Identification of chaotic systems with noisy data based on RBF neural networks

In this paper, we present that noisy chaotic systems can be identified with RBF neural networks. We design three-layers RBF network structure and clarify fundamental properties of RBF networks to learn noisy chaotic systems by some numerical experiments. We also evaluate the identified models with r...

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Bibliographic Details
Published in2009 International Conference on Machine Learning and Cybernetics Vol. 5; pp. 2578 - 2581
Main Authors Dong-Mei Li, Fa-Chao Li
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.07.2009
Subjects
Chaos
Chaotic systems identification
Conference management
Convergence
Cybernetics
Electronic mail
Machine learning
Neural networks
Noisy chaotic systems
Radial basis function networks
Rbf neural networks
System identification
Technology management
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Summary:In this paper, we present that noisy chaotic systems can be identified with RBF neural networks. We design three-layers RBF network structure and clarify fundamental properties of RBF networks to learn noisy chaotic systems by some numerical experiments. We also evaluate the identified models with reconstruction of attractors by the identified models. Simulations show that the identified models can approach to original chaotic systems and extract dynamical characteristics of original chaotic systems.
ISBN:9781424437023
1424437024
ISSN:2160-133X
DOI:10.1109/ICMLC.2009.5212655