Reweighted nonnegative least-mean-square algorithm

• Published in: Signal processing Vol. 128; pp. 131 - 141
• Main Authors: Chen, Jie, Richard, Cédric, Bermudez, José Carlos M.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.11.2016; Elsevier
• Subjects: Algorithms; Behavior analysis; Computer Science; Computer simulation; Construction; Convergence; Estimates; Nonnegativity constraints; On-line systems; Online system identification; Signal and Image Processing; Sparse system identification; Strategy; Tracking; Behavior analysis; Sparse system identification; Online system identification; Nonnegativity constraints
• ISSN: 0165-1684; 1872-7557
• DOI: 10.1016/j.sigpro.2016.03.017

Statistical inference subject to nonnegativity constraints is a frequently occurring problem in learning problems. The nonnegative least-mean-square (NNLMS) algorithm was derived to address such problems in an online way. This algorithm builds on a fixed-point iteration strategy driven by the Karush–Kuhn–Tucker conditions. It was shown to provide low variance estimates, but it however suffers from unbalanced convergence rates of these estimates. In this paper, we address this problem by introducing a variant of the NNLMS algorithm. We provide a theoretical analysis of its behavior in terms of transient learning curve, steady-state and tracking performance. We also introduce an extension of the algorithm for online sparse system identification. Monte-Carlo simulations are conducted to illustrate the performance of the algorithm and to validate the theoretical results. •We proposed a variant of NN-LMS algorithm with balanced weight convergence rates.•Accurate performance analysis is performed for a general nonstationarity model.•The sparse system identification problem can be solved via the derived algorithm.