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arXiv · 1603.03564

KLMAT: A Kernel Least Mean Absolute Third Algorithm

Lu Lu·Haiquan Zhao·Badong Chen
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Abstract

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

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Lu Lu, Haiquan Zhao, Badong Chen. 2016-03-11. KLMAT: A Kernel Least Mean Absolute Third Algorithm. https://arxiv.org/abs/1603.03564

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