TechRxiv
NNMCC_1_VER0.pdf (1.58 MB)
Download file

Nonnegative Maximum Correntropy Adaptive Algorithm and Its Statistical Analysis

Download (1.58 MB)
preprint
posted on 13.04.2022, 05:23 authored by Zeyang Sun, Yingsong LiYingsong Li

Dynamic system modeling methods have become a hot topic for stationary and nonstationary signal processing. Nonnegativity is a desired constraint that usually exerts on to be estimated parameters, and its generation usually based on the inherent physical characteristics of unknown system. Moreover, non-Gaussian noise is present in many practical system identification situations. In this paper, an adaptive nonnegative maximum correntropy criterion (NNMCC) algorithm is proposed for system identification under non-negativity constraints. We derive the NNMCC algorithm based on the Karush-Kuhn-Tucker conditions and a fixed-point iteration scheme. The first-order and second-order moments of the NNMCC algorithm adaptive weights are theoretically analyzed. Experimental results validate the theoretical analysis and illustrate the superior performance of NNMCC in non-Gaussian noise environments.


History

Email Address of Submitting Author

liyingsong@ieee.org

Submitting Author's Institution

Anhui University

Submitting Author's Country

China

Usage metrics

Licence

Exports