Multistability of delayed fractional-order competitive neural networks

被引：44
作者：
Zhang, Fanghai [1 ,2 ]
Huang, Tingwen [3 ]
Wu, Qiujie [4 ]
Zeng, Zhigang [1 ,2 ]
机构：
[1] Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automat, Wuhan, Peoples R China
[2] Educ Minist China, Key Lab Image Proc & Intelligent Control, Wuhan, Peoples R China
[3] Texas A&M Univ Qatar, Sci Program, Doha, Qatar
[4] Anhui Univ, Sch Internet, Hefei, Peoples R China
来源：
NEURAL NETWORKS | 2021年 / 140卷
基金：
中国国家自然科学基金; 中国博士后科学基金;
关键词：
Fractional-order competitive neural networks; Multistability; Attraction basins; Delays; GLOBAL ASYMPTOTICAL PERIODICITY; O(T(-ALPHA)) STABILITY; EXPONENTIAL STABILITY; SYNCHRONIZATION;
D O I：
10.1016/j.neunet.2021.03.036
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper is concerned with the multistability of fractional-order competitive neural networks (FCNNs) with time-varying delays. Based on the division of state space, the equilibrium points (EPs) of FCNNs are given. Several sufficient conditions and criteria are proposed to ascertain the multiple O(t(-alpha))-stability of delayed FCNNs. The O(t(-alpha))-stability is the extension of Mittag-Leffler stability of fractional-order neural networks, which contains monostability and multistability. Moreover, the attraction basins of the stable EPs of FCNNs are estimated, which shows the attraction basins of the stable EPs can be larger than the divided subsets. These conditions and criteria supplement and improve the previous results. Finally, the results are illustrated by the simulation examples. (C) 2021 Elsevier Ltd. All rights reserved.
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收藏
页码：325 / 335
页数：11
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