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Proportionate Minimum Error Entropy Algorithm for Sparse System Identification

School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510640, China
School of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China
School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China
Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USA
Author to whom correspondence should be addressed.
Academic Editor: Kevin H. Knuth
Entropy 2015, 17(9), 5995-6006;
Received: 22 May 2015 / Revised: 17 August 2015 / Accepted: 25 August 2015 / Published: 27 August 2015
PDF [798 KB, uploaded 27 August 2015]


Sparse system identification has received a great deal of attention due to its broad applicability. The proportionate normalized least mean square (PNLMS) algorithm, as a popular tool, achieves excellent performance for sparse system identification. In previous studies, most of the cost functions used in proportionate-type sparse adaptive algorithms are based on the mean square error (MSE) criterion, which is optimal only when the measurement noise is Gaussian. However, this condition does not hold in most real-world environments. In this work, we use the minimum error entropy (MEE) criterion, an alternative to the conventional MSE criterion, to develop the proportionate minimum error entropy (PMEE) algorithm for sparse system identification, which may achieve much better performance than the MSE based methods especially in heavy-tailed non-Gaussian situations. Moreover, we analyze the convergence of the proposed algorithm and derive a sufficient condition that ensures the mean square convergence. Simulation results confirm the excellent performance of the new algorithm. View Full-Text
Keywords: sparse system identification; PNLMS; PMEE; impulsive noise sparse system identification; PNLMS; PMEE; impulsive noise

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Wu, Z.; Peng, S.; Chen, B.; Zhao, H.; Principe, J.C. Proportionate Minimum Error Entropy Algorithm for Sparse System Identification. Entropy 2015, 17, 5995-6006.

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