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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

http://doi.org/10.1631/jzus.2004.1432


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.

@article{title="Genetic programming-based chaotic time series modeling",
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journal="Journal of Zhejiang University Science A",
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doi="10.1631/jzus.2004.1432"
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%DOI 10.1631/jzus.2004.1432

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T1 - Genetic programming-based chaotic time series modeling
A1 - ZHANG Wei
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PB - Zhejiang University Press & Springer
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Abstract: 
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

Reference

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[3] Kantz, H., Schreiber, T., 1997. Nonlinear Time Series Analysis. Cambridge University Press.

[4] Kennedy, J., Eberhart, R., 1995. Particle Swarm Optimization. Proc IEEE Int. Conf on Neural Networks, p.1942-1948.

[5] Koza, J.R., 1990. Genetic Programming, A Paradigm for Genetically Breeding Populations of Computer Programs to Solve Problems. Stanford University Report, Report No. STAN-CS-90-1394, http://www.geneticprogramming.com/jkpubs72to93.html#anchor484765.

[6] Leung, H., Varadan, V., 2002. System Modelling and Design Using Genetic Programming. The 1st IEEE International Conference on Cognitive Informatics, Banff, Canada.

[7] Lv, J.H., Lu, J.N., Chen, S.H., 2002. Nonlinear Time Series Analysis and Applications. Wuhan University Press, Wuhan (in Chinese).

[8] Pan, Z.J., Kang, L.S., Chen, Y.T., 1998. Evolutionary Computation. Tsinghua University Press and Guangxi Scientific and Technology Press (in Chinese).

[9] Rosenstein, J.R., Collins, J.J., Luca, C.J., 1993. A practical method for calculating largest Lyapunov exponents from small data sets. Physica D, 65:117-134.

[10] Shi, Y.H., Eberhart, R., 1998. A Modified Particle Swarm Optimizer. Proc IEEE Int. Conf on Evolutionary Computation, p.69-73.

[11] Varadan, V., Leung, H., 2001. Reconstruction of polynomial systems from noisy time series measurements using genetic programming. IEEE Trans. Industrial Electronics, 48(4):742-748.

[12] Xie, X.F., Zhang, W.J., Yang Z.L., 2003. Overview of particle swarm optimization. Control and Decision. 18(2):129-134 (in Chinese).

[13] Wei, R., Lu, J.G., Li, J., Wang, Z.Q., 2002. A new wavelet model for identification of discrete chaotic systems and qualitative analysis of model. Acta Electronica Sinica, 30(1):73-75.

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