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Entropy 2016, 18(7), 239; doi:10.3390/e18070239

Normalized Minimum Error Entropy Algorithm with Recursive Power Estimation

Division of Electronic, Information and Communication Engineering, Kangwon National University, Samcheok 245-711, Korea
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Academic Editor: J.A. Tenreiro Machado
Received: 26 March 2016 / Revised: 11 June 2016 / Accepted: 22 June 2016 / Published: 24 June 2016
(This article belongs to the Special Issue Computational Complexity)
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Abstract

The minimum error entropy (MEE) algorithm is known to be superior in signal processing applications under impulsive noise. In this paper, based on the analysis of behavior of the optimum weight and the properties of robustness against impulsive noise, a normalized version of the MEE algorithm is proposed. The step size of the MEE algorithm is normalized with the power of input entropy that is estimated recursively for reducing its computational complexity. The proposed algorithm yields lower minimum MSE (mean squared error) and faster convergence speed simultaneously than the original MEE algorithm does in the equalization simulation. On the condition of the same convergence speed, its performance enhancement in steady state MSE is above 3 dB. View Full-Text
Keywords: MEE; step-size; normalization; recursive; power estimation MEE; step-size; normalization; recursive; power estimation
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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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Kim, N.; Kwon, K. Normalized Minimum Error Entropy Algorithm with Recursive Power Estimation. Entropy 2016, 18, 239.

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