The robustness of the kernel recursive least square (KRLS) algorithm has recently been improved by combining them with more robust information-theoretic learning criteria, such as minimum error entropy (MEE) and generalized MEE (GMEE), which also improves the computational complexity of the KRLS-type algorithms to a certain extent. To reduce the computational load of the KRLS-type algorithms, the quantized GMEE (QGMEE) criterion, in this paper, is combined with the KRLS algorithm, and as a result two kinds of KRLS-type algorithms, called quantized kernel recursive MEE (QKRMEE) and quantized kernel recursive GMEE (QKRGMEE), are designed. As well, the mean error behavior, mean square error behavior, and computational complexity of the proposed algorithms are investigated. In addition, simulation and real experimental data are utilized to verify the feasibility of the proposed algorithms.

翻译：核递推最小二乘（KRLS）算法的鲁棒性最近通过将其与更鲁棒的信息论学习准则（如最小误差熵（MEE）和广义最小误差熵（GMEE））相结合而得到改善，这也在一定程度上提高了KRLS类算法的计算复杂度。为降低KRLS类算法的计算负荷，本文提出将量化GMEE（QGMEE）准则与KRLS算法相结合，从而设计出两种KRLS类算法，分别称为量化核递推最小误差熵（QKRMEE）和量化核递推广义最小误差熵（QKRGMEE）。此外，本文研究了所提算法的平均误差行为、均方误差行为及计算复杂度。同时，利用仿真和真实实验数据验证了所提算法的可行性。