scholarly journals Newtonian-Type Adaptive Filtering Based on the Maximum Correntropy Criterion

Entropy ◽  
2020 ◽  
Vol 22 (9) ◽  
pp. 922
Author(s):  
Pengcheng Yue ◽  
Hua Qu ◽  
Jihong Zhao ◽  
Meng Wang
Keyword(s):  
Steady State ◽  
Adaptive Filtering ◽  
Adaptive Filters ◽  
Impulsive Noise ◽  
Minimum Mean Square Error ◽  
Gradient Descent Method ◽  
Type Method ◽  
Newtonian Type ◽  
Maximum Correntropy Criterion ◽  
Steady State Performance

This paper provides a novel Newtonian-type optimization method for robust adaptive filtering inspired by information theory learning. With the traditional minimum mean square error (MMSE) criterion replaced by criteria like the maximum correntropy criterion (MCC) or generalized maximum correntropy criterion (GMCC), adaptive filters assign less emphasis on the outlier data, thus become more robust against impulsive noises. The optimization methods adopted in current MCC-based LMS-type and RLS-type adaptive filters are gradient descent method and fixed point iteration, respectively. However, in this paper, a Newtonian-type method is introduced as a novel method for enhancing the existing body of knowledge of MCC-based adaptive filtering and providing a fast convergence rate. Theoretical analysis of the steady-state performance of the algorithm is carried out and verified by simulations. The experimental results show that, compared to the conventional MCC adaptive filter, the MCC-based Newtonian-type method converges faster and still maintains a good steady-state performance under impulsive noise. The practicability of the algorithm is also verified in the experiment of acoustic echo cancellation.

scholarly journals Variable Step Size Maximum Correntropy Criteria Based Adaptive Filtering Algorithm

2016 ◽  
Vol 6 (2) ◽  
pp. 923-926
Author(s):  
S. Radhika ◽  
A. Sivabalan
Keyword(s):  
Steady State ◽  
Adaptive Filters ◽  
Impulsive System ◽  
Variable Step Size ◽  
Step Size ◽  
Impulsive Interference ◽  
Variable Step ◽  
Improved Performance ◽  
Maximum Correntropy Criterion ◽  
State Error

Maximum correntropy criterion (MCC) based adaptive filters are found to be robust against impulsive interference. This paper proposes a novel MCC based adaptive filter with variable step size in order to obtain improved performance in terms of both convergence rate and steady state error with robustness against impulsive interference. The optimal variable step size is obtained by minimizing the Mean Square Deviation (MSD) error from one iteration to the other. Simulation results in the context of a highly impulsive system identification scenario show that the proposed algorithm has faster convergence and lesser steady state error than the conventional MCC based adaptive filters.

Steady-State Performance of Spline Adaptive Filters

2016 ◽  
Vol 64 (4) ◽  
pp. 816-828 ◽  
Author(s):  
Michele Scarpiniti ◽  
Danilo Comminiello ◽  
Gaetano Scarano ◽  
Raffaele Parisi ◽  
Aurelio Uncini
Keyword(s):  
Steady State ◽  
Adaptive Filters ◽  
Steady State Performance

scholarly journals Frequency domain maximum correntropy criterion spline adaptive filtering

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Wenyan Guo ◽  
Yongfeng Zhi ◽  
Kai Feng
Keyword(s):  
Frequency Domain ◽  
Adaptive Filtering ◽  
Adaptive Filter ◽  
Adaptive Filters ◽  
Operation Time ◽  
Filtering Algorithm ◽  
Impulsive Noises ◽  
Type Frequency ◽  
Maximum Correntropy Criterion ◽  
Non Gaussian

AbstractA filtering algorithm based on frequency domain spline type, frequency domain spline adaptive filters (FDSAF), effectively reducing the computational complexity of the filter. However, the FDSAF algorithm is unable to suppress non-Gaussian impulsive noises. To suppression non-Gaussian impulsive noises along with having comparable operation time, a maximum correntropy criterion (MCC) based frequency domain spline adaptive filter called frequency domain maximum correntropy criterion spline adaptive filter (FDSAF-MCC) is developed in this paper. Further, the bound on learning rate for convergence of the proposed algorithm is also studied. And through experimental simulations verify the effectiveness of the proposed algorithm in suppressing non-Gaussian impulsive noises. Compared with the existing frequency domain spline adaptive filter, the proposed algorithm has better performance.

scholarly journals An adaptive kernel width convex combination method for maximum correntropy criterion

2021 ◽  
Vol 27 (1) ◽  
Author(s):  
Aluisio I. R. Fontes ◽  
Leandro L. S. Linhares ◽  
João P. F. Guimarães ◽  
Luiz F. Q. Silveira ◽  
Allan M. Martins
Keyword(s):  
Adaptive Filtering ◽  
Convergence Rates ◽  
Impulsive Noise ◽  
Convex Combination ◽  
Combination Method ◽  
Kernel Width ◽  
Optimal Value ◽  
Adaptive Kernel ◽  
Novel Method ◽  
Maximum Correntropy Criterion

AbstractRecently, the maximum correntropy criterion (MCC) has been successfully applied in numerous applications regarding nonGaussian data processing. MCC employs a free parameter called kernel width, which affects the convergence rate, robustness, and steady-state performance of the adaptive filtering. However, determining the optimal value for such parameter is not always a trivial task. Within this context, this paper proposes a novel method called adaptive convex combination maximum correntropy criterion (ACCMCC), which combines an adaptive kernel algorithm with convex combination techniques. ACCMCC takes advantage from a convex combination of two adaptive MCC-based filters, whose kernel widths are adjusted iteratively as a function of the minimum error value obtained in a predefined estimation window. Results obtained in impulsive noise environment have shown that the proposed approach achieves equivalent convergence rates but with increased accuracy and robustness when compared with other similar algorithms reported in literature.

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