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Entropy 2015, 17(10), 7149-7166; doi:10.3390/e17107149

Robust Hammerstein Adaptive Filtering under Maximum Correntropy Criterion

1
School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510640, China
2
School of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, China
3
School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China
*
Author to whom correspondence should be addressed.
Academic Editor: Kevin H. Knuth
Received: 25 June 2015 / Revised: 29 September 2015 / Accepted: 12 October 2015 / Published: 22 October 2015
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Abstract

The maximum correntropy criterion (MCC) has recently been successfully applied to adaptive filtering. Adaptive algorithms under MCC show strong robustness against large outliers. In this work, we apply the MCC criterion to develop a robust Hammerstein adaptive filter. Compared with the traditional Hammerstein adaptive filters, which are usually derived based on the well-known mean square error (MSE) criterion, the proposed algorithm can achieve better convergence performance especially in the presence of impulsive non-Gaussian (e.g., α-stable) noises. Additionally, some theoretical results concerning the convergence behavior are also obtained. Simulation examples are presented to confirm the superior performance of the new algorithm. View Full-Text
Keywords: Hammerstein adaptive filtering; MCC; nonlinear system identification Hammerstein adaptive filtering; MCC; nonlinear system identification
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. Robust Hammerstein Adaptive Filtering under Maximum Correntropy Criterion. Entropy 2015, 17, 7149-7166.

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