Chaotic time series prediction using fuzzy sigmoid kernel-based support vector machines
Deng Ling-Fenga, Liu Hanb, Liu Dingb
a Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Ontario, Canada; b School of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China
Abstract Support vector machines (SVM) have been widely used in chaotic time series
predictions in recent years. In order to enhance the prediction efficiency
of this method and implement it in hardware, the sigmoid kernel in SVM is
drawn in a more natural way by using the fuzzy logic method proposed in this
paper. This method provides easy hardware implementation and straightforward
interpretability. Experiments on two typical chaotic time series predictions
have been carried out and the obtained results show that the average CPU
time can be reduced significantly at the cost of a small decrease in
prediction accuracy, which is favourable for the hardware implementation for
chaotic time series prediction.
Received: 17 August 2005
Revised: 13 March 2006
Published: 20 June 2006
Fund: Project supported by the Doctoral Program Foundation of Institutions of Higher Eduction of China (Grant No 20040700010)and the Nature Science Specialties Foundation of Education Bureau of Shaanxi Province, China (Grant No 05JK267).
Cite this article:
Liu Han, Liu Ding, Deng Ling-Feng Chaotic time series prediction using fuzzy sigmoid kernel-based support vector machines 2006 Chin. Phys. 15 1196
