区间时变细胞神经网络的全局鲁棒指数稳定性  

Global robust exponential stability of interval cellular neural networks with time-varying delays

作　　者：吴立刚[1] 王常虹[1] 曾庆双[1] 

机构地区：[1]哈尔滨工业大学空间控制与惯性技术研究中心,黑龙江哈尔滨150001

出　　处：《控制理论与应用》2006年第5期724-729,共6页Control Theory & Applications

基　　金：国家自然科学基金资助项目(69874008).

摘　　要：研究了一类区间时变扰动、变时滞细胞神经网络的全局鲁棒指数稳定性问题．利用Leibniz-Newton公式对原系统进行模型变换,并分析了变换模型和原始模型的等价性．基于变换模型,运用线性矩阵不等式的方法,通过选择适当的Lyapunov-Krasovskii泛函,推导了该系统全局鲁棒指数稳定的时滞相关的充分条件．通过数值实例将所得结果与前人的结果相比较,表明了本文所提出的稳定判据具有更低的保守性．The global robust exponential stability （GRES） of a class of interval cellular neural networks with time- varying delays is studied in this paper, A transformation is made on original system by the Leibniz-Newton formula, an analysis is also given to show that those two systems are equivalent. Based on the transformed model, applying Lyapunov- Krasovskii stability theorem for functional differential equations and the linear matrix inequality （LMI） approach, some delay-dependent criteria are respectively presented for the existence, uniqueness, and global robust exponential stability of the equilibrium for the interval delayed neural networks, The criteria given here are less conservative than those provided in the earlier references, Finally, numerical example is included to demonstrate the effectiveness and superiority of the proposed results.

关 键 词：细胞神经网络 变时滞 全局指数稳定 鲁棒性 线性矩阵不等式(LMI) 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TP13[自动化与计算机技术—控制科学与工程]