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• W3084624567 abstract "Recently, the kernel based constrained adaptive filtering algorithm has attracted a lot of attentions because of its robustness and superiority over traditional methods. As two classic kernel based algorithms, both constrained maximum correntropy criterion (CMCC) and constrained minimum error entropy (CMEE) have shown their superiorities in the case of non-Gaussian noise. However, both algorithms use only one kernel as the kernel function. To further improve the performance of the kernel based adaptive filtering algorithms, we first define the mixture kernel risk-sensitive loss (MKRSL) and study its properties. Then, we apply it to the constrained adaptive filtering and propose a novel constrained minimum MKRSL (CMM-KRSL) algorithm in this paper. Furthermore, we present the performance analysis of the CMM-KRSL algorithm, and provide the stability condition and the theoretical mean square deviation (MSD). Finally, we validate the accuracy of performance analysis and the superiorities of CMM-KRSL by simulations." @default.
• W3084624567 created "2020-09-21" @default.
• W3084624567 creator A5012795428 @default.
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• W3084624567 date "2020-12-01" @default.
• W3084624567 modified "2023-10-15" @default.
• W3084624567 title "Robust constrained minimum mixture kernel risk-sensitive loss algorithm for adaptive filtering" @default.
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• W3084624567 doi "https://doi.org/10.1016/j.dsp.2020.102859" @default.
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