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Article: Convergence behavior of NLMS algorithm for Gaussian inputs: Solutions using generalized Abelian integral functions and step size selection

TitleConvergence behavior of NLMS algorithm for Gaussian inputs: Solutions using generalized Abelian integral functions and step size selection
Authors
KeywordsConvergence
Normalized least mean square
Issue Date2010
PublisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/content/120889/
Citation
Journal Of Signal Processing Systems, 2010, v. 59 n. 3, p. 255-265 How to Cite?
AbstractThis paper studies the mean and mean square convergence behaviors of the normalized least mean square (NLMS) algorithm with Gaussian inputs and additive white Gaussian noise. Using the Price's theorem and the framework proposed by Bershad in IEEE Transactions on Acoustics, Speech, and Signal Processing (1986, 1987), new expressions for the excess mean square error, stability bound and decoupled difference equations describing the mean and mean square convergence behaviors of the NLMS algorithm using the generalized Abelian integral functions are derived. These new expressions which closely resemble those of the LMS algorithm allow us to interpret the convergence performance of the NLMS algorithm in Gaussian environment. The theoretical analysis is in good agreement with the computer simulation results and it also gives new insight into step size selection. © 2009 Springer Science+Business Media, LLC.
Persistent Identifierhttp://hdl.handle.net/10722/124016
ISSN
2015 Impact Factor: 0.508
2015 SCImago Journal Rankings: 0.262
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorChan, SCen_HK
dc.contributor.authorZhou, Yen_HK
dc.date.accessioned2010-10-19T04:33:25Z-
dc.date.available2010-10-19T04:33:25Z-
dc.date.issued2010en_HK
dc.identifier.citationJournal Of Signal Processing Systems, 2010, v. 59 n. 3, p. 255-265en_HK
dc.identifier.issn1939-8018en_HK
dc.identifier.urihttp://hdl.handle.net/10722/124016-
dc.description.abstractThis paper studies the mean and mean square convergence behaviors of the normalized least mean square (NLMS) algorithm with Gaussian inputs and additive white Gaussian noise. Using the Price's theorem and the framework proposed by Bershad in IEEE Transactions on Acoustics, Speech, and Signal Processing (1986, 1987), new expressions for the excess mean square error, stability bound and decoupled difference equations describing the mean and mean square convergence behaviors of the NLMS algorithm using the generalized Abelian integral functions are derived. These new expressions which closely resemble those of the LMS algorithm allow us to interpret the convergence performance of the NLMS algorithm in Gaussian environment. The theoretical analysis is in good agreement with the computer simulation results and it also gives new insight into step size selection. © 2009 Springer Science+Business Media, LLC.en_HK
dc.languageengen_HK
dc.publisherSpringer New York LLC. The Journal's web site is located at http://springerlink.metapress.com/content/120889/en_HK
dc.relation.ispartofJournal of Signal Processing Systemsen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rightsSpringer Science+Business Media, LLCen_HK
dc.subjectConvergenceen_HK
dc.subjectNormalized least mean squareen_HK
dc.titleConvergence behavior of NLMS algorithm for Gaussian inputs: Solutions using generalized Abelian integral functions and step size selectionen_HK
dc.typeArticleen_HK
dc.identifier.emailChan, SC: ascchan@hkucc.hku.hken_HK
dc.identifier.emailZhou, Y: yizhou@eee.hku.hken_HK
dc.identifier.authorityChan, SC=rp00094en_HK
dc.identifier.authorityZhou, Y=rp00213en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1007/s11265-009-0385-9en_HK
dc.identifier.scopuseid_2-s2.0-77951256551en_HK
dc.identifier.hkuros165831-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-77951256551&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume59en_HK
dc.identifier.issue3en_HK
dc.identifier.spage255en_HK
dc.identifier.epage265en_HK
dc.identifier.eissn1939-8115en_HK
dc.identifier.isiWOS:000276185000003-
dc.publisher.placeUnited Statesen_HK
dc.description.otherSpringer Open Choice, 01 Dec 2010-
dc.identifier.scopusauthoridChan, SC=13310287100en_HK
dc.identifier.scopusauthoridZhou, Y=55209555200en_HK
dc.identifier.citeulike5286280-

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