Chaotic behavior learning of Chua's circuit

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

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.

Keywords: chaotic behavior Chua's circuit support vector machines

Received: 08 March 2012
Revised: 20 April 2012
Accepted manuscript online:

PACS:	05.45.-a	(Nonlinear dynamics and chaos)
	05.45.Tp	(Time series analysis)

Fund: Project supported by the National Natural Science Foundation of China (Grant No. 61072103) and the Jiangxi Province Training Program for Younger Scientists.

Corresponding Authors: Sun Jian-Cheng
E-mail: sunjc73@gmail.com

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Sun Jian-Cheng (孙建成) Chaotic behavior learning of Chua's circuit 2012 Chin. Phys. B 21 100502

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