A kernel recursive minimum error entropy adaptive filter

The minimum error entropy, a currently useful alternative criterion, is widely adopted in the signal processing domain against impulsive noise. In this brief, we propose a novel algorithm to blend the advantages of both the kernel recursive least squares algorithm and the minimum error entropy crite...

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Vydáno v:	Signal processing Ročník 193; s. 108410
Hlavní autoři:	Wang, Gang, Yang, Xinyue, Wu, Lei, Fu, Zhenting, Ma, Xiangjie, He, Yuanhang, Peng, Bei
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Elsevier B.V 01.04.2022
Témata:	Kernel adaptive filter (KAF) Kernel recursive minimum error entropy (KRMEE) Minimum error entropy (MEE) Kernel adaptive filter (KAF) Kernel recursive minimum error entropy (KRMEE) Minimum error entropy (MEE)
ISSN:	0165-1684, 1872-7557
On-line přístup:	Získat plný text
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Popis
Shrnutí:	The minimum error entropy, a currently useful alternative criterion, is widely adopted in the signal processing domain against impulsive noise. In this brief, we propose a novel algorithm to blend the advantages of both the kernel recursive least squares algorithm and the minimum error entropy criterion, called kernel recursive minimum error entropy algorithm. The proposed new algorithm achieves better recovery performance in predicting the Mackey–Glass time series, equalizing the nonlinear channel under heavy tailed alpha-stable environments and processing EEG data.
ISSN:	0165-1684 1872-7557
DOI:	10.1016/j.sigpro.2021.108410