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Keywords = proportionate NLMS (PNLMS)

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13 pages, 1951 KiB  
Article
A General Zero Attraction Proportionate Normalized Maximum Correntropy Criterion Algorithm for Sparse System Identification
by Yingsong Li, Yanyan Wang, Felix Albu and Jingshan Jiang
Symmetry 2017, 9(10), 229; https://doi.org/10.3390/sym9100229 - 15 Oct 2017
Cited by 41 | Viewed by 4200
Abstract
A general zero attraction (GZA) proportionate normalized maximum correntropy criterion (GZA-PNMCC) algorithm is devised and presented on the basis of the proportionate-type adaptive filter techniques and zero attracting theory to highly improve the sparse system estimation behavior of the classical MCC algorithm within [...] Read more.
A general zero attraction (GZA) proportionate normalized maximum correntropy criterion (GZA-PNMCC) algorithm is devised and presented on the basis of the proportionate-type adaptive filter techniques and zero attracting theory to highly improve the sparse system estimation behavior of the classical MCC algorithm within the framework of the sparse system identifications. The newly-developed GZA-PNMCC algorithm is carried out by introducing a parameter adjusting function into the cost function of the typical proportionate normalized maximum correntropy criterion (PNMCC) to create a zero attraction term. The developed optimization framework unifies the derivation of the zero attraction-based PNMCC algorithms. The developed GZA-PNMCC algorithm further exploits the impulsive response sparsity in comparison with the proportionate-type-based NMCC algorithm due to the GZA zero attraction. The superior performance of the GZA-PNMCC algorithm for estimating a sparse system in a non-Gaussian noise environment is proven by simulations. Full article
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14 pages, 2740 KiB  
Article
An Enhanced Set-Membership PNLMS Algorithm with a Correntropy Induced Metric Constraint for Acoustic Channel Estimation
by Zhan Jin, Yingsong Li and Yanyan Wang
Entropy 2017, 19(6), 281; https://doi.org/10.3390/e19060281 - 15 Jun 2017
Cited by 16 | Viewed by 4731
Abstract
In this paper, a sparse set-membership proportionate normalized least mean square (SM-PNLMS) algorithm integrated with a correntropy induced metric (CIM) penalty is proposed for acoustic channel estimation and echo cancellation. The CIM is used for constructing a new cost function within the kernel [...] Read more.
In this paper, a sparse set-membership proportionate normalized least mean square (SM-PNLMS) algorithm integrated with a correntropy induced metric (CIM) penalty is proposed for acoustic channel estimation and echo cancellation. The CIM is used for constructing a new cost function within the kernel framework. The proposed CIM penalized SM-PNLMS (CIMSM-PNLMS) algorithm is derived and analyzed in detail. A desired zero attraction term is put forward in the updating equation of the proposed CIMSM-PNLMS algorithm to force the inactive coefficients to zero. The performance of the proposed CIMSM-PNLMS algorithm is investigated for estimating an underwater communication channel estimation and an echo channel. The obtained results demonstrate that the proposed CIMSM-PNLMS algorithm converges faster and provides a smaller estimation error in comparison with the NLMS, PNLMS, IPNLMS, SM-PNLMS and zero-attracting SM-PNLMS (ZASM-PNLMS) algorithms. Full article
(This article belongs to the Special Issue Maximum Entropy and Its Application II)
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