DocumentCode
739158
Title
Two-stage parameter estimation algorithms for Box–Jenkins systems
Author
Feng Ding ; Honghong Duan
Author_Institution
Key Lab. of Adv. Process Control for Light Ind. (Minist. of Educ.), Jiangnan Univ., Wuxi, China
Volume
7
Issue
8
fYear
2013
fDate
10/1/2013 12:00:00 AM
Firstpage
646
Lastpage
654
Abstract
A two-stage recursive least-squares identification method and a two-stage multi-innovation stochastic gradient method are derived for Box-Jenkins (BJ) systems. The key is to decompose a BJ system into two subsystems, one containing the parameters of the system model and the other containing the parameters of the noise model, and then to estimate the parameters of the system model and the noise model, respectively. The simulation examples indicate that the proposed algorithms can generate highly accurate parameter estimates and require small computational burden.
Keywords
gradient methods; least squares approximations; parameter estimation; recursive estimation; signal processing; stochastic processes; BJ system; Box-Jenkins systems; noise model parameter estimation; system model parameter estimation; two-stage multiinnovation stochastic gradient method; two-stage parameter estimation algorithms; two-stage recursive least-square identification method;
fLanguage
English
Journal_Title
Signal Processing, IET
Publisher
iet
ISSN
1751-9675
Type
jour
DOI
10.1049/iet-spr.2012.0183
Filename
6611355
Link To Document