; Kernel; Neural networks; Nonlinear control systems; Nonlinear systems; Parameter estimation; Principal component analysis; Neurofuzzy network; Nonlinear principal component analysis (NPCA); Nonlinear system; Quasi-ARX model;

fLanguage

English

Publisher

ieee

Conference_Titel

ICCAS-SICE, 2009

Conference_Location

Fukuoka

Print_ISBN

978-4-907764-34-0

Electronic_ISBN

978-4-907764-33-3

Type

conf

Filename

5334442