Nonlinear Multiple-Delay Feedback Based Kernel Least Mean Square Algorithm

In this paper, a novel algorithm called nonlinear multiple-delay feedback kernel least mean square (NMDF-KLMS) is proposed by introducing a nonlinear multiple-delay into the framework of multikernel adaptive filtering. The proposed algorithm incorporates the nonlinear multiple-delay to enhance the filtering performance in comparison with the kernel adaptive filtering algorithm using linear feedback. Furthermore, for NMDF-KLMS, the theoretical mean-square convergence analyses is also conducted. Simulation results under chaotic time-series prediction and real-world data applications show that NMDF-KLMS achieves a faster convergence rate and superior filtering accuracy.