KLMAT: A Kernel Least Mean Absolute Third Algorithm
Systems and Control
2017-08-15 v3
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.
Cite
@article{arxiv.1603.03564,
title = {KLMAT: A Kernel Least Mean Absolute Third Algorithm},
author = {Lu Lu and Haiquan Zhao and Badong Chen},
journal= {arXiv preprint arXiv:1603.03564},
year = {2017}
}
Comments
submitted to the journal in March, 17th, 2015