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.
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.
作者
李志英
LI Zhi-ying(Yuanpei College,Shaoxing University,Shaoxing 312000,China)
出处
《数学的实践与认识》
2021年第14期218-230,共13页
Mathematics in Practice and Theory
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)。
