Convergence analysis of the LMS algorithm with a general error nonlinearity and an IID input

Tareq Y. Al-Naffouri*, Azzedine Zerguine, Maamar Bettayeb

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

The class of least mean square (LMS) algorithms employing a general error nonlinearity is considered. A linearization approach is used to characterize the convergence and performance of this class of algorithms for an independent and identically distributed (iid) input. The analysis results are entirely consistent with those of the LMS algorithm and several of its variants. The results also encompass those of a recent work that considered the same class of algorithms for arbitrary and Gaussian inputs.

Original languageEnglish (US)
Pages (from-to)556-559
Number of pages4
JournalConference Record of the Asilomar Conference on Signals, Systems and Computers
Volume1
StatePublished - 1998
Externally publishedYes

ASJC Scopus subject areas

  • Hardware and Architecture
  • Signal Processing
  • Electrical and Electronic Engineering

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