CLC number: TN914
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 0000-00-00
Cited: 6
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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",
author="ZHANG Wei, WU Zhi-ming, YANG Gen-ke",
journal="Journal of Zhejiang University Science A",
volume="5",
number="11",
pages="1432-1439",
year="2004",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2004.1432"
}
%0 Journal Article
%T Genetic programming-based chaotic time series modeling
%A ZHANG Wei
%A WU Zhi-ming
%A YANG Gen-ke
%J Journal of Zhejiang University SCIENCE A
%V 5
%N 11
%P 1432-1439
%@ 1869-1951
%D 2004
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2004.1432
TY - JOUR
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
IS - 11
SP - 1432
EP - 1439
%@ 1869-1951
Y1 - 2004
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2004.1432
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.
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