A family of normalized LMS algorithms

Author

Douglas, Scott C.

Author_Institution

Dept. of Electr. Eng., Utah Univ., Salt Lake City, UT, USA

Volume

1

Issue

3

fYear

1994

fDate

3/1/1994 12:00:00 AM

Firstpage

49

Lastpage

51

Abstract

A derivation of the normalized LMS algorithm is generalized, resulting in a family of projection-like algorithms based on an L/sub p/-minimized filter coefficient change. The resulting algorithms include the simplified NLMS algorithm of Nagumo and Noda (1967) and an even simpler single-coefficient update algorithm based on the maximum absolute value datum of the input data vector. A complete derivation of the algorithm family is given, and simulations are performed to show the convergence behaviors of the algorithms.<>

Keywords

convergence of numerical methods; filtering and prediction theory; least squares approximations; signal processing; L/sub p/-minimized filter coefficient change; algorithm family; convergence behavior; input data; least mean squares; maximum absolute value datum; normalized LMS algorithms; projection-like algorithms; simplified NLMS algorithm; single-coefficient update algorithm; Adaptive filters; Adaptive signal processing; Convergence; Finite impulse response filter; Least squares approximation; Process control; Projection algorithms; Signal processing algorithms; Statistics;

fLanguage

English

Journal_Title

Signal Processing Letters, IEEE

Publisher

ieee

ISSN

1070-9908

Type

jour

DOI

10.1109/97.295321

Filename

295321