Nonnegative Least-Mean-Square Algorithm

Author

Chen, Jie ; Richard, Cédric ; Bermudez, José Carlos M ; Honeine, Paul

Author_Institution

CNRS, Univ. of Technol. of Troyes, Troyes, France

Volume

59

Issue

11

fYear

2011

Firstpage

5225

Lastpage

5235

Abstract

Dynamic system modeling plays a crucial role in the development of techniques for stationary and nonstationary signal processing. Due to the inherent physical characteristics of systems under investigation, nonnegativity is a desired constraint that can usually be imposed on the parameters to estimate. In this paper, we propose a general method for system identification under nonnegativity constraints. We derive the so-called nonnegative least-mean-square algorithm (NNLMS) based on stochastic gradient descent, and we analyze its convergence. Experiments are conducted to illustrate the performance of this approach and consistency with the analysis.

Keywords

gradient methods; least mean squares methods; parameter estimation; signal processing; dynamic system modeling; nonnegative least-mean-square algorithm; nonnegativity constraint; nonstationary signal processing; parameter estimation; stochastic gradient descent; system identification; Algorithm design and analysis; Convergence; Equations; Facsimile; Least squares approximation; Mathematical model; Prediction algorithms; Adaptive filters; adaptive signal processing; least mean square algorithms; nonnegative constraints; transient analysis;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/TSP.2011.2162508

Filename

5958632