%0 DATA
%A Ying, Guo
%A Bing, Ma
%A Yingsong, Li
%D 2020
%T A Kernel-Width Adaption Diffusion Maximum Correntropy Algorithm
%U https://www.techrxiv.org/articles/preprint/A_Kernel-Width_Adaption_Diffusion_Maximum_Correntropy_Algorithm/11842281
%R 10.36227/techrxiv.11842281.v1
%2 https://www.techrxiv.org/ndownloader/files/21700617
%K adaptive filter
%K Adaptive kernel width
%K diffusion algorithm
%K maximum correntropy criterion
%K sparse system identification
%X In this paper, a diffusion maximum correntropy criterion (DMCC) algorithm with adaption kernel width is proposed, denoting as DMCC$_{\rm adapt}$ algorithm, to find out a solution for dynamically choosing the kernel width. The DMCC$_{\rm adapt}$ algorithm chooses small kernel width at initial stage to improve its convergence speed rate, and uses large kernel width at completion stage to reduce its steady-state error. To render the proposed DMCC$_{\rm adapt}$ algorithm suitable for sparse system identifications, the DMCC$_{\rm adapt}$ algorithm based on proportional coefficient adjustment is realized and named as diffusion proportional maximum correntropy criterion (DPMCC$_{\rm adapt}$). The theoretical analysis and simulation results are presented to show that the DPMCC$_{\rm adapt}$ and DMCC$_{\rm adapt}$ algorithms have better convergence than the traditional diffusion AF algorithms under impulse noise and sparse systems.