An adaptive kernel width convex combination method for 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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Performance evaluation of the maximum complex correntropy criterion with adaptive kernel width update

EURASIP Journal on Advances in Signal Processing ◽

10.1186/s13634-019-0652-2 ◽

2019 ◽

Vol 2019 (1) ◽

Author(s):

Manoel B. L. Aquino ◽

João P. F. Guimarães ◽

Leandro L. S. Linhares ◽

Aluísio I. R. Fontes ◽

Allan M. Martins

Keyword(s):

Free Parameter ◽

Impulsive Noise ◽

Gaussian Kernel ◽

Kernel Width ◽

Optimal Value ◽

Gradient Solution ◽

Adaptive Kernel ◽

Gradient Based ◽

Complex Valued ◽

Steady State Performance

Abstract The complex correntropy is a recently defined similarity measure that extends the advantages of conventional correntropy to complex-valued data. As in the real-valued case, the maximum complex correntropy criterion (MCCC) employs a free parameter called kernel width, which affects the convergence rate, robustness, and steady-state performance of the method. However, determining the optimal value for such parameter is not always a trivial task. Within this context, several works have introduced adaptive kernel width algorithms to deal with this free parameter, but such solutions must be updated to manipulate complex-valued data. This work reviews and updates the most recent adaptive kernel width algorithms so that they become capable of dealing with complex-valued data using the complex correntropy. Besides that, a novel gradient-based solution is introduced to the Gaussian kernel and its respective convergence analysis. Simulations compare the performance of adaptive kernel width algorithms with different fixed kernel sizes in an impulsive noise environment. The results show that the iterative kernel adjustment improves the performance of the gradient solution for complex-valued data.

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Newtonian-Type Adaptive Filtering Based on the Maximum Correntropy Criterion

Entropy ◽

10.3390/e22090922 ◽

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.

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Diffusion Correntropy Subband Adaptive Filtering (SAF) Algorithm over Distributed Smart Dust Networks

Symmetry ◽

10.3390/sym11111335 ◽

2019 ◽

Vol 11 (11) ◽

pp. 1335 ◽

Cited By ~ 1

Author(s):

Ying Guo ◽

Jingjing Li ◽

Yingsong Li

Keyword(s):

Adaptive Filtering ◽

Impulsive Noise ◽

Gaussian Kernel ◽

Sparse System ◽

Smart Dust ◽

Input Signals ◽

Maximum Correntropy Criterion ◽

Non Gaussian ◽

Weighted Values ◽

Dynamic Gain

The diffusion subband adaptive filtering (DSAF) algorithm has attracted much attention in recent years due to its decorrelation ability for colored input signals. In this paper, a modified DSAF algorithm using the symmetry maximum correntropy criterion (MCC) with individual weighting factors is proposed and discussed to combat impulsive noise, which is denoted as the MCC-DSAF algorithm. During the iterations, the negative exponent in the Gaussian kernel of the MCC-DSAF eliminates the interference of outliers to provide a robust performance in non-Gaussian noise environments. Moreover, in order to enhance the convergence for sparse system identifications, a variant of MCC-DSAF named as improved proportionate MCC-DSAF (MCC-IPDSAF) is presented and investigated, which provides a dynamic gain assignment matrix in the MCC-DSAF to adjust the weighted values of each coefficient. Simulation results verify that the newly presented MCC-DSAF and MCC-IPDSAF algorithms are superior to the popular DSAF algorithms.

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Adaptive Filtering Under a Variable Kernel Width Maximum Correntropy Criterion

IEEE Transactions on Circuits & Systems II Express Briefs ◽

10.1109/tcsii.2017.2671339 ◽

2017 ◽

Vol 64 (10) ◽

pp. 1247-1251 ◽

Cited By ~ 24

Author(s):

Fuyi Huang ◽

Jiashu Zhang ◽

Sheng Zhang

Keyword(s):

Adaptive Filtering ◽

Variable Kernel ◽

Kernel Width ◽

Maximum Correntropy Criterion

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Improved proportionate-type sparse adaptive filtering under maximum correntropy criterion in impulsive noise environments

Digital Signal Processing ◽

10.1016/j.dsp.2018.04.011 ◽

2018 ◽

Vol 79 ◽

pp. 190-198 ◽

Cited By ~ 9

Author(s):

Vinay Chakravarthi Gogineni ◽

Subrahmanyam Mula

Keyword(s):

Adaptive Filtering ◽

Impulsive Noise ◽

Maximum Correntropy Criterion

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Robust Adaptive Filtering Algorithm for Self-Interference Cancellation with Impulsive Noise

Electronics ◽

10.3390/electronics10020196 ◽

2021 ◽

Vol 10 (2) ◽

pp. 196

Author(s):

Jun Lu ◽

Qunfei Zhang ◽

Wentao Shi ◽

Lingling Zhang ◽

Juan Shi

Keyword(s):

Adaptive Filtering ◽

Interference Cancellation ◽

Impulsive Noise ◽

Weight Vector ◽

Filtering Algorithm ◽

Convergence Performance ◽

Simulation Results ◽

Robust Adaptive ◽

Arctangent Cost Function ◽

Simultaneous Transmission

Self-interference (SI) is usually generated by the simultaneous transmission and reception in the same system, and the variable SI channel and impulsive noise make it difficult to eliminate. Therefore, this paper proposes an adaptive digital SI cancellation algorithm, which is an improved normalized sub-band adaptive filtering (NSAF) algorithm based on the sparsity of the SI channel and the arctangent cost function. The weight vector is hardly updated when the impulsive noise occurs, and the iteration error resulting from impulsive noise is significantly reduced. Another major factor affecting the performance of SI cancellation is the variable SI channel. To solve this problem, the sparsity of the SI channel is estimated with the estimation of the weight vector at each iteration, and it is used to adjust the weight vector. Then, the convergence performance and calculation complexity are analyzed theoretically. Simulation results indicate that the proposed algorithm has better performance than the referenced algorithms.

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A Convex Combination Approach for Artificial Neural Network of Interval Data

Applied Sciences ◽

10.3390/app11093997 ◽

2021 ◽

Vol 11 (9) ◽

pp. 3997

Author(s):

Woraphon Yamaka ◽

Rungrapee Phadkantha ◽

Paravee Maneejuk

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Convex Combination ◽

Reference Points ◽

Combination Method ◽

Suitable Alternative ◽

Input And Output ◽

Artificial Neural ◽

Conventional Models ◽

Interval Valued

As the conventional models for time series forecasting often use single-valued data (e.g., closing daily price data or the end of the day data), a large amount of information during the day is neglected. Traditionally, the fixed reference points from intervals, such as midpoints, ranges, and lower and upper bounds, are generally considered to build the models. However, as different datasets provide different information in intervals and may exhibit nonlinear behavior, conventional models cannot be effectively implemented and may not be guaranteed to provide accurate results. To address these problems, we propose the artificial neural network with convex combination (ANN-CC) model for interval-valued data. The convex combination method provides a flexible way to explore the best reference points from both input and output variables. These reference points were then used to build the nonlinear ANN model. Both simulation and real application studies are conducted to evaluate the accuracy of the proposed forecasting ANN-CC model. Our model was also compared with traditional linear regression forecasting (information-theoretic method, parametrized approach center and range) and conventional ANN models for interval-valued data prediction (regularized ANN-LU and ANN-Center). The simulation results show that the proposed ANN-CC model is a suitable alternative to interval-valued data forecasting because it provides the lowest forecasting error in both linear and nonlinear relationships between the input and output data. Furthermore, empirical results on two datasets also confirmed that the proposed ANN-CC model outperformed the conventional models.

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