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Entropy 2016, 18(7), 239;

Normalized Minimum Error Entropy Algorithm with Recursive Power Estimation

Division of Electronic, Information and Communication Engineering, Kangwon National University, Samcheok 245-711, Korea
Author to whom correspondence should be addressed.
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)
Full-Text   |   PDF [2079 KB, uploaded 24 June 2016]   |  


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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Kim, N.; Kwon, K. Normalized Minimum Error Entropy Algorithm with Recursive Power Estimation. Entropy 2016, 18, 239.

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