%0 Journal Article %1 WeiZhang:2004:JZUS %A Zhang, Wei %A ming Wu, Zhi %A ke Yang, Gen %D 2004 %J Journal of Zhejiang University Science %K (NPE), Chaotic Estimation Genetic Nonlinear Optimization, PSO, Parameter Particle Swarm algorithms, analysis, genetic identification modelling, programming programming, series system time %N 11 %P 1432--1439 %R doi:10.1631/jzus.2004.1432 %T Genetic programming-based chaotic time series modeling %U http://www.zju.edu.cn/jzus/2004/0411/041118.pdf %V 5 %X 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.

@article{WeiZhang:2004:JZUS, 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.}, added-at = {2008-06-19T17:46:40.000+0200}, author = {Zhang, Wei and ming Wu, Zhi and ke Yang, Gen}, biburl = {https://www.bibsonomy.org/bibtex/21470f48480a1ceeb482bd6e8ac39d6ff/brazovayeye}, doi = {doi:10.1631/jzus.2004.1432}, interhash = {275222161f04f6ecc342732e8603e447}, intrahash = {1470f48480a1ceeb482bd6e8ac39d6ff}, issn = {1009-3095}, journal = {Journal of Zhejiang University Science}, keywords = {(NPE), Chaotic Estimation Genetic Nonlinear Optimization, PSO, Parameter Particle Swarm algorithms, analysis, genetic identification modelling, programming programming, series system time}, notes = {Department of Automation, Shanghai Jiaotong University, Shanghai 200030, China JZUS http://www.zju.edu.cn/jzus Document code: A CLC number: TN914 chaotic Chebyshev-map}, number = 11, pages = {1432--1439}, size = {8 pages}, timestamp = {2008-06-19T17:55:47.000+0200}, title = {Genetic programming-based chaotic time series modeling}, url = {http://www.zju.edu.cn/jzus/2004/0411/041118.pdf}, volume = 5, year = 2004 }