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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Veröffentlicht in:IET signal processing Jg. 7; H. 8; S. 646 - 654
Hauptverfasser: Ding, Feng, Duan, Honghong
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Stevenage The Institution of Engineering and Technology 01.10.2013
Institution of Engineering and Technology
John Wiley & Sons, Inc
Schlagworte:
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
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung: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.
Bibliographie:SourceType-Scholarly Journals-1
ObjectType-Feature-1
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ISSN:1751-9675
1751-9683
1751-9683
DOI:10.1049/iet-spr.2012.0183