A Robust Kernel Least Mean Square Algorithm and its Quantization

To further improve the performance of the kernel adaptive filtering algorithm in a non-Gaussian environment, a robust kernel least mean square algorithm is proposed, and the effectiveness of the root cost function and the convergence of the algorithm is theoretically analyzed. An improved online vector quantization criterion is then applied to the proposed algorithm to suppress the linearly growing network size. Finally, the different performances of the algorithm of this paper and other kernel adaptive filtering algorithms as well as this paper's algorithm before and after quantization are compared in Mackey Glass chaotic time series as well as in system identification, confirming the superiority of the algorithm of this paper and the improved online vector quantization criterion.