A unifying approach to the derivation of the class of PNLMS algorithms

Beth Jelfs, Danilo P. Mandic, Andrzej Cichocki

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Citations (Scopus)

Abstract

A unifying approach to the derivation of the class of proportionate normalised least mean square (PNLMS) algorithms is provided. This is an important class of algorithms where the two most used algorithms are introduced empirically. It is shown that it is possible to derive PNLMS algorithms as a result of an optimisation procedure. This is achieved in a rigorous way, starting from the standard LMS through to the PNLMS with the "sparsification" factor in both the numerator and denominator of the weight update. The proposed approach is generic and also applies to other LMS types of adaptive algorithms. Simulations on benchmark sparse impulse responses support the approach.

Original languageEnglish
Title of host publication2007 15th International Conference on Digital Signal Processing, DSP 2007
Pages35-38
Number of pages4
DOIs
Publication statusPublished - 2007
Externally publishedYes
Event2007 15th International Conference onDigital Signal Processing, DSP 2007 - Wales, United Kingdom
Duration: 1 Jul 20074 Jul 2007

Publication series

Name2007 15th International Conference on Digital Signal Processing, DSP 2007

Conference

Conference2007 15th International Conference onDigital Signal Processing, DSP 2007
Country/TerritoryUnited Kingdom
CityWales
Period1/07/074/07/07

Keywords

  • LMS
  • Normalised LMS (NLMS)
  • Proportionate NLMS (PNLMS)

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