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
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
关键词 ：
05.45.+b ,
05.45.Tp ,
07.05.Mh
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.
Key words ：
05.45.+b
05.45.Tp
07.05.Mh
出版日期: 2001-03-01
引用本文:
ZHANG Jia-Shu; XIAO Xian-Ci. Predicting Hyper-Chaotic Time Series Using Adaptive Higher-Order Nonlinear Filter[J]. 中国物理快报, 2001, 18(3): 337-340.
链接本文:
https://cpl.iphy.ac.cn/CN/
或
https://cpl.iphy.ac.cn/CN/Y2001/V18/I3/337
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2001, Vol. 18 
