Exponential stability and periodicity of memristor-based recurrent neural networks with time-varying delays

被引：2
|
作者：
Zhang, Wei [1 ,2 ]
Li, Chuandong [1 ]
Huang, Tingwen [3 ]
机构：
[1] Southwest Univ, Sch Elect & Informat Engn, Chongqing 400715, Peoples R China
[2] Chongqing Univ Educ, Key Lab Machine Percept & Childrens Intelligence, Chongqing 400067, Peoples R China
[3] Texas A&M Univ Qatar, Dept Math, Doha, Qatar
来源：
INTERNATIONAL JOURNAL OF BIOMATHEMATICS | 2017年 / 10卷 / 02期
关键词：
Recurrent neural networks; exponential stability; periodicity; linear matrix inequality (LMI); SYNCHRONIZATION;
D O I：
10.1142/S1793524517500279
中图分类号：
Q [生物科学];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
In this paper, the stability and periodicity of memristor-based neural networks with time-varying delays are studied. Based on linear matrix inequalities, differential inclusion theory and by constructing proper Lyapunov functional approach and using linear matrix inequality, some sufficient conditions are obtained for the global exponential stability and periodic solutions of memristor-based neural networks. Finally, two illustrative examples are given to demonstrate the results.
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页数：19
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