Convergence and tracking analysis of the ε-NSRLMF algorithm

Mohammed Mujahid Ulla Faiz, Azzedine Zerguine

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

9 Scopus citations

Abstract

In this work, the convergence and tracking behavior of the ε-normalized sign regressor least mean fourth (NSRLMF) algorithm are analyzed in the presence of white and correlated Gaussian data. Furthermore, the stability bound on the step-size of the ε-NSRLMF algorithm to ensure convergence in the mean, which also leads us to the mean convergence of the ε-normalized sign regressor least mean mixed-norm (NSRLMMN) algorithm is derived. Finally, simulation results are conducted to confirm the validity and performance of the proposed adaptive algorithm for both white and correlated Gaussian regressors.

Original languageEnglish
Title of host publication2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
Pages5657-5660
Number of pages4
DOIs
StatePublished - 18 Oct 2013

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Keywords

  • Convergence
  • LMF
  • NLMF
  • NSRLMF
  • SRLMF
  • Tracking

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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