Generalized complex kernel least-mean-square algorithm with adaptive kernel widths

A novel variable kernel width generalized complex-valued least mean-square (VKW-GCKLMS) algorithm aims to optimize kernel width in online way to tackle with the problem that the performance of nonlinear kernel algorithms with fixed values of kernel widths is degraded by inappropriate choice of kernel widths. The proposed VKW-GCKLMS algorithm is able to continuously optimize values of kernel widths by using stochastic gradient algorithm in the task of complex-valued nonlinear filtering. Numerical simulations illustrate that the VKW-GCKLMS algorithm is capable of guiding values of kernel widths of different kernels toward those that can achieve the optimal filtering performances. In addition, it is also shown that initial values of kernel widths do not have evident effect on the filtering performance of the VKW-GCKLMS algorithm, which demonstrates the high effectiveness of the VKW-GCKLMS algorithm.