Open AccessHammerstein system with a stochastic input of arbitrary/unknown autocorrelation – nonparametric estimator of the static nonlinear subsystem
Author(s) -
Lin TsairChuan,
Wong Kainam Thomas
Publication year - 2021
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
eISSN - 1751-9683
pISSN - 1751-9675
DOI - 10.1049/sil2.12030
Subject(s) - estimator , pointwise , autocorrelation , nonparametric statistics , mathematics , nonlinear system , rate of convergence , minimum variance unbiased estimator , convergence (economics) , statistics , computer science , mathematical analysis , economic growth , channel (broadcasting) , physics , quantum mechanics , economics , computer network
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.