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Predicting Hyper-Chaotic Time Series Using Adaptive Higher-Order Nonlinear Filter |
| ZHANG Jia-Shu; XIAO Xian-Ci |
| Department of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 610054
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| Cite this article: |
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ZHANG Jia-Shu, XIAO Xian-Ci 2001 Chin. Phys. Lett. 18 337-340 |
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Abstract A newly proposed method, i.e. the adaptive higher-order nonlinear finite impulse response (HONFIR) filter based on higher-order sparse Volterra series expansions, is introduced to predict hyper-chaotic time series. The effectiveness of using adaptive HONFIR filter for making one-step and multi-step predictions is tested based on very few data points by computer-generated hyper-chaotic time series including Mackey-Glass equation and 4-dimensional nonlinear dynamical system. A comparison is made with some neural networks for predicting the Mackey-Glass hyper-chaotic time series. Numerical simulation results show that the adaptive HONFIR filter proposed here is a very powerful tool for making prediction of hyper-chaotic time series.
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| Keywords:
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Published: 01 March 2001
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