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Extended stochastic gradient identification algorithms for Hammerstein–Wiener ARMAX systems

Extended stochastic gradient identification algorithms for Hammerstein-Wiener ARMAX systems
descriptionPublicationkeyboard_double_arrow_right Article 01 Dec 2008 English Publisher:Elsevier BVJournal:Computers & Mathematics with Applications, volume 56, pages 3,157-3,164 (issn: 0898-1221, Copyright policy )
Authors: Dongqing Wang; Feng Ding 0001;

doi: 10.1016/j.camwa.2008.07.015

Extended stochastic gradient identification algorithms for Hammerstein–Wiener ARMAX systems

Abstract

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Keywords

Numerical solutions to stochastic differential and integral equations, Estimation and detection in stochastic control theory, Hammerstein-Wiener systems, Estimation in multivariate analysis, Point estimation, Convergence properties, stochastic gradient, forgetting factor, Computational Mathematics, convergence properties, Computational Theory and Mathematics, Forgetting factor, Modelling and Simulation, Parameter estimation, Stochastic gradient, parameter estimation, Hammerstein–Wiener systems

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
160
Top 10%
Top 1%
Top 1%
hybrid