文献类型：期刊文献

英文题名：Stochastic asymptotic stability for stochastic inertial Cohen-Grossberg neural networks with time-varying delay

作者：Xu, Danning[1];Liu, Wei[1]

机构：[1]Shaoxing Univ, Yuanpei Coll, Shaoxing 312000, Zhejiang, Peoples R China

年份：2023

卷号：23

期号：2

起止页码：921

外文期刊名：JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING

收录：EI(收录号：20231413859087)、ESCI(收录号：WOS:000964733000028)、Scopus(收录号：2-s2.0-85151552650)、WOS

基金：The authors acknowledge the Project Foundation of Zhejiang Provincial Department of Education (Grant: Y202145903), Project Foundation of Shaoxing University (Grant: 2020LG1009).

语种：英文

外文关键词：Stochastic inertial Cohen-Grossberg neural networks; Time-varying delay; Homeomorphic mapping; Linear matrix inequality; Stochastic asymptotic stability

外文摘要：This paper studies stochastic asymptotic stability for stochastic inertial Cohen-Grossberg neural networks with time-varying delay. Firstly, the second-order differential equation is converted into the first-order differential equation by appropriate variable substitution. Secondly, the existence of the equilibrium point is derived by using homeomorphic mapping, finite increment formula of Lagrange mean value theorem and linear matrix inequality. The sufficient conditions for the stochastic asymptotic stability of the equilibrium point of the system are derived by defining the appropriate operator, and constructing the appropriate positive Lyapunov function and positive-definite matrix. Thirdly, a numerical example illustrates the correctness of these theorems.