Recursive Maximum Correntropy Algorithms for Second-Order Volterra Filtering

Zhao, J; Zhang, JA; Li, Q; Zhang, H; Wang, X

Recursive Maximum Correntropy Algorithms for Second-Order Volterra Filtering

Zhao, J Zhang, JA Li, Q Zhang, H Wang, X

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Transactions on Circuits and Systems II: Express Briefs, 2021, PP, (99), pp. 1-1
Issue Date:: 2021-01-01

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Field	Value	Language
dc.contributor.author	Zhao, J
dc.contributor.author	Zhang, JA
dc.contributor.author	Li, Q
dc.contributor.author	Zhang, H
dc.contributor.author	Wang, X
dc.date.accessioned	2022-01-28T02:56:10Z
dc.date.available	2022-01-28T02:56:10Z
dc.date.issued	2021-01-01
dc.identifier.citation	IEEE Transactions on Circuits and Systems II: Express Briefs, 2021, PP, (99), pp. 1-1
dc.identifier.issn	1549-7747
dc.identifier.issn	1558-3791
dc.identifier.uri	http://hdl.handle.net/10453/153734
dc.description.abstract	As a special case of the Volterra system, the second-order Volterra (SOV) filter is very efficient for nonlinear system identification. The improved correntorpy based on the generalized Gaussian density function has been proven robust against impulsive noise. In this brief, we propose several SOV filters based on a recursive maximum correntropy (RMC) algorithm for nonlinear system identification. We first introduce a basic RMC algorithm, which faces a trade-off between filtering accuracy and tracking capability due to the use of a fixed forgetting factor (FFF). Two RMCs with variable FF (VFF) are further proposed to enhance the tracking ability. Simulation results demonstrate that our proposed algorithms outperform existing ones in impulsive noise environments and/or in time-varying systems.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Transactions on Circuits and Systems II: Express Briefs
dc.relation.isbasedon	10.1109/TCSII.2021.3064946
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.subject	0906 Electrical and Electronic Engineering
dc.subject.classification	Electrical & Electronic Engineering
dc.title	Recursive Maximum Correntropy Algorithms for Second-Order Volterra Filtering
dc.type	Journal Article
utslib.citation.volume	PP
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2023-03-31T00:00:00+1000Z
dc.date.updated	2022-01-28T02:56:09Z
pubs.issue	99
pubs.publication-status	Published
pubs.volume	PP
utslib.citation.issue	99

Abstract:

As a special case of the Volterra system, the second-order Volterra (SOV) filter is very efficient for nonlinear system identification. The improved correntorpy based on the generalized Gaussian density function has been proven robust against impulsive noise. In this brief, we propose several SOV filters based on a recursive maximum correntropy (RMC) algorithm for nonlinear system identification. We first introduce a basic RMC algorithm, which faces a trade-off between filtering accuracy and tracking capability due to the use of a fixed forgetting factor (FFF). Two RMCs with variable FF (VFF) are further proposed to enhance the tracking ability. Simulation results demonstrate that our proposed algorithms outperform existing ones in impulsive noise environments and/or in time-varying systems.

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http://hdl.handle.net/10453/153734