文档介绍：1432 Zhang et al. / J Zhejiang Univ SCI 2004 5(11):1432-1439
Journal of Zhejiang University SCIENCE
ISSN 1009-3095
/jzus
E-mail: ******@zju.
ic programming-based chaotic time series modeling*
ZHANG Wei (张伟)†, WU Zhi-ming (吴智铭), YANG Gen-ke (杨根科)
(Department of Automation, Shanghai Jiaotong University, Shanghai 200030, China)
†E-mail: zhang_******@sjtu.
Received Sept. 18, 2003; revision accepted Dec. 12, 2003
Abstract: This paper proposes a ic 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, ic programming modeling, Nonlinear Parameter Estimation (NPE), Particle
Swarm Optimization (PSO), Nonlinear system identification
doi:.1432 Document code: A CLC number: TN914
INTRODUCTION sion and approaching tools than to be modeling
tools. They are less powerful in revealing the sys-
Chaos is