基于RBF神经网络的分数阶混沌系统的同步  

Synchronization of Fractional Order Chaotic Systems via RBF Neural Network

作　　者：赵小国[1] 阎晓妹[2] 孟欣[1] 

机构地区：[1]西安建筑科技大学机电工程学院,西安710055 [2]西安理工大学自动化与信息工程学院,西安710048

出　　处：《复杂系统与复杂性科学》2010年第1期40-46,共7页Complex Systems and Complexity Science

摘　　要：针对分数阶混沌系统的同步问题,提出一种基于径向基函数(Radial BasisFunction,RBF)神经网络的控制器。利用RBF神经网络对同步误差系统进行补偿控制,神经网络的权值可以在线调整,使得同步误差渐近收敛到零点。基于Lya-punov稳定性理论,分析了该控制器的稳定性。分别以分数阶Chen系统的同步和分数阶Liu系统的同步为例进行了数值仿真,仿真结果验证了所设计的控制器的有效性和鲁棒性。For synchronization of fractional order chaotic systems,a new controller based on RBF（Radial Basis Function,RBF） neural network is presented in this paper.RBF neural network is designed to compensate the nonlinearity in synchronization error systems,and the update law is given to adjust the weights of RBF neural network on-line.The proposed controller can make the synchronization error convergent to zero asymptotically.Based on Lyapunov stability theory,analysis of stability is performed for the proposed controller.Two numerical simulations（synchronization of fractional order Chen system,synchronization of fractional order Liu system） are performed,and the simulation results demonstrate the effectiveness and the robustness of the proposed controller.

关 键 词：分数阶 RBF神经网络 混沌同步 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]