Finite-time and fixed-time synchronization of fuzzy Clifford-valued Cohen-Grossberg neural networks with discontinuous activations and time-varying delays

被引：16
|
作者：
Aouiti, Chaouki [1 ]
Bessifi, Mayssa [1 ]
机构：
[1] Univ Carthage, Fac Sci Bizerte, Dept Math, GAMA Lab LR21ES10, Tunis, Tunisia
来源：
INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING | 2021年 / 35卷 / 12期
关键词：
Clifford-valued neural networks; differential inclusion; discontinuous activation; finite-time; fixed-time; fuzzy Cohen-Grossberg neural networks; settling time; synchronization; GLOBAL EXPONENTIAL STABILITY; ROBUST STABILITY; NEUTRAL-TYPE; STABILIZATION; SYSTEMS; DESIGN;
D O I：
10.1002/acs.3333
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this article, we are concerned with fuzzy Clifford-valued Cohen-Grossberg neural networks (FCVCGNNs) via discontinuous activations and time-varying delays. First, the time-delayed feedback strategy is used to investigate the synchronization in finite-time and fixed-time of FCVCGNNs with discontinuous activations and time-varying delays. By designing Lyapunov functions and utilizing differential inequalities, several effective conditions are derived to ensure synchronization in finite-time and fixed-time of the addressed neural networks. A novel fixed-time convergence method is proposed to study synchronization in fixed-time of discontinuous delayed FCVCGNNs. Furthermore, the settling time of synchronization are estimated. In the end, two numerical examples with simulations are given to confirm the effectiveness of the synchronization criteria.
引用
收藏
页码：2499 / 2520
页数：22
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