Global robust exponential stability of interval BAM neural networks with multiple time-varying delays: A direct method based on system solutions

被引:3
作者
Lan, Jinbao [1 ]
Zhang, Xian [1 ,2 ]
Wang, Xin [1 ,2 ]
机构
[1] Heilongjiang Univ, Sch Math Sci, Harbin 150080, Peoples R China
[2] Heilongjiang Univ, Heilongjiang Prov Key Lab Theory & Computat Compl, Harbin 150080, Peoples R China
基金
中国博士后科学基金;
关键词
Interval BAM neural networks; Global robust exponential stability; Multiple time-varying delays; A direct method based on system solutions; SYNCHRONIZATION; CRITERIA; NORM;
D O I
10.1016/j.isatra.2023.11.001
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 analyzes global robust exponential stability of interval bidirectional associative memory (BAM) neural networks with multiple time-varying delays, proposes a direct method based on system solutions, and gives sufficient conditions under which interval BAM neural networks have a unique and globally robustly exponentially stable equilibrium point. This method not only avoids the difficult to set up any Lyapunov-Krasovskii functional, but also derives simpler global robust exponential stability criteria. Compared with the data from other literature, the robust exponential stability criteria obtained in this paper have been presented to have more merits theoretically and numerically.
引用
收藏
页码:145 / 152
页数:8
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