一类随机惯性时滞BAM神经网络的指数稳定性  

Exponential Stability of Stochastic and Intertial BAM Neural Networks with Time Delays

作　　者：章月红 蒋望东 刘伟[1] ZHANG Yue-hong;JIANG Wang-dong;LIU Wei(Yuanpei College,Shaoxing University,Shaoxing 312000,China)

机构地区：[1]绍兴文理学院元培学院,浙江绍兴312000

出　　处：《数学的实践与认识》2020年第13期209-220,共12页Mathematics in Practice and Theory

基　　金：教育部产学合作协同育人项目(201801123017);浙江省高等教育教学改革研究项目(JG20160261);绍兴市高等教育教学改革研究项目(SXSJG201833)。

摘　　要：研究一类随机惯性时滞BAM神经网络,通过引入变量替换将二阶微分系统变换为一阶微分系统.利用微分方程有关知识,Schwarz不等式、Ito积分性质和递推归纳,讨论其系统解的有界性和指数稳定性,并给出其判定的充分条件,最后给出数值例子说明所得结果容易验证.This paper discuss a kind of stochastic and intertial BAM neural networks with time delays,we introduce variable transformation,the second-order differential equation is transformed into a first-order differential equation.Using related properties of differential equations,Schwarz inequality and Ito formula,we discuss the Boundedness and exponential stability of the Solutions,and give sufficient conditions for its judgement.At last,numerical examples are given to show that the results are easy to verify.

关 键 词：随机惯性时滞BAM神经网络 SCHWARZ不等式 Ito积分 有界性 指数稳定 

分 类 号：O175[理学—数学] TP183[理学—基础数学]