TY - JOUR
T1 - A modified quantized kernel least mean square algorithm for prediction of chaotic time series
AU - Zheng, Yunfei
AU - Wang, Shiyuan
AU - Feng, Jiuchao
AU - Tse, Chi K.
PY - 2016/1
Y1 - 2016/1
N2 - 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.
AB - 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.
KW - Chaotic time series
KW - Coefficient update
KW - Gradient descent method
KW - Quantized kernel least-mean-square
UR - http://www.scopus.com/inward/record.url?scp=84947264155&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84947264155&origin=recordpage
U2 - 10.1016/j.dsp.2015.09.015
DO - 10.1016/j.dsp.2015.09.015
M3 - RGC 21 - Publication in refereed journal
SN - 1051-2004
VL - 48
SP - 130
EP - 136
JO - Digital Signal Processing: A Review Journal
JF - Digital Signal Processing: A Review Journal
ER -