Estimator with forgetting factor of correntropy and recursive algorithm for traffic network prediction

Correntropy as a new similarity function with its excellent property has been used for many case. It´s estimator as a computing tool of Correntropy in sense of mean value, this character has some defect for tracking time-varying parameters in some special case. Thus the estimator with forgetting factor of Correntropy (Correntropy-FF) is researched by us first in this paper. Correntropy-FF is as an estimator of Correntropy in sense of weight average. It can promise some important factor for effect of estimate results. A kind of recursive learning algorithm is deduced based on the Correntropy-FF as a cost function. In general, the current result is impacted by nearing it in the time varying system, so kernel function in Correntropy-FF playing this role as Correntropy. The simulation results on traffic network prediction indicate that the effective performance and available of the proposed algorithm.