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Entropy 2017, 19(1), 45; doi:10.3390/e19010045

A Soft Parameter Function Penalized Normalized Maximum Correntropy Criterion Algorithm for Sparse System Identification

1
College of Information and Communications Engineering, Harbin Engineering University, Harbin 150001, China
2
College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
3
Department of Electronics, Valahia University of Targoviste, Targoviste 130082, Romania
*
Author to whom correspondence should be addressed.
Academic Editors: Badong Chen and Jose C. Principe
Received: 9 December 2016 / Revised: 15 January 2017 / Accepted: 20 January 2017 / Published: 23 January 2017
(This article belongs to the Special Issue Entropy in Signal Analysis)
View Full-Text   |   Download PDF [2443 KB, uploaded 23 January 2017]   |  

Abstract

A soft parameter function penalized normalized maximum correntropy criterion (SPF-NMCC) algorithm is proposed for sparse system identification. The proposed SPF-NMCC algorithm is derived on the basis of the normalized adaptive filter theory, the maximum correntropy criterion (MCC) algorithm and zero-attracting techniques. A soft parameter function is incorporated into the cost function of the traditional normalized MCC (NMCC) algorithm to exploit the sparsity properties of the sparse signals. The proposed SPF-NMCC algorithm is mathematically derived in detail. As a result, the proposed SPF-NMCC algorithm can provide an efficient zero attractor term to effectively attract the zero taps and near-zero coefficients to zero, and, hence, it can speed up the convergence. Furthermore, the estimation behaviors are obtained by estimating a sparse system and a sparse acoustic echo channel. Computer simulation results indicate that the proposed SPF-NMCC algorithm can achieve a better performance in comparison with the MCC, NMCC, LMS (least mean square) algorithms and their zero attraction forms in terms of both convergence speed and steady-state performance. View Full-Text
Keywords: adaptive filters; maximum correntropy criterion; kernel framework; sparse adaptive filtering; soft parameter function; zero attracting algorithm adaptive filters; maximum correntropy criterion; kernel framework; sparse adaptive filtering; soft parameter function; zero attracting algorithm
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Li, Y.; Wang, Y.; Yang, R.; Albu, F. A Soft Parameter Function Penalized Normalized Maximum Correntropy Criterion Algorithm for Sparse System Identification. Entropy 2017, 19, 45.

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