Multi-innovation Stochastic Gradient Algorithm for Hammerstien Nonlinear ARX Systems

Author

Liao, Yuwu ; Yu, Li ; Xiang, Lili

Author_Institution

Sch. of Phys. & Electron. Eng., Xiangfan Univ., Xiangfan, China

fYear

2010

fDate

13-15 Dec. 2010

Firstpage

868

Lastpage

871

Abstract

Since the multi-innovation stochastic gradient (MISG) method can produce highly accurate parameter estimates for linear regression models, this paper extends the MISG method to nonlinear regression models and presents an MISG algorithm for Hammerstein nonlinear CAR (ARX) systems with memory less nonlinear blocks followed by controlled autoregressive models. The numerical results indicate that the proposed algorithm can effectively estimate the parameters of nonlinear systems.

Keywords

autoregressive processes; gradient methods; nonlinear dynamical systems; parameter estimation; regression analysis; Hammerstein nonlinear CAR systems; Hammerstien nonlinear ARX systems; MISG method; controlled autoregressive models; linear dynamical blocks; linear regression models; memory less nonlinear blocks; multiinnovation stochastic gradient algorithm; parameter estimation; recursive identification; Computational modeling; Computers; Mathematical model; Parameter estimation; Signal processing algorithms; Stochastic processes; Hammerstein models; Parameter estimation; Recursive identification; Stochastic gradient;

fLanguage

English

Publisher

ieee

Conference_Titel

Genetic and Evolutionary Computing (ICGEC), 2010 Fourth International Conference on

Conference_Location

Shenzhen

Print_ISBN

978-1-4244-8891-9

Electronic_ISBN

978-0-7695-4281-2

Type

conf

DOI

10.1109/ICGEC.2010.220

Filename

5715570