Filtering based multi-innovation extended stochastic gradient algorithm for Hammerstein nonlinear system modeling

This paper considers parameter estimation problems of Hammerstein finite impulse response moving average (FIR-MA) systems. In order to provide highly accurate parameter estimates and improve the convergence rate, a data filtering based multi-innovation extended stochastic gradient algorithm is prese...

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Vydáno v:Applied mathematical modelling Ročník 39; číslo 18; s. 5724 - 5732
Hlavní autoři: Wang, Ziyun, Wang, Yan, Ji, Zhicheng
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Inc 15.09.2015
Témata:
Filtering
Gradient search
Nonlinear system
Parameter estimation
System modeling
System modeling
Gradient search
Parameter estimation
Filtering
Nonlinear system
ISSN:0307-904X
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Shrnutí:This paper considers parameter estimation problems of Hammerstein finite impulse response moving average (FIR-MA) systems. In order to provide highly accurate parameter estimates and improve the convergence rate, a data filtering based multi-innovation extended stochastic gradient algorithm is presented to estimate the parameters of Hemmerstein FIR-MA systems by using the current innovation and past innovations. The simulation results show that the proposed algorithm can effectively estimate the parameters of the Hammerstein FIR-MA systems.
ISSN:0307-904X
DOI:10.1016/j.apm.2013.06.016