Periodic solution problems of neutral-type stochastic neural networks with time-varying delays

被引：1
|
作者：
Zheng, Famei [1 ]
Li, Xiaoliang [2 ]
Du, Bo [1 ]
机构：
[1] Huaiyin Normal Univ, Sch Math & Stat, Huaian, Jiangsu, Peoples R China
[2] Zhejiang Agr & Forestry Univ, Jiyang Coll, Zhuji, Zhejiang, Peoples R China
来源：
FRONTIERS IN PHYSICS | 2024年 / 12卷
基金：
中国国家自然科学基金;
关键词：
periodic solution; stochastic; neutral-type neural networks; existence; exponential stability; NONAUTONOMOUS LOGISTIC EQUATION; ROBUST-STABILITY-CRITERIA; SYNCHRONIZATION;
D O I：
10.3389/fphy.2024.1338799
中图分类号：
O4 [物理学];
学科分类号：
0702 ;
摘要：
This paper is devoted to investigating a class of stochastic neutral-type neural networks with delays. By using the fixed point theorem and the properties of neutral-type operator, we obtain the existence conditions for periodic solutions of stochastic neutral-type neural networks. Furthermore, we obtain the conditions for the exponential stability of periodic solutions using Gronwall-Bellman inequality and stochastic analysis technique. Finally, a numerical example is given to show the effectiveness and merits of the present results. Our results can be used to obtain the existence and exponential stability of periodic solution to the corresponding deterministic systems.
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页数：8
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