一类时滞混沌神经网络的全局渐近同步  

Global Asymptotic Synchronization of a Class of Delayed Chaotic Neural Networks

作　　者：王占山[1] 张化光[1] 王智良[1] 

机构地区：[1]东北大学信息科学与工程学院,辽宁沈阳110004

出　　处：《东北大学学报（自然科学版）》2006年第6期598-601,共4页Journal of Northeastern University(Natural Science)

基　　金：国家自然科学基金资助项目(605340106057207060521003);教育部长江学者及创新团队计划项目

摘　　要：应用驱动—响应同步方法,研究了一类时滞混沌神经网络的全局渐近同步问题.基于分散控制策略,通过构造适当的Lyapunov-Krasovskii泛函,给出了保证两个具有相同结构但初始条件不相同的时滞混沌神经网络全局渐近同步的控制律设计方法.所得到的控制律不仅易于实现,而且具有高可靠性等特点,克服了传统的集中控制的不足.仿真示例验证了该方法的有效性.The global asymptotic synchronization of a class of delayed chaotic neural networks is studied on the basis of driveresponse synchronization. Constructing a suitable Lyapunov-Krasovskii functional and based on the scheme of decentralized control, the design of a control law is proposed to ensure the global asymptotic synchronization of state trajectories of two chaotic neural networks of which the structure are the same and the initial conditions are different. In this way the control law obtained is not only easy to be implemented but highly reliable in practice, thus making up for the inadequacy of conventional centralized control. Two illustrative examples are used to demonstrate the effectiveness of the proposed method.

关 键 词：混沌神经网络 同步 驱动-响应法 分散控制 LYAPUNOV-KRASOVSKII泛函 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]