一类离散时滞静态神经网络系统的时滞相依全局渐近稳定性分析  

Analysis on the Delay-Dependent Globally Asymptotically Stability of the Static Neural Networks with Discrete Time-Delay

作　　者：毛凯 杨树杰 刘丹 MAO Kai;YANG Shujie;LIU Dan(Teaching and Research Section of Mathematics,College of Basic Science for Aviation,Naval Aviation University,Yantai 264001,China)

机构地区：[1]海军航空大学航空基础学院数学教研室,山东烟台264001

出　　处：《重庆理工大学学报（自然科学）》2020年第3期210-217,共8页Journal of Chongqing University of Technology：Natural Science

基　　金：国家自然科学基金项目(11802338)。

摘　　要：研究了一类时滞静态神经网络系统的时滞相依全局渐近稳定性条件。通过构造一个考虑了更多时滞状态信息的增广Lyapunov-Krasovskii泛函,利用时滞分割方法并结合Jensen积分不等式和自由权矩阵,获得以LMI形式表达的且保守性更低的一类具有定常时滞的静态神经网络的时滞相依全局渐近稳定性判定条件,并给出实例验证结果的有效性和更低的保守性。The globally asymptotic stability for static neural networks with time delay is concerned in this paper.By delay fractitioning approach and taking more delayed-state variables into account,a newly augmented Lyapunov-Krasovskii Functional was constructed.Together with the Jensen integral inequality and free weighing matrix method,a delay-dependent global stability criterion was obtained.It is less conservative than some existing ones.Example is provided to show the effectiveness and reduced conservatism of the proposed results.

关 键 词：静态神经网络 LYAPUNOV-KRASOVSKII泛函 时滞分割 自由权矩阵 LMI 

分 类 号：O175.13[理学—数学]