Open AccessSparse SM‐NLMS algorithm based on correntropy criterion
Author(s) -
Li Yingsong,
Wang Yanyan
Publication year - 2016
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2016.1944
Subject(s) - algorithm , euclidean distance , mathematics , norm (philosophy) , weight , matrix (chemical analysis) , set (abstract data type) , computer science , mathematical optimization , artificial intelligence , materials science , lie algebra , political science , pure mathematics , law , composite material , programming language
A sparse set‐membership normalised least mean square (SM‐NLMS) algorithm with a correntropy penalty is proposed and its performance is investigated for estimating a sparse echo channel. The proposed sparse SM‐NLMS algorithm is derived by minimising an unconstraint cost function that utilises the correntropy on the weight vector as well as the sum of a symmetric and positive definite matrix constrained Euclidean norm of the differences between the instantaneous error and the upper bound of the SM filtering. Simulation results over a sparse echo channel show that the proposed algorithm is superior to the existing algorithms with respect to the steady‐state misalignment.