An efficient least mean squares algorithm based on q-gradient

Ubaid M. Al-Saggaf, Muhammad Moinuddin, Azzedine Zerguine

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

9 Scopus citations

Abstract

In this work, we propose a novel LMS type algorithm by utilizing the q-gradient. The concept of q-gradient is derived from the definition of Jacksons derivative which is also called as the q-derivative. The q-gradient based LMS algorithm results in faster convergence for q > 1 because of the fact that the q-derivative, unlike the conventional derivative which evaluates tangent, computes the secant of the cost function and hence takes larger steps towards the optimum solution. We show an important application of the proposed q-LMS algorithm in which it acts like a whitening filter. Convergence analysis of the proposed algorithm is also presented. Simulation results are presented to support our theoretical findings.

Original languageEnglish
Title of host publicationConference Record of the 48th Asilomar Conference on Signals, Systems and Computers
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages891-894
Number of pages4
ISBN (Electronic)9781479982974
DOIs
StatePublished - 24 Apr 2015

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2015-April
ISSN (Print)1058-6393

Bibliographical note

Publisher Copyright:
© 2014 IEEE.

Keywords

  • Convergence analysis
  • LMS algorithm
  • q-LMS algorithm
  • q-gradient

ASJC Scopus subject areas

  • Signal Processing
  • Computer Networks and Communications

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