Hammerstein system with a stochastic input of arbitrary/unknown autocorrelation – nonparametric estimator of the static nonlinear subsystem

Autor:	Tsair‐Chuan Lin, Kainam Thomas Wong
Jazyk:	angličtina
Rok vydání:	2021
Předmět:	convergence estimation theory identification nonlinear systems nonparametric statistics stochastic processes Telecommunication TK5101-6720
Zdroj:	IET Signal Processing, Vol 15, Iss 5, Pp 301-313 (2021)
Druh dokumentu:	article
ISSN:	1751-9683 1751-9675
DOI:	10.1049/sil2.12030
Popis:	Abstract This study proposes the first estimator in the open literature (to the present authors' best knowledge) to nonparametrically estimate a Hammerstein system's nonlinear static subsystem when excited by an input that is temporally self‐correlated with an unknown spectrum, an unknown variance and an unknown mean (instead of the input as commonly presumed to be white and zero‐mean). This proposed nonparametric estimator is analytically proved here to be asymptotically unbiased and pointwise consistent. The proposed estimate's associated finite‐sample convergence rate is also derived analytically.
Databáze:	Directory of Open Access Journals
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