Chaotic time series prediction using fuzzy sigmoid kernel-based support vector machines |
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Authors: | Liu Han Liu Ding and Deng Ling-Feng |
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Affiliation: | School of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China; Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, Ontario, Canada |
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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. |
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Keywords: | support vector machines chaotic time series prediction fuzzy sigmoid kernel |
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