Steady-State Performance of Non-Negative Least-Mean-Square Algorithm and Its Variants

Author

Jie Chen ; Bermudez, Jose C. M. ; Richard, Cedric

Author_Institution

Lagrange Lab., Univ. of Nice Sophia-Antipolis, Nice, France

Volume

21

Issue

8

fYear

2014

fDate

Aug. 2014

Firstpage

928

Lastpage

932

Abstract

The Non-Negative Least-Mean-Square (NNLMS) algorithm and its variants have been proposed for online estimation under non-negativity constraints. The transient behavior of the NNLMS, Normalized NNLMS, Exponential NNLMS and Sign-Sign NNLMS algorithms have been studied in the literature. In this letter, we derive closed-form expressions for the steady-state excess mean-square error (EMSE) for the four algorithms. Simulation results illustrate the accuracy of the theoretical results. This work complements the understanding of the behavior of these algorithms.

Keywords

deconvolution; least mean squares methods; EMSE; Sign-Sign NNLMS algorithms; closed-form expressions; nonnegative least-mean-square algorithm; nonnegativity constraints; online estimation; steady-state excess mean-square error; steady-state performance; transient behavior; Algorithm design and analysis; Artificial neural networks; Equations; Mathematical model; Signal processing algorithms; Steady-state; Vectors; Non-negative LMS; excess mean-square error; steady-state performance; stochastic behavior;

fLanguage

English

Journal_Title

Signal Processing Letters, IEEE

Publisher

ieee

ISSN

1070-9908

Type

jour

DOI

10.1109/LSP.2014.2320944

Filename

6807652