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

T.Y. Al-Naffouri; A. Zerguine; M. Bettayeb

doi:10.1109/ACSSC.1998.750925

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

T.Y. Al-Naffouri, A. Zerguine and M. Bettayeb

Conference Record of Thirty-Second Asilomar Conference on Signals, Systems and Computers (Cat. No.98CH36284), Vol.1, pp.556-559 vol.1

1998

DOI: https://doi.org/10.1109/ACSSC.1998.750925

Abstract

Adaptive algorithm

Algorithm design and analysis

Convergence

Equations

Error correction

Filtering

Interference

Least squares approximation

Physics

System identification

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 previous work that considered the same class of algorithms for arbitrary and Gaussian inputs.

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Details

Title: Convergence analysis of the LMS algorithm with a general error nonlinearity and an IID input
Creators - without role: T.Y. Al-Naffouri - Stanford University
A. Zerguine
M. Bettayeb
Publication Details: Conference Record of Thirty-Second Asilomar Conference on Signals, Systems and Computers (Cat. No.98CH36284), Vol.1, pp.556-559 vol.1
Publisher: IEEE
Identifiers: 9934979108331
Academic Unit: King Abdulaziz University
Language: English
Resource Type: Conference proceeding