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Article: A new diffusion variable spatial regularized LMS algorithm

TitleA new diffusion variable spatial regularized LMS algorithm
Authors
KeywordsDiffusion LMS algorithm
Variable spatial regularization
Performance analysis
Issue Date2021
PublisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/sigpro
Citation
Signal Processing, 2021, v. 188, article no. 108207 How to Cite?
AbstractThis paper develops a new diffusion (Diff) least mean squares (LMS) algorithm for the identification of a network of systems that have distinct parameters at each node. The mean and mean squares behavior of the Diff-LMS algorithm in the so called multitask environment is studied in order to obtain an explicit expression of the estimation bias and variance in terms of the spatial regularization (SR) parameter. An optimal SR formula for the Diff LMS algorithm is then derived via minimizing the estimation error. An approximation is made to the formula such that a new practical Diff variable SR LMS (Diff-VSR-LMS) algorithm is obtained. This paper also provides a framework for the design of other LMS-like algorithms that incorporate diffusion technology to solve multitask problems. The theoretical analysis is evaluated via computer simulations and the performance of the proposed algorithm is compared with conventional Diff LMS algorithms under the multitask environment.
Persistent Identifierhttp://hdl.handle.net/10722/307668
ISSN
2023 Impact Factor: 3.4
2023 SCImago Journal Rankings: 1.065
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorChu, YJ-
dc.contributor.authorChan, SC-
dc.contributor.authorZhou, Y-
dc.contributor.authorWu, M-
dc.date.accessioned2021-11-12T13:36:04Z-
dc.date.available2021-11-12T13:36:04Z-
dc.date.issued2021-
dc.identifier.citationSignal Processing, 2021, v. 188, article no. 108207-
dc.identifier.issn0165-1684-
dc.identifier.urihttp://hdl.handle.net/10722/307668-
dc.description.abstractThis paper develops a new diffusion (Diff) least mean squares (LMS) algorithm for the identification of a network of systems that have distinct parameters at each node. The mean and mean squares behavior of the Diff-LMS algorithm in the so called multitask environment is studied in order to obtain an explicit expression of the estimation bias and variance in terms of the spatial regularization (SR) parameter. An optimal SR formula for the Diff LMS algorithm is then derived via minimizing the estimation error. An approximation is made to the formula such that a new practical Diff variable SR LMS (Diff-VSR-LMS) algorithm is obtained. This paper also provides a framework for the design of other LMS-like algorithms that incorporate diffusion technology to solve multitask problems. The theoretical analysis is evaluated via computer simulations and the performance of the proposed algorithm is compared with conventional Diff LMS algorithms under the multitask environment.-
dc.languageeng-
dc.publisherElsevier BV. The Journal's web site is located at http://www.elsevier.com/locate/sigpro-
dc.relation.ispartofSignal Processing-
dc.subjectDiffusion LMS algorithm-
dc.subjectVariable spatial regularization-
dc.subjectPerformance analysis-
dc.titleA new diffusion variable spatial regularized LMS algorithm-
dc.typeArticle-
dc.identifier.emailChan, SC: scchan@eee.hku.hk-
dc.identifier.authorityChan, SC=rp00094-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.sigpro.2021.108207-
dc.identifier.scopuseid_2-s2.0-85108976193-
dc.identifier.hkuros329440-
dc.identifier.volume188-
dc.identifier.spagearticle no. 108207-
dc.identifier.epagearticle no. 108207-
dc.identifier.isiWOS:000684282300015-
dc.publisher.placeNetherlands-

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