有界函数
独特性
人工神经网络
常量(计算机编程)
非线性系统
区间(图论)
数学
理论(学习稳定性)
新颖性
控制理论(社会学)
计算机科学
应用数学
人工智能
数学分析
组合数学
机器学习
控制(管理)
哲学
物理
程序设计语言
量子力学
神学
作者
Muhammet Mert Ketencigil,Ozlem Faydasicok,Sabri Arik
出处
期刊:Discrete and Continuous Dynamical Systems - Series S
[American Institute of Mathematical Sciences]
日期:2022-01-01
卷期号:15 (11): 3189-3189
被引量:3
标识
DOI:10.3934/dcdss.2022081
摘要
<p style='text-indent:20px;'>This research paper deals with the investigation of global robust stability results for Cohen-Grossberg neural networks involving the multiple constant time delays. The activation functions in this neural network model are supposed to be in the set of non-decreasing slope-bounded nonlinear functions and the uncertainties in the constant network parameters are considered to have bounded upper norms. By employing a proper positive definite Lyapunov-type functional and using homeomorphism mapping theory, we propose some novel sets of novel conditions that assure both existence, uniqueness and global robust asymptotic stability of equilibrium points of this nonlinear Cohen-Grossberg-type neural network model involving the multiple time delays. The derived robustly stable conditions mainly rely on examining some proper relationships that are imposed on constant valued interconnection matrices of this delayed neural network. These stability conditions can be certainly verified by employing various simple and useful properties of real interval matrices. Some comparisons are made to address the key advantages of these novel criteria over previously reported corresponding results. An instructive example is also examined to observe novelty of these proposed criteria.</p>
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