Data filtering and auxiliary model based recursive least squares estimation algorithm for OEMA systems

Author

Wang, Dongqing ; Sun, Shouqing ; Ding, Feng ; Song, Guiling

Author_Institution

Coll. of Autom. Eng., Qingdao Univ., Qingdao, China

fYear

2011

fDate

23-26 May 2011

Firstpage

477

Lastpage

481

Abstract

Based on the filtering theory and the auxiliary model identification idea, we present a filtering and auxiliary model based recursive least squares identification algorithm for an output-error moving average system. The proposed algorithm has a higher computational efficiency compared with the auxiliary model based recursive extended least squares algorithm.

Keywords

filtering theory; identification; least squares approximations; moving average processes; recursive estimation; OEMA systems; auxiliary model identification; computational efficiency; data filtering; filtering theory; output-error moving average system; recursive extended least squares algorithm; recursive least squares estimation algorithm; Computational modeling; Data models; Estimation; Least squares approximation; Mathematical model; Parameter estimation; Stochastic processes; Recursive identification; auxiliary model; filtering; least squares; output-error moving average (OEMA) systems; parameter estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

Advanced Control of Industrial Processes (ADCONIP), 2011 International Symposium on

Conference_Location

Hangzhou

Print_ISBN

978-1-4244-7460-8

Electronic_ISBN

978-988-17255-0-9

Type

conf

Filename

5930475