State Bounding of Memristive Cohen-Grossberg Neural Networks with Mixed Delays

被引：0
|
作者：
Su, Meng [1 ,2 ]
Xue, Yu [1 ,2 ]
Zhang, Xian [1 ,2 ]
机构：
[1] Heilongjiang Univ, Sch Math Sci, Harbin, Peoples R China
[2] Heilongjiang Univ, Heilongjiang Prov Key Lab Theory & Computat Compl, Harbin, Peoples R China
来源：
2023 35TH CHINESE CONTROL AND DECISION CONFERENCE, CCDC | 2023年
关键词：
Memristive Cohen-Grossberg neural networks; state bounding; reachable set estimation; global exponential stability; mixed delays; bounded disturbances; REACHABLE SET ESTIMATION; GLOBAL EXPONENTIAL STABILITY; TIME SYNCHRONIZATION; FINITE-TIME; SYSTEMS;
D O I：
10.1109/CCDC58219.2023.10327031
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper concerns the state bounding problem for a class of memristive Cohen-Grossberg neural networks with mixed delays and bounded disturbances. A delay-dependent sufficient condition, which is presented in terms of system parameters and contains several simple inequalities, is first given to guarantee that the state trajectories remain inside or converge exponentially into a polytope. Furthermore, based on the state bounding results, reachable set estimation and global exponential stability criterion are obtained. Finally, a numerical simulation illustrates the effectiveness of the obtained theoretical results.
引用
收藏
页码：4830 / 4835
页数：6
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