Gaussian kernel adaptive filters with adaptive kernel bandwidth

Zhao, J; Zhang, H; Zhang, JA

Gaussian kernel adaptive filters with adaptive kernel bandwidth

Zhao, J Zhang, H Zhang, JA

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Signal Processing, 2020, 166
Issue Date:: 2020-01-01

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Field	Value	Language
dc.contributor.author	Zhao, J
dc.contributor.author	Zhang, H
dc.contributor.author	Zhang, JA
dc.date.accessioned	2021-01-10T05:23:17Z
dc.date.available	2021-01-10T05:23:17Z
dc.date.issued	2020-01-01
dc.identifier.citation	Signal Processing, 2020, 166
dc.identifier.issn	0165-1684
dc.identifier.issn	1872-7557
dc.identifier.uri	http://hdl.handle.net/10453/145247
dc.description.abstract	© 2019 Gaussian kernel adaptive filters (GKAFs) have been successfully applied in functional approximation. The kernel bandwidth for GKAFs not only impacts on the smoothness of function approximation and the locality of training samples, but also affects the convergence rate and testing accuracy. However, in practice, it is hard to predesign an optimal one. In this paper, for practical applications, we propose a novel framework for kernel bandwidth adaptation in sparsification case. In this framework, we consider the latest K kernel bandwidths as free parameters, and sequentially update them using a gradient decent method to minimize the instantaneous squared error. Furthermore, we apply the proposed method to the quantized kernel least mean square (QKLMS) algorithm, and conduct convergence analysis for the algorithm. Extensive simulation results are provided and validate the superiority of our method compared to some state-of-the-art algorithms.
dc.language	English
dc.publisher	ELSEVIER
dc.relation.ispartof	Signal Processing
dc.relation.isbasedon	10.1016/j.sigpro.2019.107270
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 10 Technology
dc.subject.classification	Networking & Telecommunications
dc.title	Gaussian kernel adaptive filters with adaptive kernel bandwidth
dc.type	Journal Article
utslib.citation.volume	166
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	10 Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
utslib.copyright.status	closed_access	*
dc.date.updated	2021-01-10T05:22:56Z
pubs.publication-status	Published
pubs.volume	166

Abstract:

© 2019 Gaussian kernel adaptive filters (GKAFs) have been successfully applied in functional approximation. The kernel bandwidth for GKAFs not only impacts on the smoothness of function approximation and the locality of training samples, but also affects the convergence rate and testing accuracy. However, in practice, it is hard to predesign an optimal one. In this paper, for practical applications, we propose a novel framework for kernel bandwidth adaptation in sparsification case. In this framework, we consider the latest K kernel bandwidths as free parameters, and sequentially update them using a gradient decent method to minimize the instantaneous squared error. Furthermore, we apply the proposed method to the quantized kernel least mean square (QKLMS) algorithm, and conduct convergence analysis for the algorithm. Extensive simulation results are provided and validate the superiority of our method compared to some state-of-the-art algorithms.

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http://hdl.handle.net/10453/145247