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Conference Paper: Convergence behaviors of the fast LMM/Newton algorithm with Gaussian inputs and contaminated Gaussian noise

TitleConvergence behaviors of the fast LMM/Newton algorithm with Gaussian inputs and contaminated Gaussian noise
Authors
Issue Date2009
Citation
Proceedings - Ieee International Symposium On Circuits And Systems, 2009, p. 2573-2576 How to Cite?
AbstractThis paper studies the convergence behaviors of the fast least mean M-estimate/Newton adaptive filtering algorithm proposed in [4], which is based on the fast LMS/Newton principle and the minimization of an M-estimate function using robust statistics for robust filtering in impulsive noise. By using the Price's theorem and its extension for contaminated Gaussian (CG) noise case, the convergence behaviors of the fast LMM/Newton algorithm with Gaussian inputs and both Gaussian and CG noises are analyzed. Difference equations describing the mean and mean square behaviors of this algorithm and step size bound for ensuring stability are derived. These analytical results reveal the advantages of the fast LMM/Newton algorithm in combating impulsive noise, and they are in good agreement with computer simulation results. ©2009 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/158598
ISSN
References

 

DC FieldValueLanguage
dc.contributor.authorChan, SCen_HK
dc.contributor.authorZhou, Yen_HK
dc.date.accessioned2012-08-08T09:00:24Z-
dc.date.available2012-08-08T09:00:24Z-
dc.date.issued2009en_HK
dc.identifier.citationProceedings - Ieee International Symposium On Circuits And Systems, 2009, p. 2573-2576en_US
dc.identifier.issn0271-4310en_HK
dc.identifier.urihttp://hdl.handle.net/10722/158598-
dc.description.abstractThis paper studies the convergence behaviors of the fast least mean M-estimate/Newton adaptive filtering algorithm proposed in [4], which is based on the fast LMS/Newton principle and the minimization of an M-estimate function using robust statistics for robust filtering in impulsive noise. By using the Price's theorem and its extension for contaminated Gaussian (CG) noise case, the convergence behaviors of the fast LMM/Newton algorithm with Gaussian inputs and both Gaussian and CG noises are analyzed. Difference equations describing the mean and mean square behaviors of this algorithm and step size bound for ensuring stability are derived. These analytical results reveal the advantages of the fast LMM/Newton algorithm in combating impulsive noise, and they are in good agreement with computer simulation results. ©2009 IEEE.en_HK
dc.languageengen_US
dc.relation.ispartofProceedings - IEEE International Symposium on Circuits and Systemsen_HK
dc.titleConvergence behaviors of the fast LMM/Newton algorithm with Gaussian inputs and contaminated Gaussian noiseen_HK
dc.typeConference_Paperen_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.naturelink_to_subscribed_fulltexten_US
dc.identifier.doi10.1109/ISCAS.2009.5118327en_HK
dc.identifier.scopuseid_2-s2.0-70350140454en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-70350140454&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.spage2573en_HK
dc.identifier.epage2576en_HK
dc.identifier.scopusauthoridChan, SC=13310287100en_HK
dc.identifier.scopusauthoridZhou, Y=55209555200en_HK

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