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Sparse leaky‐LMS algorithm for system identification and its convergence analysis
Author(s) -
Salman Mohammad Shukri
Publication year - 2014
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.2428
Subject(s) - algorithm , least mean squares filter , convergence (economics) , adaptive filter , computer science , identification (biology) , system identification , stability (learning theory) , function (biology) , filter (signal processing) , rate of convergence , mathematics , key (lock) , machine learning , data mining , botany , computer security , evolutionary biology , economics , computer vision , biology , measure (data warehouse) , economic growth
SUMMARY In this paper, a novel adaptive filter for sparse systems is proposed. The proposed algorithm incorporates a log‐sum penalty into the cost function of the standard leaky least mean square (LMS) algorithm, which results in a shrinkage in the update equation. This shrinkage, in turn, enhances the performance of the adaptive filter, especially, when the majority of unknown system coefficients are zero. Convergence analysis of the proposed algorithm is presented, and a stability criterion for the algorithm is derived. This algorithm is given a name of zero‐attracting leaky‐LMS (ZA‐LLMS) algorithm. The performance of the proposed ZA‐LLMS algorithm is compared to those of the standard leaky‐LMS and ZA‐LMS algorithms in sparse system identification settings, and it shows superior performance compared to the aforementioned algorithms. Copyright © 2013 John Wiley & Sons, Ltd.