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Journal of Zhejiang University-SCIENCE A

, Volume 5, Issue 11, pp 1432–1439 | Cite as

Genetic programming-based chaotic time series modeling

  • Wei ZhangEmail author
  • Zhi-ming Wu
  • Gen-ke Yang
Computer & Information Science

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.

Key words

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

Document code

CLC number

TN914 

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Copyright information

© Zhejiang University Press 2004

Authors and Affiliations

  1. 1.Department of AutomationShanghai Jiaotong UniversityShanghaiChina

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