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PubMed · 15495338

Genetic programming-based chaotic time series modeling.

Wei Zhang·Zhi-ming Wu·Gen-ke Yang
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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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AlgorithmsArtificial IntelligenceModels, BiologicalNonlinear DynamicsTime FactorsEvaluation StudyJournal ArticleResearch Support, Non-U.S. Gov't

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Wei Zhang, Zhi-ming Wu, Gen-ke Yang. 2004. Genetic programming-based chaotic time series modeling.. https://doi.org/10.1631/jzus.2004.1432

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