Filtered-s normalized maximum mixture correntropy criterion algorithm for nonlinear active noise control

Abstract

In order to deal with impulsive noise, the traditional filtered-s normalized maximum correntropy criterion (FsNMCC) adaptive algorithm has good robustness in nonlinear active noise control (ANC) systems. However, the FsNMCC algorithm has a single Gaussian kernel, of which the noise reduction performance is susceptible to the value of the kernel width. To surmount this shortcoming, the filtered-s normalized maximum mixture correntropy criterion (FsNMMCC) algorithm is designed for a functional link artificial neural network (FLANN) based on ANC systems. Simulation results show that the proposed FsNMMCC algorithm in this paper has better noise reduction performance than the FsNMCC algorithm in active noise control of impulsive noise with standard symmetric α-stable (SαS) distribution.