Multiple Mittag-Leffler Stability of Delayed Fractional-Order Cohen-Grossberg Neural Networks via Mixed Monotone Operator Pair

被引：42
作者：
Zhang, Fanghai [1 ,2 ]
Zeng, Zhigang [1 ,3 ]
机构：
[1] Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automat, Wuhan 430074, Peoples R China
[2] Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Wuhan 430074, Peoples R China
[3] Huazhong Univ Sci & Technol, Educ Minist China, Key Lab Image Proc & Intelligent Control, Wuhan 430074, Peoples R China
来源：
IEEE TRANSACTIONS ON CYBERNETICS | 2021年 / 51卷 / 12期
基金：
中国国家自然科学基金; 中国博士后科学基金;
关键词：
Neural networks; Stability criteria; Delays; Asymptotic stability; Numerical stability; MONOS devices; Fractional-order Cohen-Grossberg neural networks; Mittag-Leffler stability; mixed monotone operator pair; time-varying delays; MULTISTABILITY ANALYSIS; ACTIVATION FUNCTIONS; DIFFERENTIAL-EQUATIONS; ABSOLUTE STABILITY; GENERAL-CLASS;
D O I：
10.1109/TCYB.2019.2963034
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 article mainly investigates the multiple Mittag-Leffler stability of delayed fractional-order Cohen-Grossberg neural networks with time-varying delays. By using mixed monotone operator pair, the conditions of the coexistence of multiple equilibrium points are obtained for fractional-order Cohen-Grossberg neural networks, and these conditions are eventually transformed into algebraic inequalities based on the vertex of the divided region. In particular, when the symbols of these inequalities are determined by the dominant term, several verifiable corollaries are given. And then, the sufficient conditions of the Mittag-Leffler stability are derived for fractional-order Cohen-Grossberg neural networks with time-varying delays. In addition, two numerical examples are provided to illustrate the effectiveness of the theoretical results.
引用
收藏
页码：6333 / 6344
页数：12
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