Decomposition based recursive identification algorithms for bilinear-parameter models

Author

Xuehai Wang ; Feng Ding

Author_Institution

Key Lab. of Adv. Process Control for Light Ind., Jiangnan Univ., Wuxi, China

fYear

2014

Firstpage

6116

Lastpage

6120

Abstract

This paper presents two recursive identification algorithms for bilinear-parameter models: a decomposition based stochastic gradient algorithm and a decomposition based recursive least squares algorithm. The key is to decompose a bilinear-parameter model into two fictitious subsystems, and to identify the parameters of each subsystem by replacing the unknown variables in the information vectors with their estimates. The simulation results show the performances the proposed algorithms.

Keywords

gradient methods; least squares approximations; recursive estimation; stochastic processes; bilinear-parameter models; decomposition based recursive identification algorithms; decomposition based recursive least squares algorithm; decomposition based stochastic gradient algorithm; information vectors; parameter identification; Least squares approximations; Mathematical model; Nonlinear systems; Parameter estimation; Signal processing algorithms; Stochastic processes; Vectors; Bilinear-parameter model; Gradient search; Least squares; Parameter estimation; Recursive identification;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation (WCICA), 2014 11th World Congress on

Type

conf

DOI

10.1109/WCICA.2014.7053768

Filename

7053768