Recursive identification for Wiener-Hammerstein systems using instrumental variable

Author

Chen Xi ; Fang Hai-Tao

Author_Institution

Inst. of Syst. Sci., Beijing, China

fYear

2012

fDate

6-8 July 2012

Firstpage

3043

Lastpage

3048

Abstract

An identification method is discussed that deals with the Wiener-Hammerstein systems of general nonlinearity. By introducing a suitable instrumental variable a new algorithm is presented to recursively estimate the linear subsystems using stochastic approximation algorithm. The kernel nonparametric method is used to estimate the nonlinear function. The consistent analysis of the method is given under mild condition. A simulation example is provided justifying the proposed method.

Keywords

approximation theory; linear systems; nonlinear functions; nonparametric statistics; recursive estimation; stochastic processes; stochastic systems; Wiener-Hammerstein systems; consistent analysis; general nonlinearity; identification method; instrumental variable; kernel nonparametric method; linear subsystems; nonlinear function estimation; recursive estimation; recursive identification; stochastic approximation algorithm; Algorithm design and analysis; Approximation algorithms; Equations; Estimation; Instruments; Kernel; Nonlinear systems; Instrumental variable; Recursive estimate; Wiener-Hammerstein systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation (WCICA), 2012 10th World Congress on

Conference_Location

Beijing

Print_ISBN

978-1-4673-1397-1

Type

conf

DOI

10.1109/WCICA.2012.6358393

Filename

6358393