File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Conference Paper: A new proportionate fast LMS/Newton algorithm for adaptive filtering

TitleA new proportionate fast LMS/Newton algorithm for adaptive filtering
Authors
Zhou, YChan, SCHo, KL
Issue Date2005
PublisherIEEE.
Citation
The 13th IEEE / SP Workshop on Statistical Signal Processing, Bordeaux, France, 17-20 July 2005. In Conference Proceedings, 2005, p. 115-120 How to Cite?
DOI: http://dx.doi.org/10.1109/SSP.2005.1628575
AbstractThis paper proposes a new proportionate adaptive filtering algorithm which exploits the advantageous features of the generalized proportionate NLMS (GP-NLMS) algorithm and the fast LMS/Newton algorithm. By means of an efficient switching mechanism, the new algorithm works alternately between the GP-NLMS and the fast LMS/Newton algorithms in order to combine their respective advantages. The overall converging speed and steady state performance for both sparse and dispersive channels as well as tracking performance are thus significantly improved. Computer simulations on an echo cancellation problem verify the superior performance of the new algorithm over both the GP-NLMS algorithm and the conventional fast LMS/Newton algorithm. ©2005 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/45922
References
References in Scopus

 

DC FieldValueLanguage
dc.contributor.authorZhou, Yen_HK
dc.contributor.authorChan, SCen_HK
dc.contributor.authorHo, KLen_HK
dc.date.accessioned2007-10-30T06:38:32Z-
dc.date.available2007-10-30T06:38:32Z-
dc.date.issued2005en_HK
dc.identifier.citationThe 13th IEEE / SP Workshop on Statistical Signal Processing, Bordeaux, France, 17-20 July 2005. In Conference Proceedings, 2005, p. 115-120en_HK
dc.identifier.urihttp://hdl.handle.net/10722/45922-
dc.description.abstractThis paper proposes a new proportionate adaptive filtering algorithm which exploits the advantageous features of the generalized proportionate NLMS (GP-NLMS) algorithm and the fast LMS/Newton algorithm. By means of an efficient switching mechanism, the new algorithm works alternately between the GP-NLMS and the fast LMS/Newton algorithms in order to combine their respective advantages. The overall converging speed and steady state performance for both sparse and dispersive channels as well as tracking performance are thus significantly improved. Computer simulations on an echo cancellation problem verify the superior performance of the new algorithm over both the GP-NLMS algorithm and the conventional fast LMS/Newton algorithm. ©2005 IEEE.en_HK
dc.languageengen_HK
dc.publisherIEEE.en_HK
dc.relation.ispartofIEEE/SP Workshop on Statistical Signal Processing (SSP)en_HK
dc.rights©2005 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.titleA new proportionate fast LMS/Newton algorithm for adaptive filteringen_HK
dc.typeConference_Paperen_HK
dc.identifier.emailZhou, Y: yizhou@eee.hku.hken_HK
dc.identifier.emailChan, SC: ascchan@hkucc.hku.hken_HK
dc.identifier.emailHo, KL: klho@eee.hku.hken_HK
dc.identifier.authorityZhou, Y=rp00213en_HK
dc.identifier.authorityChan, SC=rp00094en_HK
dc.identifier.authorityHo, KL=rp00117en_HK
dc.description.naturepublished_or_final_versionen_HK
dc.identifier.doi10.1109/SSP.2005.1628575-
dc.identifier.scopuseid_2-s2.0-33947109005en_HK
dc.identifier.hkuros121314-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-33947109005&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume2005en_HK
dc.identifier.spage115en_HK
dc.identifier.epage120en_HK
dc.identifier.scopusauthoridZhou, Y=55209555200en_HK
dc.identifier.scopusauthoridChan, SC=13310287100en_HK
dc.identifier.scopusauthoridHo, KL=7403581592en_HK
dc.customcontrol.immutablesml 151023 - merged-

Export via OAI-PMH Interface in XML Formats

OR

Export to Other Non-XML Formats