Results 121 to 130 of about 543 (157)
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Robust Multikernel Maximum Correntropy Filters
IEEE Transactions on Circuits and Systems II: Express Briefs, 2020The multikernel adaptive filters based on the minimum mean square error (MMSE) criterion have been proposed to improve the performance of the kernel least mean square (KLMS), efficiently. However, these multikernel methods suffer from large computational burden as well as instability in impulsive noises.
Kui Xiong, Wei Shi, Shiyuan Wang
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Kernel recursive maximum correntropy with variable center
Signal Processing, 2022Abstract In signal processing and machine learning, the maximum correntropy criterion with variable center (MCC-VC) has attracted more and more attention due to its robustness to non-zero mean noise. In this letter, we introduce MCC-VC into kernel space and develop the kernel recursive maximum correntropy with variable center (KRMCVC) algorithm.
Xiang Liu, Chengtian Song, Zhihua Pang
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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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A Separable Maximum Correntropy Adaptive Algorithm
IEEE Transactions on Circuits and Systems II: Express Briefs, 2020In this brief, a separable maximum correntropy criterion (SMCC) algorithm is developed by exploiting the typical separability property of tensors. Utilizing the separability property, a great number savings are obtained along with accelerated learning rate and improved estimate accuracy. In the proposed SMCC, a correntropy scheme is used to construct a
Wanlu Shi, Yingsong Li 0001, Badong Chen
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Robust constrained maximum total correntropy algorithm
Signal Processing, 2021Abstract Constrained adaptive filtering has been paid more attentions recently. As a robust constrained adaptive filtering algorithm, constrained maximum correntropy criterion (CMCC) has shown its superiority for the output data contaminated by heavy-tail impulsive noises.
Guobing Qian +3 more
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Projected Kernel Recursive Maximum Correntropy
IEEE Transactions on Circuits and Systems II: Express Briefs, 2018In this brief, a different kernel recursive maximum correntropy algorithm is derived using the weighted output information, called KRMC-W. To curb the network growth, we propose a new online sparsification strategy in a feature space, named vector projection (VP) method.
Ji Zhao 0005 +2 more
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Kernel Recursive Generalized Maximum Correntropy
IEEE Signal Processing Letters, 2017In this letter, a novel kernel adaptive algorithm, called kernel recursive generalized maximum correntropy algorithm (KRGMC), is derived in a kernel space and under the generalized maximum correntropy (GMC) criterion. The proposed kernel algorithm can effectively scale down the dynamic recursive weight coefficients influenced by the impulsive estimate ...
Ji Zhao 0005, Hongbin Zhang 0002
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Multi-Kernel Maximum Correntropy Kalman Filter
IEEE Control Systems Letters, 2022Maximum correntropy criterion (MCC) has been widely used in Kalman filter to cope with heavy-tailed measurement noises. However, its performance on mitigating non-Gaussian process noises and unknown disturbance is rarely explored. In this letter, we extend the definition of correntropy from a single kernel to multiple kernels.
Shilei Li +3 more
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State space maximum correntropy filter
Signal Processing, 2017The state space recursive least squares (SSRLS) filter is a new addition to the well-known recursive least squares (RLS) family filters, which can achieve an excellent tracking performance by overcoming some limitations of the standard RLS algorithm.
Xi Liu 0006 +3 more
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Maximum Total Complex Correntropy for Adaptive Filter
IEEE Transactions on Signal Processing, 2020Nowadays, complex Correntropy has been widely used for adaptive filtering in the complex domain. Compared with the second order statistics methods, the complex correntropy based algorithms have shown the superiority in the non-Gaussian noise, especially the impulsive noise.
Guobing Qian +2 more
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