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Sparse-Aware Bias-Compensated Adaptive Filtering Algorithms Using the Maximum Correntropy Criterion for Sparse System Identification with Noisy Input

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School of Automation and Information Engineering, Xi’an University of Technology, Xi’an 710048, China
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State Key Laboratory of Electrical Insulation and Power Equipment, Xi’an Jiaotong University, Xi’an 710049, China
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Management Center of Internet Information, Xi’an University of Technology, Xi’an 710048, China
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Authors to whom correspondence should be addressed.
Entropy 2018, 20(6), 407; https://doi.org/10.3390/e20060407
Received: 2 April 2018 / Revised: 2 May 2018 / Accepted: 7 May 2018 / Published: 25 May 2018
To address the sparse system identification problem under noisy input and non-Gaussian output measurement noise, two novel types of sparse bias-compensated normalized maximum correntropy criterion algorithms are developed, which are capable of eliminating the impact of non-Gaussian measurement noise and noisy input. The first is developed by using the correntropy-induced metric as the sparsity penalty constraint, which is a smoothed approximation of the 0 norm. The second is designed using the proportionate update scheme, which facilitates the close tracking of system parameter change. Simulation results confirm that the proposed algorithms can effectively improve the identification performance compared with other algorithms presented in the literature for the sparse system identification problem. View Full-Text
Keywords: bias-compensated; correntropy-induced metric; maximum correntropy criterion; noisy input; proportionate update; sparse system identification bias-compensated; correntropy-induced metric; maximum correntropy criterion; noisy input; proportionate update; sparse system identification
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Ma, W.; Zheng, D.; Zhang, Z.; Duan, J.; Qiu, J.; Hu, X. Sparse-Aware Bias-Compensated Adaptive Filtering Algorithms Using the Maximum Correntropy Criterion for Sparse System Identification with Noisy Input. Entropy 2018, 20, 407.

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