Predicting Hyper-Chaotic Time Series Using Adaptive Higher-Order Nonlinear Filter

作     者：ZHANG Jia-Shu XIAO Xian-Ci 张家树;肖先赐

作者机构：Department of Electronic EngineeringUniversity of Electronic Science and Technology of ChinaChengdu 610054 

出 版 物：《Chinese Physics Letters》 (中国物理快报（英文版）)

年 卷 期：2001年第18卷第3期

页      面：337-340页

核心收录：

学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学] 

基　　金：Supported by the National Defense Foundation of China under Grant No.98JS05.4.1.DZ0205 

主　　题：chaotic nonlinear Nonlinear 

摘      要：A newly proposed method,*** adaptive higher-order nonlinear finite impulse response(HONFIR)filter based on higher-order sparse Volterra series expansions,is introduced to predict hyper-chaotic time *** 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 *** simulation results show that the adaptive HONFIR filter proposed here is a very powerful tool for making prediction of hyper-chaotic time series.