区间时变细胞神经网络的全局鲁棒指数稳定性

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．

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

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.