Generalized correntropy induced metric based total least squares for sparse system identification

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

Generalized correntropy induced metric based total least squares for sparse system identification

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

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Publisher:: ELSEVIER
Publication Type:: Journal Article
Citation:: Neurocomputing, 2022, 467, pp. 66-72
Issue Date:: 2022-01-07

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Field	Value	Language
dc.contributor.author	Zhao, J
dc.contributor.author	Zhang, JA
dc.contributor.author	Zhang, H
dc.contributor.author	Li, Q
dc.date.accessioned	2022-01-28T02:53:12Z
dc.date.available	2022-01-28T02:53:12Z
dc.date.issued	2022-01-07
dc.identifier.citation	Neurocomputing, 2022, 467, pp. 66-72
dc.identifier.issn	0925-2312
dc.identifier.issn	1872-8286
dc.identifier.uri	http://hdl.handle.net/10453/153729
dc.description.abstract	The total least squares (TLS) method has been successfully applied to system identification in the errors-in-variables (EIV) model, which can efficiently describe systems where input–output pairs are contaminated by noise. In this paper, we propose a new gradient-descent TLS filtering algorithm based on the generalized correntropy induced metric (GCIM), called as GCIM-TLS, for sparse system identification. By introducing GCIM as a penalty term to the TLS problem, we can achieve improved accuracy of sparse system identification. We also characterize the convergence behaviour analytically for GCIM-TLS. To reduce computational complexity, we use the first-order Taylor series expansion and further derive a simplified version of GCIM-TLS. Simulation results verify the effectiveness of our proposed algorithms in sparse system identification.
dc.language	English
dc.publisher	ELSEVIER
dc.relation.ispartof	Neurocomputing
dc.relation.isbasedon	10.1016/j.neucom.2021.09.049
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	08 Information and Computing Sciences, 09 Engineering, 17 Psychology and Cognitive Sciences
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Generalized correntropy induced metric based total least squares for sparse system identification
dc.type	Journal Article
utslib.citation.volume	467
utslib.for	08 Information and Computing Sciences
utslib.for	09 Engineering
utslib.for	17 Psychology and Cognitive Sciences
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	*
dc.date.updated	2022-01-28T02:53:10Z
pubs.publication-status	Published
pubs.volume	467

Abstract:

The total least squares (TLS) method has been successfully applied to system identification in the errors-in-variables (EIV) model, which can efficiently describe systems where input–output pairs are contaminated by noise. In this paper, we propose a new gradient-descent TLS filtering algorithm based on the generalized correntropy induced metric (GCIM), called as GCIM-TLS, for sparse system identification. By introducing GCIM as a penalty term to the TLS problem, we can achieve improved accuracy of sparse system identification. We also characterize the convergence behaviour analytically for GCIM-TLS. To reduce computational complexity, we use the first-order Taylor series expansion and further derive a simplified version of GCIM-TLS. Simulation results verify the effectiveness of our proposed algorithms in sparse system identification.

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http://hdl.handle.net/10453/153729