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 :
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