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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Project (Nos. 60174009 and 70071017) supported by the National Natural Science Foundation of China
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Zhang, W., Wu, Zm. & Yang, Gk. Genetic programming-based chaotic time series modeling. J. Zheijang Univ.-Sci. 5, 1432–1439 (2004). https://doi.org/10.1631/jzus.2004.1432
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DOI: https://doi.org/10.1631/jzus.2004.1432
Key words
- Chaotic time series analysis
- Genetic programming modeling
- Nonlinear Parameter Estimation (NPE)
- Particle Swarm Optimization (PSO)
- Nonlinear system identification