Identification methods of Hammerstein nonelinear CARAR systems

Author

Xiao, Yongsong ; Yue, Na ; Ding, Feng

Author_Institution

Control Sci. & Eng. Res. Center, Jiangnan Univ., Wuxi, China

fYear

2010

fDate

26-28 May 2010

Firstpage

3284

Lastpage

3288

Abstract

A recursive generalized least squares and a generalized stochastic gradient algorithms are developed for Hammerstein nonlinear systems with memoryless nonlinear blocks followed by linear dynamical blocks described by CARAR models (HCARAR models). The basic idea is to replace the unmeasurable noise terms in the information vectors with their estimates and to compute the noise estimates through different methods. The simulation results show the performance of the proposed algorithms.

Keywords

gradient methods; identification; least squares approximations; nonlinear control systems; recursive estimation; Hammerstein nonlinear CARAR system; generalized stochastic gradient algorithm; identification method; linear dynamical block; memoryless nonlinear block; recursive generalized least square; Computational modeling; Least squares approximation; Least squares methods; Nonlinear control systems; Nonlinear systems; Parameter estimation; Polynomials; Stochastic resonance; Stochastic systems; Vectors; Hammerstein Models; Least Squares; Parameter Estimation; Recursive Identification; Stochastic Gradient;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Decision Conference (CCDC), 2010 Chinese

Conference_Location

Xuzhou

Print_ISBN

978-1-4244-5181-4

Electronic_ISBN

978-1-4244-5182-1

Type

conf

DOI

10.1109/CCDC.2010.5498591

Filename

5498591