人工神经网络
非线性系统
指数稳定性
随机神经网络
指数函数
理论(学习稳定性)
计算机科学
班级(哲学)
控制理论(社会学)
应用数学
数学
循环神经网络
人工智能
数学分析
物理
机器学习
控制(管理)
量子力学
作者
Wenqi Wu,Li Yang,Yaping Ren
出处
期刊:International Journal of Nonlinear Sciences and Numerical Simulation
[De Gruyter]
日期:2020-08-10
卷期号:22 (1): 13-21
被引量:4
标识
DOI:10.1515/ijnsns-2019-0142
摘要
Abstract This paper is concerned with the periodic solutions for a class of stochastic Cohen–Grossberg neural networks with time-varying delays. Since there is a non-linearity in the leakage terms of stochastic Cohen–Grossberg neural networks, some techniques are needed to overcome the difficulty in dealing with the nonlinearity. By applying fixed points principle and Gronwall–Bellman inequality, some sufficient conditions on the existence and exponential stability of periodic solution for the stochastic neural networks are established. Moreover, a numerical example is presented to validate the theoretical results. Our results are also applicable to the existence and exponential stability of periodic solution for the corresponding deterministic systems.
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