Genetic programming-based chaotic time series modeling

被引：8
|
作者：
Zhang W. [1 ]
Wu Z.-M. [1 ]
Yang G.-K. [1 ]
机构：
[1] Dept. of Automat., Shanghai Jiaotong Univ.
来源：
Journal of Zhejiang University-SCIENCE A | 2004年 / 5卷 / 11期
基金：
中国国家自然科学基金;
关键词：
Chaotic time series analysis; Genetic programming modeling; Nonlinear parameter estimation (NPE); Nonlinear system identification; Particle swarm optimization (PSO);
D O I：
10.1631/jzus.2004.1432
中图分类号：
学科分类号：
摘要：
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.
引用
收藏
页码：1432 / 1439
页数：7
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