Univariate kernel sums correntropy for adaptive filtering

In this paper, we focus on a special form of univariate kernel and, by specifying the relationship between its parameters, make its first derivative close to a neat form. By using this kind of univariate kernel sums instead of Gaussian kernel function, we put forward the sum of univariate kernels maximum correntropy criterion (SKMCC) on the basis of maximum entropy criterion (MCC) and apply it to adaptive filtering. We study the characteristics of the performance surface of the new algorithm and confirm the theoretical results in the simulation. Its superior performance is proved by comparing with other latest adaptive algorithms.