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.