Finite/fixed-time synchronization of delayed memristive reaction-diffusion neural networks

被引：32
作者：
Wang, Shiqin [1 ]
Guo, Zhenyuan [1 ]
Wen, Shiping [2 ]
Huang, Tingwen [3 ]
Gong, Shuqing [1 ,4 ]
机构：
[1] Hunan Univ, Coll Math & Econometr, Changsha 410082, Hunan, Peoples R China
[2] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 611731, Sichuan, Peoples R China
[3] Texas A&M Univ Qatar, Sci Program, POB 23874, Doha, Qatar
[4] Changsha Univ Sci & Technol, Sch Math & Stat, Changsha 410114, Hunan, Peoples R China
来源：
NEUROCOMPUTING | 2020年 / 375卷
基金：
中国国家自然科学基金;
关键词：
Finite/fixed-time synchronization; Reaction-diffusion; Memristive neural network; State feedback controller; ROBUST SYNCHRONIZATION; STABILITY; STABILIZATION; SYSTEMS;
D O I：
10.1016/j.neucom.2019.06.092
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 finite/fixed-time synchronization (FFTS) problems of two delayed memristive reaction-diffusion neural networks (MRDNNs). By designing appropriate state feedback controllers, utilizing the Lyapunov function method and inequality techniques, several sufficient criteria are derived to guarantee the FFTS of the drive-response MRDNNs. Taking into account both the influences of time and space, the model, described as a state-dependent switching system here, is more complex and closer to practical applications than those in the existing results. Finally, an example is presented to substantiate the effectiveness of the theoretical results. (C) 2019 Elsevier B.V. All rights reserved.
引用
收藏
页码：1 / 8
页数：8
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