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

Author

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

Author_Institution

Dept. of Electr. Eng., Stanford Univ., CA, USA

Volume

1

fYear

1998

fDate

1-4 Nov. 1998

Firstpage

556

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

Keywords

adaptive estimation; adaptive filters; adaptive signal processing; convergence of numerical methods; filtering theory; least mean squares methods; linearisation techniques; statistical analysis; Gaussian input; IID input; LMS algorithm; adaptive filters; adaptive signal processing; arbitrary input; convergence analysis; error nonlinearity; general error nonlinearity; i.i.d. input; independent identically distributed input; least mean square algorithms; linearization approach; performance; stochastic gradient algorithms; Adaptive algorithm; Algorithm design and analysis; Convergence; Equations; Error correction; Filtering; Interference; Least squares approximation; Physics; System identification;

fLanguage

English

Publisher

ieee

Conference_Titel

Signals, Systems & Computers, 1998. Conference Record of the Thirty-Second Asilomar Conference on

Conference_Location

Pacific Grove, CA, USA

ISSN

1058-6393

Print_ISBN

0-7803-5148-7

Type

conf

DOI

10.1109/ACSSC.1998.750925

Filename

750925