KLMAT: A Kernel Least Mean Absolute Third Algorithm

2016/03/11 by Lu Lu, Haiquan Zhao, Lu, Lu +3
Computer Science · Engineering · #Advanced Adaptive Filtering Techniques #Blind Source Separation Techniques #FOS: Electrical engineering #Neural Networks and Applications #Systems and Control (eess.SY) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.1603.03564

openalex publication_date 2016/03/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

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.

Citations

• Improved convergence analysis of stochastic gradient adaptive filters using the sign algorithm

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