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Kernel Recursive Generalized Maximum Correntropy
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DOI：10.1109/LSP.2017.2761886.png)
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Abstract
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In 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 error to avoid the significant performance degradation. The superior performance of the proposed algorithm is verified by numerical simulations about short-time series prediction in alpha-stable noise environment.
Keywords:
Generalized maximum correntropy (GMC) criterion
kernel adaptive filter (KAF)
non-Gaussian noise
recursive
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