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- Publisher Website: 10.1109/ISSPIT.2007.4458106
- Scopus: eid_2-s2.0-71549148115
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Conference Paper: On the convergence analysis of the normalized LMS and the normalized least mean M-estimate algorithms
Title | On the convergence analysis of the normalized LMS and the normalized least mean M-estimate algorithms |
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Authors | |
Keywords | Adaptive filtering Impulsive noise Least mean square/M-estimate |
Issue Date | 2007 |
Citation | Isspit 2007 - 2007 Ieee International Symposium On Signal Processing And Information Technology, 2007, p. 1048-1053 How to Cite? |
Abstract | This paper studies the convergence behaviors of the normalized least mean square (NLMS) and the normalized least mean M-estimate (NLMM) algorithms. Our analysis is obtained by extending the framework of Bershad [6], [7], which were previously reported for the NLMS algorithm with Gaussian inputs. Due to the difficulties in evaluating certain expectations involved, in [6], [7] the behaviors of the NLMS algorithm for general eigenvalue distributions of input autocorrelation matrix were not fully analyzed. In this paper, using an extension of Price's theorem to mixture Gaussian distributions and by introducing certain special integral functions, closed-form results of these expectations are obtained which allow us to interpret the convergence performance of both the NLMS and the NLMM algorithms in Contaminated Gaussian noise. The validity of the proposed analysis is verified through computer simulations. ©2007 IEEE. |
Persistent Identifier | http://hdl.handle.net/10722/158610 |
References |
DC Field | Value | Language |
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dc.contributor.author | Chan, SC | en_HK |
dc.contributor.author | Zhou, Y | en_HK |
dc.date.accessioned | 2012-08-08T09:00:28Z | - |
dc.date.available | 2012-08-08T09:00:28Z | - |
dc.date.issued | 2007 | en_HK |
dc.identifier.citation | Isspit 2007 - 2007 Ieee International Symposium On Signal Processing And Information Technology, 2007, p. 1048-1053 | en_US |
dc.identifier.uri | http://hdl.handle.net/10722/158610 | - |
dc.description.abstract | This paper studies the convergence behaviors of the normalized least mean square (NLMS) and the normalized least mean M-estimate (NLMM) algorithms. Our analysis is obtained by extending the framework of Bershad [6], [7], which were previously reported for the NLMS algorithm with Gaussian inputs. Due to the difficulties in evaluating certain expectations involved, in [6], [7] the behaviors of the NLMS algorithm for general eigenvalue distributions of input autocorrelation matrix were not fully analyzed. In this paper, using an extension of Price's theorem to mixture Gaussian distributions and by introducing certain special integral functions, closed-form results of these expectations are obtained which allow us to interpret the convergence performance of both the NLMS and the NLMM algorithms in Contaminated Gaussian noise. The validity of the proposed analysis is verified through computer simulations. ©2007 IEEE. | en_HK |
dc.language | eng | en_US |
dc.relation.ispartof | ISSPIT 2007 - 2007 IEEE International Symposium on Signal Processing and Information Technology | en_HK |
dc.subject | Adaptive filtering | en_HK |
dc.subject | Impulsive noise | en_HK |
dc.subject | Least mean square/M-estimate | en_HK |
dc.title | On the convergence analysis of the normalized LMS and the normalized least mean M-estimate algorithms | en_HK |
dc.type | Conference_Paper | en_HK |
dc.identifier.email | Chan, SC: ascchan@hkucc.hku.hk | en_HK |
dc.identifier.email | Zhou, Y: yizhou@eee.hku.hk | en_HK |
dc.identifier.authority | Chan, SC=rp00094 | en_HK |
dc.identifier.authority | Zhou, Y=rp00213 | en_HK |
dc.description.nature | link_to_subscribed_fulltext | en_US |
dc.identifier.doi | 10.1109/ISSPIT.2007.4458106 | en_HK |
dc.identifier.scopus | eid_2-s2.0-71549148115 | en_HK |
dc.relation.references | http://www.scopus.com/mlt/select.url?eid=2-s2.0-71549148115&selection=ref&src=s&origin=recordpage | en_HK |
dc.identifier.spage | 1048 | en_HK |
dc.identifier.epage | 1053 | en_HK |
dc.identifier.scopusauthorid | Chan, SC=13310287100 | en_HK |
dc.identifier.scopusauthorid | Zhou, Y=55209555200 | en_HK |