Finite-time and fixed-time synchronization for a class of memristor-based competitive neural networks with different time scales

被引：40
作者：
Zhao, Yong [1 ]
Ren, Shanshan [2 ]
Kurths, Jurgen [3 ,4 ,5 ]
机构：
[1] Guangdong Polytech Normal Univ, Sch Math & Syst Sci, Guangzhou 510665, Peoples R China
[2] Henan Polytech Univ, Sch Math & Informat Sci, Jiaozuo 454000, Henan, Peoples R China
[3] Humboldt Univ, Inst Phys, D-12489 Berlin, Germany
[4] Potsdam Inst Climate Impact Res, D-14473 Potsdam, Germany
[5] Sechenov First Moscow State Med Univ, Ctr Anal Complex Syst, Moscow 119991, Russia
来源：
CHAOS SOLITONS & FRACTALS | 2021年 / 148卷
基金：
中国国家自然科学基金;
关键词：
Memristor; Memristor-based competitive neural; networks; Finite-time synchronization; Fixed-time synchronization; EXPONENTIAL SYNCHRONIZATION; DELAYS; STABILIZATION;
D O I：
10.1016/j.chaos.2021.111033
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
In this paper, finite-time and fixed-time synchronization are considered for a class of memristor-based competitive neural networks(MCNNs) with different time scales. Based on the theory of differential equations with discontinuous right-hand sides, several new sufficient conditions ensuring the finite-time and fixed-time synchronization of MCNNs are obtained by designing proper controllers. Moreover, the settling time is estimated. Finally, a numerical example is given to show the effectiveness and feasibility of our results. (c) 2021 Elsevier Ltd. All rights reserved.
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页数：8
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