Single Feedback Based Kernel Generalized Maximum Correntropy Adaptive Filtering Algorithm

This paper presents a novel single feedback based kernel generalized maximum correntropy (SF-KGMC) algorithm by introducing a single delay into the framework of kernel adaptive filtering. In SF-KGMC, the history information implicitly existing in the single delayed output can enhance the convergence rate. Compared to the second-order statistics criterion, the generalized maximum correntropy (GMC) criterion shows better robustness against outliers. Therefore, SF-KGMC can efficiently reduce the influence of impulsive noise and avoids significant performance degradation. In addition, for SF-KGMC, the theoretical convergence analysis is also conducted. Simulation results on chaotic time-series prediction and real-world data applications validate that SF-KGMC achieves better filtering accuracy and a faster convergence rate.