Results 161 to 170 of about 1,526 (199)
Correntropy-Based Evolving Fuzzy Neural System [PDF]
IEEE Transactions on Fuzzy Systems, 2018 In this paper, a correntropy-based evolving fuzzy neural system (correntropy-EFNS) is proposed for approximation of nonlinear systems. Different from the commonly used meansquare error criterion, correntropy has a strong outliers rejection ability ...
Rong-Jing Bao +2 more
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Maximum correntropy Kalman filter [PDF]
Automatica, 2017 Traditional Kalman filter (KF) is derived under the well-known minimum mean square error (MMSE) criterion, which is optimal under Gaussian assumption. However, when the signals are non-Gaussian, especially when the system is disturbed by some heavy-tailed impulsive noises, the performance of KF will deteriorate seriously.
Badong Chen, Xi Liu, Haiquan Zhao
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Pattern Recognition, 2009 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Weifeng Liu +2 more
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Mixture correntropy for robust learning
Pattern Recognition, 2018Abstract Correntropy is a local similarity measure defined in kernel space, hence can combat large outliers in robust signal processing and machine learning. So far, many robust learning algorithms have been developed under the maximum correntropy criterion (MCC), among which, a Gaussian kernel is generally used in correntropy. To further improve the
Badong Chen, Na Lu, Shiyuan Wang
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Kernel recursive maximum correntropy
Signal Processing, 2015In this letter, a robust kernel adaptive algorithm, called the kernel recursive maximum correntropy (KRMC), is derived in kernel space and under the maximum correntropy criterion (MCC). The proposed algorithm is particularly useful for nonlinear and non-Gaussian signal processing, especially when data contain large outliers or disturbed by impulsive ...
Wentao Ma, Badong Chen
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Generalized Correntropy for RobustAdaptive Filtering [PDF]
IEEE Transactions on Signal Processing, 2016 As a robust nonlinear similarity measure in kernel space, correntropy has received increasing attention in domains of machine learning and signal processing. In particular, the maximum correntropy criterion (MCC) has recently been successfully applied in robust regression and filtering. The default kernel function in correntropy is the Gaussian kernel,
Badong Chen, Lei Xing, Haiquan Zhao
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Maximum Correntropy Criterion With Variable Center [PDF]
IEEE Signal Processing Letters, 2019 5 pages, 1 ...
Badong Chen, Xin Wang, Yingsong Li
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Complex correntropy function: Properties, and application to a channel equalization problem
Expert Systems With Applications, 2018 The use of correntropy as a similarity measure has been increasing in dif ferent scenarios due to the well-known ability to extract high-order statistic information from data.
João P F Guimarães +2 more
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Cyclostationary correntropy: Definition and applications
Expert Systems With Applications, 2017Abstract Information extraction is a frequent and relevant problem in digital signal processing. In the past few years, different methods have been utilized for the parameterization of signals and the achievement of efficient descriptors. When the signals possess statistical cyclostationary properties, the Cyclic Autocorrelation Function (CAF) and ...
Aluisio I R Fontes +2 more
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The Quarternion Maximum Correntropy Algorithm
IEEE Transactions on Circuits and Systems II: Express Briefs, 2015We develop a kernel adaptive filter for quaternion data based on maximizing correntropy. We apply a modified form of the HR calculus that is applicable to Hilbert spaces for evaluating the cost function gradient to develop the quaternion kernel maximum correntropy (KMC) algorithm. The KMC method uses correntropy to measure similarity between the filter
Tokunbo Ogunfunmi, Thomas K. Paul
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