Chaotic behavior learning of Chua's circuit

Least-square support vector machines (LS-SVM) are applied for learning the chaotic behavior of Chua's circuit. The system is divided into three multiple-input single-output (MISO) structures and the LS-SVM are trained individually. Comparing with classical approaches, the proposed one reduces t...

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Bibliographic Details
Published inChinese physics B Vol. 21; no. 10; pp. 100502 - 1-100502-7
Main Author Sun, Jian-Cheng
Format Journal Article
LanguageEnglish
Published 01.10.2012
Subjects
Chaos theory
Circuits
Learning
Least squares method
Links
Mathematical models
State (computer science)
Support vector machines
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Summary:Least-square support vector machines (LS-SVM) are applied for learning the chaotic behavior of Chua's circuit. The system is divided into three multiple-input single-output (MISO) structures and the LS-SVM are trained individually. Comparing with classical approaches, the proposed one reduces the structural complexity and the selection of parameters is avoided. Some parameters of the attractor are used to compare the chaotic behavior of the reconstructed and the original systems for model validation. Results show that the LS-SVM combined with the MISO can be trained to identify the underlying link among Chua's circuit state variables, and exhibit the chaotic attractors under the autonomous working mode.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:1674-1056
1741-4199
DOI:10.1088/1674-1056/21/10/100502