具有分段常数参数的Cohen-Grossberg神经网络的全局指数稳定  

Global exponential stability of Cohen-Grossberg neural network with piecewise constant argument

作　　者：谢涛 郑文晴 XIE Tao;ZHENG Wenqing(College of Mathematics and Statistics,Hubei Normal University,Huangshi 435002,China)

机构地区：[1]湖北师范大学数学与统计学院,湖北黄石435002

出　　处：《湖北师范大学学报（自然科学版）》2024年第1期9-15,共7页Journal of Hubei Normal University：Natural Science

摘　　要：主要研究了具有分段常数参数的Cohen-Grossberg神经网络的全局指数稳定性,得到了保证平衡点的存在唯一性的充分条件,通过线性不等式方法,并构造适当的李雅普诺夫泛函,提出了系统的平衡点的全局指数稳定性的判据,最后给了一个数值例子验证所得结果的有效性。This paper mainly probed into the global exponential stability of the Cohen-Grossberg neural network with piecewise constant argument to obtainsufficient conditions for the existence and uniqueness of the equilibrium point.By using linear inequality method and constructing the appropriate Lyapunov functional,it puts forward the criterion to identify the global exponential stability of the equilibrium point of the system.Finally,it gave a numerical example to verify the validity of the obtained results.

关 键 词：COHEN-GROSSBERG神经网络 分段常数参数 李雅普诺夫函数 全局指数稳定性 

分 类 号：O193[理学—数学]