Results 71 to 80 of about 2,111 (181)
An adaptive kernel width convex combination method for maximum correntropy criterion
Journal of the Brazilian Computer Society, 2021 Recently, 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 ...
Aluisio I. R. Fontes +4 more
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IET Control Theory &Applications, Volume 19, Issue 1, January/December 2025.This work addresses the issue of rejection delay due to DoS attacks triggered by historical measurements during the transmission of a large amount of measurement data in WECS‐based networked microgrids. We propose a novel robust SRCKF method, designated as MCC‐SRCKF, which incorporates MCC into the SRCKF structure of DSE.
Xiao Hu +4 more
wiley +1 more source
KLMS‐Net: Deep unrolling for kernel least mean square algorithm
Electronics Letters, Volume 61, Issue 1, January/December 2025.This letter proposes a novel network framework based on the deep unrolling of kernel least mean square (KLMS‐Net). KLMS‐Net transforms the iterative process of KLMS into the forward propagation of deep neural networks, which learn the implicit feature mappings in a model‐driven manner, providing deep neural networks with explicit interpretability ...
Yu Tang +5 more
wiley +1 more source
Complex Correntropy Applied to a Compressive Sensing Problem in an Impulsive Noise Environment
IEEE Access, 2019 Correntropy is a similarity function capable of extracting high-order statistical information from data. It has been used in different kinds of applications as a cost function to overcome traditional methods in non-Gaussian noise environments. One of the
Joao P. F. Guimaraes +4 more
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IET Electric Power Applications, Volume 19, Issue 1, January/December 2025.This paper proposes an attention‐guided semi‐supervised model for transformer fault diagnosis using vibration‐acoustic data fusion. The model employs multilevel attention and a consistency learning strategy to enhance diagnostic accuracy under limited labelled data.
Yanfei Sun +3 more
wiley +1 more source
Method of individual communication transmitter identification based on maximum correntropy
Tongxin xuebao, 2016 To measure the similarity between the fine features of communication transmitters, a method of individual communication transmitter identification based on maximum correntropy was put forward.
Zhe TANG, Ying-ke LEI
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Maximum Correntropy Derivative-Free Robust Kalman Filter and Smoother
IEEE Access, 2018 We consider the problem of robust estimation involving filtering and smoothing for nonlinear state space models which are disturbed by heavy-tailed impulsive noises. To deal with heavy-tailed noises and improve the robustness of the traditional nonlinear
Hongwei Wang +4 more
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Mean square cross error: performance analysis and applications in non-Gaussian signal processing
EURASIP Journal on Advances in Signal Processing, 2021 Most of the cost functions of adaptive filtering algorithms include the square error, which depends on the current error signal. When the additive noise is impulsive, we can expect that the square error will be very large.
Yunxiang Zhang +3 more
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A Student's T Distribution‐Based Filter Design for SINS/GNSS With Heavy‐Tailed Noise
IET Electric Power Applications, Volume 19, Issue 1, January/December 2025.To reduce the affection of outliers caused by heavy‐tailed noise, the noise model is constructed by the Student's T distribution rather than the Gaussian distribution, and the related probability density functions (PDF) are adaptively modelled as student's T PDFs with different DoF parameters.
Menghao Qian, Wei Chen, Ruisheng Sun
wiley +1 more source
A Robust Adaptive Filter for a Complex Hammerstein System
Entropy, 2019 The Hammerstein adaptive filter using maximum correntropy criterion (MCC) has been shown to be more robust to outliers than the ones using the traditional mean square error (MSE) criterion. As there is no report on the robust Hammerstein adaptive filters
Guobing Qian, Dan Luo, Shiyuan Wang
doaj +1 more source