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Synchronization of chaos using radial basis functions neural networks
作者单位:Ren Haipeng(School of Automation and Information Engineering, Xi'an Univ. of Technology, Xi'an 710048, P.R.China) ;Liu Ding(School of Automation and Information Engineering, Xi'an Univ. of Technology, Xi'an 710048, P.R.China) ;
基金项目:山西省自然科学基金;中国博士后科学基金
摘    要:The Radial Basis Functions Neural Network (RBFNN) is used to establish the model of a response system through the input and output data of the system. The synchronization between a drive system and the response system can be implemented by employing the RBFNN model and state feedback control. In this case, the exact mathematical model, which is the precondition for the conventional method, is unnecessary for implementing synchronization. The effect of the model error is investigated and a corresponding theorem is developed. The effect of the parameter perturbations and the measurement noise is investigated through simulations. The simulation results under different conditions show the effectiveness of the method.

关 键 词:径向基函数  神经网络  混沌  同步化
收稿时间:2 June 2005. 

Synchronization of chaos using radial basis functions neural networks
Ren Haipeng,Liu Ding. Synchronization of chaos using radial basis functions neural networks[J]. Journal of Systems Engineering and Electronics, 2007, 18(1): 83-88. DOI: 10.1016/S1004-4132(07)60056-5
Authors:Ren Haipeng  Liu Ding
Affiliation:School of Automation and Information Engineering, Xi'an Univ. of Technology, Xi'an 710048, P.R.China
Abstract:The Radial Basis Functions Neural Network (RBFNN) is used to establish the model of a response system through the input and output data of the system. The synchronization between a drive system and the response system can be implemented by employing the RBFNN model and state feedback control. In this case, the exact mathematical model, which is the precondition for the conventional method, is unnecessary for implementing synchronization. The effect of the model error is investigated and a corresponding theorem is developed. The effect of the parameter perturbations and the measurement noise is investigated through simulations. The simulation results under different conditions show the effectiveness of the method.
Keywords:Chaos synchronization  Radial basis function neural networks  Model error  Parameter perturbation  Measurement noise
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