Title :
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
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;
Journal_Title :
Signal Processing, IEEE Transactions on
DOI :
10.1109/TSP.2011.2162508
