Tracking Analysis of Gaussian Kernel Signed Error Algorithm for Time-Variant Nonlinear Systems

This brief establishes a novel kernel-based model with a random walk variation of the optimum weight coefficients to characterize the time-variant nonlinear system. Then, the steady-state tracking performance of the kernel signed error algorithm (KSEA) with Gaussian kernel is analyzed for the proposed time-variant nonlinear system in the presence of non-Gaussian impulsive noise. The theoretical findings enable us to determine the optimal step-size that minimizes the steady-state excess mean-square error under this non-stationary environment. Simulation results illustrate the usefulness and accuracy of the derived analytical models for characterizing the steady-state tracking behavior of Gaussian KSEA.