Chaotic time series prediction using fuzzy sigmoid kernel-based support vector machines
Liu Han (刘涵)a, Liu Ding (刘丁)a, Deng Ling-Feng (邓凌峰)b
a School of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China; b Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Ontario, Canada
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
Accepted manuscript online:
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 (刘涵), Liu Ding (刘丁), Deng Ling-Feng (邓凌峰) Chaotic time series prediction using fuzzy sigmoid kernel-based support vector machines 2006 Chinese Physics 15 1196
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