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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