Abstract
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.