一类随机高阶变时滞神经网络的指数稳定性  

Exponential Stability Analysis of a Class Stochastic High-order Neural Networks with Time-varying Delays

作　　者：蒋望东 刘伟[1] 章月红 JIANG Wang-dong;LIU Wei;ZHANG Yue hong(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12期182-192,共11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：对于一类随机高阶变时滞神经网络,应用Brouwer不动点原理和随机分析理论知识,利用Schwarz积分不等式和递推归纳技巧,研究高阶变时滞神经网络在随机扰动下的稳定性,给出其指数稳定判定的充分性条件.最后通过数值例子说明所得结果的有效性.For a class high-order neural networks with time-varying delays,using Brouwer’s fixed-point theorem,stochastic analysis theorem,Schwarz integral inequality and recursive induction techniques to research the stability of a class high-order neural networks with stochastic disturbance term and time-varying delays,the general sufficient conditions for exponential stability are established.Finally,a numerical example is given to illustrate the validity of the results obtained.

关 键 词：随机高阶 变时滞神经网络 Brouwer不动点原理 Schwarz积分不等式 指数稳定性 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] O211.63[自动化与计算机技术—控制科学与工程]