A multi-innovation generalized extended stochastic gradient algorithm for output nonlinear autoregressive moving average systems

This paper proposes a generalized extended stochastic gradient (GESG) algorithm for estimating the parameters of a class of Wiener nonlinear autoregressive moving average systems using the gradient search. In order to improve the convergence rates of the GESG algorithm, a multi-innovation GESG algor...

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Veröffentlicht in:Applied mathematics and computation Jg. 247; S. 218 - 224
Hauptverfasser: Hu, Yuanbiao, Liu, Baolin, Zhou, Qin
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Inc 15.11.2014
Schlagworte:
Algorithms
Autoregressive moving average
Computer simulation
Convergence
Dynamical systems
Gradient algorithm
Mathematical analysis
Nonlinear system
Nonlinearity
Parameter estimation
Recursive identification
Stochasticity
Recursive identification
Parameter estimation
Gradient algorithm
Nonlinear system
ISSN:0096-3003, 1873-5649
Online-Zugang:Volltext
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Zusammenfassung:This paper proposes a generalized extended stochastic gradient (GESG) algorithm for estimating the parameters of a class of Wiener nonlinear autoregressive moving average systems using the gradient search. In order to improve the convergence rates of the GESG algorithm, a multi-innovation GESG algorithm is derived. The simulation results indicate that the proposed algorithms can effectively estimate the parameters of a class of output nonlinear systems.
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ISSN:0096-3003
1873-5649
DOI:10.1016/j.amc.2014.08.096