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Based on the kernel methods and the nonlinear feature of chaotic time series, we develop a new algorithm called kernel least mean kurtosis (KLMK) by applying the kernel trick to the least mean kurtosis (LMK) algorithm, which maps the input data to a high dimensional feature space. The KLMK algorithm can overcome the shortcomings of the original LMK for nonlinear time series prediction, and it is easy to implement a sample by sample adaptation procedure. Theoretical analysis suggests that thedoi:10.1088/0256-307x/30/11/110505 fatcat:ux4yyqkmevdpdme26zwnljomru