一类含连续分布时滞的随机Hopfiled神经网络模型的几乎必然指数稳定性和p阶矩指数稳定性  

The Almost Sure Exponential Stability and pth Moment Exponential Stability of Stochastic Hopfield Neural Networks with Continuously Distributed Delays

作　　者：赵亮[1] 李树勇[1] 张秀英[1] 杜启凤[1] 

机构地区：[1]四川师范大学数学与软件科学学院,四川成都610066

出　　处：《四川师范大学学报（自然科学版）》2013年第3期317-323,共7页Journal of Sichuan Normal University（Natural Science）

基　　金：国家自然科学基金(10971147);高等学校博士学科点专项基金(11271270);四川省应用基础研究基金(2009JY0066)资助项目

摘　　要：考虑一类含连续分布时滞的随机Hopfiled神经网络模型的几乎必然指数稳定性和p阶矩指数稳定性,借助创建Lyapunov函数和运用非负半鞅收敛定理得到了该网络模型平凡解几乎必然指数稳定和p阶矩指数稳定的充分条件,并通过2个例子,说明结果的有效性.In this paper, the almost sure exponential stability and the pth moment exponential stability of stochastic Hopfield neural networks with continuously distributed delays are discussed. A sufficient condition to guarantee the almost sure exponential stability and the pth moment exponential stability of an equilibrium solution is given by using the Lyapunov function and the nonnegative semimartin- gale convergence theorem. Two examples are given to illustrate the applicability of the obtained resuh.

关 键 词：分布时滞 随机Hopfiled神经网络模型 非负半鞅收敛定理 几乎必然指数稳定 p阶矩指数稳定 

分 类 号：O175.26[理学—数学]