具有leakage时滞与随机干扰的离散神经网络渐近稳定性分析  

Stability Analysis on Discrete-Time Stochastic Neural Networks with Leakage Delay

作　　者：耿立杰[1] 李彦路[2] 徐瑞[1] 

机构地区：[1]军械工程学院数学研究所,河北石家庄050003 [2]军械工程学院训练部,河北石家庄050003

出　　处：《生物数学学报》2013年第4期649-655,共7页Journal of Biomathematics

基　　金：国家自然科学基金项目支持(11071254)

摘　　要：研究了一类具有leakage时滞与随机干扰的离散型神经网络的全局渐近稳定性问题.利用一种新的时滞分割方法将时滞区间分割为多个区间.通过构造新的Lyapunov泛函得到了基于线性矩阵不等式(LMI)的渐近稳定性判据.该判据在获得更小的保守性同时也降低了计算的复杂度.In this paper, the global asymptotic stability analysis for a class of discrete-time stochastic neural networks with leakage delays is investigated. Based on an novel delay-partitioning approach, the variation interval of delay was divided into several subintervals and By employing a novel Lyapunov-Krasvskii functional, a new stability criterion is achieved in terms of linear matrix inequalities （LMI）. The result is less conservative and, meanwhile, the computational complexity is reduced.

关 键 词：离散型随机神经网络 时滞分割 leakage时滞 全局渐近稳定性 线性矩阵不等式 

分 类 号：O175[理学—数学]