Open AccessPREDICTION OF CHAOTIC TIME SERIES BY USING ADAPTIVE HIGHER-ORDER NONLINEAR FOUR IER INFRARED FILTER
Author(s) -
Jiashu Zhang,
Xin Xiao
Publication year - 2000
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.49.1221
Subject(s) - chaotic , control theory (sociology) , filter (signal processing) , kernel adaptive filter , nonlinear system , adaptive filter , series (stratigraphy) , nonlinear filter , raised cosine filter , computer science , mathematics , filter design , algorithm , physics , artificial intelligence , computer vision , paleontology , control (management) , quantum mechanics , biology
Based on the Volterra expansion of nonlinear dynamical system functions and the deterministic and nonlinear characterization of the chaotic signals,an adaptive higher-order nonlinear Fourier infrared(HONFIR)filter is proposed to make predic tion of chaotic time series.The time domain orthogonal algorithm is taken to upd ate filter's coefficients.A higher-order nonlinear adaptive filtering scheme is suggested in order to track current chaotic trajectory by using preceding predic tive error for adjustign filter parameters rather than approximating global or l ocal map of chaotic series.Experimental results show that:(1)this adaptive HONFI R filter can be successfully used to predict hyperchaotic time series;(2)the pre diction capacities of the HONFIR filter is related to its nonlinear function,but not determined by the HONFIR filter's degree of nonlinearity;(3)the adaptive pr ediction performance of the HONFIR filter is not confined by the Takens embeddin g dimension;(4)the proposed HONFIR filter can have some anti-noise ability.