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In this paper, a kernel least mean absolute third (KLMAT) algorithm isdeveloped for adaptive prediction. Combining the benefits of the kernel methodand the least mean absolute third (LMAT) algorithm, the proposed KLMATalgorithm performs robustly against noise with different probability densities.To further enhance the convergence rate of the KLMAT algorithm, a variablestep-size version (VSS-KLMAT algorithm) is proposed based on a Lorentzianfunction. Moreover, the stability and convergence property of the proposedalgorithms are analyzed. Simulation results in the context of time seriesprediction demonstrate that the effectiveness of proposed algorithms.

KLMAT: A Kernel Least Mean Absolute Third Algorithm