Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/43484
Title: A modified quantized kernel least mean square algorithm for prediction of chaotic time series
Authors: Zheng, Y
Wang, S
Feng, J
Tse, CK 
Keywords: Chaotic time series
Coefficient update
Gradient descent method
Quantized kernel least-mean-square
Issue Date: 2016
Publisher: Academic Press
Source: Digital signal processing : a review journal, 2016, v. 48, p. 130-136 How to cite?
Journal: Digital signal processing : a review journal 
Abstract: A modified quantized kernel least mean square (M-QKLMS) algorithm is proposed in this paper, which is an improvement of quantized kernel least mean square (QKLMS) and the gradient descent method is used to update the coefficient of filter. Unlike the QKLMS method which only considers the prediction error, the M-QKLMS method uses both the new training data and the prediction error for coefficient adjustment of the closest center in the dictionary. Therefore, the proposed method completely utilizes the knowledge hidden in the new training data, and achieves a better accuracy. In addition, the energy conservation relation and a sufficient condition for mean-square convergence of the proposed method are obtained. Simulations on prediction of chaotic time series show that the M-QKLMS method outperforms the QKLMS method in terms of steady-state mean square errors.
URI: http://hdl.handle.net/10397/43484
ISSN: 1051-2004
DOI: 10.1016/j.dsp.2015.09.015
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