On the normalization of the step size in nonsymmetric stochastic gradient algorithms

Author

Slock, Dirk T M

Author_Institution

Eurecom Inst., Valbonne, France

fYear

1992

Firstpage

876

Abstract

The normalization of the step size in the least-mean-square (LMS) algorithm allows easy control of the range of stable operation for the normalized step size in the normalized LMS (NLMS) algorithm, and also easy determination of the step size for maximum convergence speed. A generalized step size normalization for stochastic gradient algorithms in which the gradient vector differs from the data vector is proposed. Three applications are considered in detail: the stochastic Newton scheme, the sign-data LMS algorithm, and an instrumental variable method proposed to speed up the convergence of the LMS algorithm

Keywords

adaptive filters; convergence; digital filters; filtering and prediction theory; least squares approximations; stochastic processes; FIR filters; least mean square algorithm; maximum convergence speed; nonsymmetric stochastic gradient algorithms; stable operation; step size normalisation; stochastic Newton scheme; Algorithm design and analysis; Convergence; Eigenvalues and eigenfunctions; Equations; Finite impulse response filter; Instruments; Least squares approximation; Size control; Statistics; Stochastic processes;

fLanguage

English

Publisher

ieee

Conference_Titel

Signals, Systems and Computers, 1992. 1992 Conference Record of The Twenty-Sixth Asilomar Conference on

Conference_Location

Pacific Grove, CA

ISSN

1058-6393

Print_ISBN

0-8186-3160-0

Type

conf

DOI

10.1109/ACSSC.1992.269147

Filename

269147