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CLC number: TN914

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Received: 2003-09-18

Revision Accepted: 2003-12-12

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Journal of Zhejiang University SCIENCE A 2004 Vol.5 No.11 P.1432~1439


Genetic programming-based chaotic time series modeling

Author(s):  ZHANG Wei, WU Zhi-ming, YANG Gen-ke

Affiliation(s):  Department of Automation, Shanghai Jiaotong University, Shanghai 200030, China

Corresponding email(s):   zhang_wi@sjtu.edu.cn

Key Words:  Chaotic time series analysis, Genetic programming modeling, Nonlinear Parameter Estimation (NPE), Particle Swarm Optimization (PSO), Nonlinear system identification

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ZHANG Wei, WU Zhi-ming, YANG Gen-ke. Genetic programming-based chaotic time series modeling[J]. Journal of Zhejiang University Science A, 2004, 5(11): 1432~1439.

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author="ZHANG Wei, WU Zhi-ming, YANG Gen-ke",
journal="Journal of Zhejiang University Science A",
publisher="Zhejiang University Press & Springer",

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%DOI 10.1631/jzus.2004.1432

T1 - Genetic programming-based chaotic time series modeling
A1 - ZHANG Wei
A1 - WU Zhi-ming
A1 - YANG Gen-ke
J0 - Journal of Zhejiang University Science A
VL - 5
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SP - 1432
EP - 1439
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Y1 - 2004
PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.2004.1432

This paper proposes a Genetic Programming-Based Modeling (GPM) algorithm on chaotic time series. GP is used here to search for appropriate model structures in function space, and the particle Swarm Optimization (PSO) algorithm is used for nonlinear Parameter Estimation (NPE) of dynamic model structures. In addition, GPM integrates the results of Nonlinear Time Series Analysis (NTSA) to adjust the parameters and takes them as the criteria of established models. Experiments showed the effectiveness of such improvements on chaotic time series modeling.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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yan wang@shanghai jiaotong unversity<wangyan8383@sjtu.edu.cn>

2012-02-18 14:04:11

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