Chaotic behavior learning of Chua's circuit

doi:10.1088/1674-1056/21/10/100502

中国物理B ›› 2012, Vol. 21 ›› Issue (10): 100502-100502.doi: 10.1088/1674-1056/21/10/100502

Chaotic behavior learning of Chua's circuit

孙建成

School of Software and Communication Engineering, Jiangxi University of Finance and Economics, Nanchang 330013, China

收稿日期:2012-03-08 修回日期:2012-04-20 出版日期:2012-09-01 发布日期:2012-09-01
基金资助:
Project supported by the National Natural Science Foundation of China (Grant No. 61072103) and the Jiangxi Province Training Program for Younger Scientists.

Chaotic behavior learning of Chua's circuit

Sun Jian-Cheng (孙建成)

School of Software and Communication Engineering, Jiangxi University of Finance and Economics, Nanchang 330013, China

Received:2012-03-08 Revised:2012-04-20 Online:2012-09-01 Published:2012-09-01
Contact: Sun Jian-Cheng E-mail:sunjc73@gmail.com
Supported by:
Project supported by the National Natural Science Foundation of China (Grant No. 61072103) and the Jiangxi Province Training Program for Younger Scientists.

摘要/Abstract

摘要： 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.

关键词: chaotic behavior, Chua's circuit, support vector machines

Abstract: 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.

Key words: chaotic behavior, Chua's circuit, support vector machines

中图分类号: (Nonlinear dynamics and chaos)

05.45.-a

05.45.Tp (Time series analysis)

孙建成. Chaotic behavior learning of Chua's circuit[J]. 中国物理B, 2012, 21(10): 100502-100502.

Sun Jian-Cheng (孙建成). Chaotic behavior learning of Chua's circuit[J]. Chin. Phys. B, 2012, 21(10): 100502-100502.

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Chaotic behavior learning of Chua's circuit

Chaotic behavior learning of Chua's circuit

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