STUDY ON PREDICTION METHODS FOR DYNAMIC SYSTEMS OF NONLINEAR CHAOTIC TIME SERIES

Applied Mathematics and Mechanics (English Edition) ›› 2004, Vol. 25 ›› Issue (6): 605-611.

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STUDY ON PREDICTION METHODS FOR DYNAMIC SYSTEMS OF NONLINEAR CHAOTIC TIME SERIES

马军海^1,3, 陈予恕², 辛宝贵³

1. Management School, Tianjin University, Tianjin 300072, P. R. China;
2. Mechanics Department, Tianjin University, Tianjin 300072, P. R. China;
3. Tianjin University of Finance and Economics, Tianjin 300222, P. R. China

收稿日期:2002-04-09 修回日期:2003-12-23 出版日期:2004-06-18 发布日期:2004-06-18
基金资助:
the National Natural Science Foundation of China(70271071,19990510)

STUDY ON PREDICTION METHODS FOR DYNAMIC SYSTEMS OF NONLINEAR CHAOTIC TIME SERIES

MA Jun-hai^1,3, CHEN Yu-shu², XIN Bao-gui³

1. Management School, Tianjin University, Tianjin 300072, P. R. China;
2. Mechanics Department, Tianjin University, Tianjin 300072, P. R. China;
3. Tianjin University of Finance and Economics, Tianjin 300222, P. R. China

Received:2002-04-09 Revised:2003-12-23 Online:2004-06-18 Published:2004-06-18
Supported by:
the National Natural Science Foundation of China(70271071,19990510)

摘要/Abstract

摘要： The prediction methods for nonlinear dynamic systems which are decided by chaotic time series are mainly studied as well as structures of nonlinear self-related chaotic models and their dimensions. By combining neural networks and wavelet theories, the structures of wavelet transform neural networks were studied and also a wavelet neural networks learning method was given. Based on wavelet networks, a new method for parameter identification was suggested, which can be used selectively to extract different scales of frequency and time in time series in order to realize prediction of tendencies or details of original time series. Through pre-treatment and comparison of results before and after the treatment, several useful conclusions are reached: High accurate identification can be guaranteed by applying wavelet networks to identify parameters of self-related chaotic models and more valid prediction of the chaotic time series including noise can be achieved accordingly.

Abstract: The prediction methods for nonlinear dynamic systems which are decided by chaotic time series are mainly studied as well as structures of nonlinear self-related chaotic models and their dimensions. By combining neural networks and wavelet theories, the structures of wavelet transform neural networks were studied and also a wavelet neural networks learning method was given. Based on wavelet networks, a new method for parameter identification was suggested, which can be used selectively to extract different scales of frequency and time in time series in order to realize prediction of tendencies or details of original time series. Through pre-treatment and comparison of results before and after the treatment, several useful conclusions are reached: High accurate identification can be guaranteed by applying wavelet networks to identify parameters of self-related chaotic models and more valid prediction of the chaotic time series including noise can be achieved accordingly.

中图分类号:

马军海;陈予恕;辛宝贵. STUDY ON PREDICTION METHODS FOR DYNAMIC SYSTEMS OF NONLINEAR CHAOTIC TIME SERIES[J]. Applied Mathematics and Mechanics (English Edition), 2004, 25(6): 605-611.

MA Jun-hai;CHEN Yu-shu;XIN Bao-gui. STUDY ON PREDICTION METHODS FOR DYNAMIC SYSTEMS OF NONLINEAR CHAOTIC TIME SERIES[J]. Applied Mathematics and Mechanics (English Edition), 2004, 25(6): 605-611.

参考文献

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STUDY ON PREDICTION METHODS FOR DYNAMIC SYSTEMS OF NONLINEAR CHAOTIC TIME SERIES

STUDY ON PREDICTION METHODS FOR DYNAMIC SYSTEMS OF NONLINEAR CHAOTIC TIME SERIES

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