  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.
