Two-stage parameter estimation algorithms for Box–Jenkins systems

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 para...

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Vydáno v:	IET signal processing Ročník 7; číslo 8; s. 646 - 654
Hlavní autoři:	Ding, Feng, Duan, Honghong
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Stevenage The Institution of Engineering and Technology 01.10.2013 Institution of Engineering and Technology John Wiley & Sons, Inc
Témata:	Algorithms Applied sciences BJ system decomposition Box‐Jenkins systems Computer simulation Detection, estimation, filtering, equalization, prediction Estimates Exact sciences and technology gradient methods Information, signal and communications theory least squares approximations Least squares method Mathematical models Noise noise model parameter algorithm parameter estimation Recursive recursive estimation Signal and communications theory Signal processing Signal, noise stochastic processes system model parameter algorithm Telecommunications and information theory two‐stage multiinnovation stochastic gradient method two‐stage parameter estimation algorithms two‐stage recursive least‐square identification method least squares approximations system model parameter algorithm signal processing Box-Jenkins systems two-stage parameter estimation algorithms two-stage recursive least-square identification method noise model parameter algorithm two-stage multiinnovation stochastic gradient method BJ system decomposition recursive estimation parameter estimation stochastic processes gradient methods Parameter estimation Innovation Multistage method Simulation Least squares method Subsystem Recursive method Stochastic method Algorithm Gradient method
ISSN:	1751-9675, 1751-9683, 1751-9683
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Popis
Shrnutí:	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.
Bibliografie:	SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-2 content type line 23
ISSN:	1751-9675 1751-9683 1751-9683
DOI:	10.1049/iet-spr.2012.0183