Synchronization of an uncertain chaotic system via recurrent neural networks
Tan Wen (谭文)ab, Wang Yao-Nan (王耀南)b
a Department of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan 411201, China; b College of Electrical and Information Engineering, Hunan University, Changsha 410082, China University, Changsha 410082, China
Abstract Incorporating distributed recurrent networks with high-order connections between neurons, the identification and synchronization problem of an unknown chaotic system in the presence of unmodelled dynamics is investigated. Based on the Lyapunov stability theory, the weights learning algorithm for the recurrent high-order neural network model is presented.Also, analytical results concerning the stability properties of the scheme are obtained. Then adaptive control law for eliminating synchronization error of uncertain chaotic plant is developed via Lyapunov methodology. The proposed scheme is applied to model and synchronize an unknown Rossler system.
Received: 28 May 2004
Revised: 13 September 2004
Accepted manuscript online:
PACS:
0545
Fund: Project supported by the National Natural Science Foundation of China (Grant No 60375001) and by the Hunan Province Natural Science Foundation, China(Grant No 03JJY3107)
Cite this article:
Tan Wen (谭文), Wang Yao-Nan (王耀南) Synchronization of an uncertain chaotic system via recurrent neural networks 2005 Chinese Physics 14 72
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