Finite-time stability and synchronization for memristor-based fractional-order Cohen-Grossberg neural network

被引：43
|
作者：
Zheng, Mingwen [1 ,2 ]
Li, Lixiang [3 ]
Peng, Haipeng [3 ]
Xiao, Jinghua [1 ]
Yang, Yixian [3 ]
Zhao, Hui [1 ]
机构：
[1] Beijing Univ Posts & Telecommun, Sch Sci, Beijing 100876, Peoples R China
[2] Shandong Univ Technol, Sch Sci, Zibo 255000, Peoples R China
[3] Beijing Univ Posts & Telecommun, Natl Engn Lab Disaster Backup & Recovery, State Key Lab Networking & Switching Technol, Informat Secur Ctr, Beijing 100876, Peoples R China
来源：
EUROPEAN PHYSICAL JOURNAL B | 2016年 / 89卷 / 09期
基金：
中国国家自然科学基金;
关键词：
EXPONENTIAL SYNCHRONIZATION; PERIODIC-SOLUTION;
D O I：
10.1140/epjb/e2016-70337-6
中图分类号：
O469 [凝聚态物理学];
学科分类号：
070205 ;
摘要：
In this paper, we study the finite-time stability and synchronization problem of a class of memristor-based fractional-order Cohen-Grossberg neural network (MFCGNN) with the fractional order alpha is an element of (0, 1]. We utilize the set-valued map and Filippov differential inclusion to treat MFCGNN because it has discontinuous right-hand sides. By using the definition of Caputo fractional-order derivative, the definitions of finite-time stability and synchronization, Gronwall's inequality and linear feedback controller, two new sufficient conditions are derived to ensure the finite-time stability of our proposed MFCGNN and achieve the finite-time synchronization of drive-response systems which are constituted by MFCGNNs. Finally, two numerical simulations are presented to verify the rightness of our proposed theorems.
引用
收藏
页数：11
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