文献类型：期刊文献

中文题名：一类具有惯性随机时滞神经网络的指数同步

英文题名：Exponential Synchronization of Stochastic Neural Networks with Inertial and Time Delay

作者：李志英[1]

机构：[1]绍兴文理学院元培学院公共基础教育分院,浙江绍兴312000

年份：2021

卷号：51

期号：14

起止页码：218

中文期刊名：数学的实践与认识

外文期刊名：Mathematics in Practice and Theory

收录：CSTPCD

基金：绍兴文理学院元培学院院级科研项目(KY2020C01);绍兴文理学院校级科研项目(2020LG1009)。

语种：中文

中文关键词：惯性随机时滞神经网络;Ito积分;Lyapunov函数;微积分性质;指数同步

外文关键词：stochastic neural networks with inertial;Ito formula;Lyapunov function;properties of calculus;exponential synchronization

中文摘要：研究一类具有惯性随机时滞神经网络的指数同步.首先,根据同步概念构造受控的响应系统,得到相应的误差系统.其次,引入适当的变量替换将二阶微分系统变换为一阶微分系统.利用Ito积分性质,微分算子,分别采用构造Lyapunov函数和直接应用微积分有关性质的方法,给出了判定其指数同步稳定的两个不同充分条件,最后通过两个数值例子说明所得结果容易验证.

外文摘要：The exponential synchronization of a stochastic neural network with inertial and time delay is investigated.First,a controller is proposed to guarantee the exponential synchronization between the driving and the driven neural networks.Then,under appropriate variable substitution,the second order differential system is shifted to a first order differential system.Based on differential operator and Ito formula,the Lyapunov function and the properties of calculus are separately used to prove two theorems of sufficient conditions for the exponential synchronization.At last,two illustrative examples are given to demonstrate the effectiveness of the theorems.