204 research outputs found
Generalized Minimum Error Entropy for Adaptive Filtering
Error entropy is a important nonlinear similarity measure, and it has
received increasing attention in many practical applications. The default
kernel function of error entropy criterion is Gaussian kernel function,
however, which is not always the best choice. In our study, a novel concept,
called generalized error entropy, utilizing the generalized Gaussian density
(GGD) function as the kernel function is proposed. We further derivate the
generalized minimum error entropy (GMEE) criterion, and a novel adaptive
filtering called GMEE algorithm is derived by utilizing GMEE criterion. The
stability, steady-state performance, and computational complexity of the
proposed algorithm are investigated. Some simulation indicate that the GMEE
algorithm performs well in Gaussian, sub-Gaussian, and super-Gaussian noises
environment, respectively. Finally, the GMEE algorithm is applied to acoustic
echo cancelation and performs well.Comment: 9 pages, 8 figure
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