Global dissipativity of memristor-based complex-valued neural networks with time-varying delays

被引：47
|
作者：
Rakkiyappan, R. [1 ]
Velmurugan, G. [1 ]
Li, Xiaodi [2 ]
O'Regan, Donal [3 ]
机构：
[1] Bharathiar Univ, Dept Math, Coimbatore 641046, Tamil Nadu, India
[2] Shandong Normal Univ, Sch Math Sci, Jinan 250014, Shandong, Peoples R China
[3] Natl Univ Ireland, Sch Math Stat & Appl Math, Galway, Ireland
来源：
NEURAL COMPUTING & APPLICATIONS | 2016年 / 27卷 / 03期
基金：
中国国家自然科学基金; 中国博士后科学基金;
关键词：
Memristor; Complex-valued neural networks (CVNNs); Dissipativity; M-matrix theory; Time delays; Linear matrix inequality (LMI); DYNAMICAL-SYSTEMS; STABILITY; STABILIZATION; PASSIVITY;
D O I：
10.1007/s00521-015-1883-2
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Memristor is the new model two-terminal nonlinear circuit device in electronic circuit theory. This paper deals with the problem of global dissipativity and global exponential dissipativity for memristor-based complex-valued neural networks (MCVNNs) with time-varying delays. Sufficient global dissipativity conditions are derived from the theory of M-matrix analysis, and the globally attractive set as well as the positive invariant set is established. By constructing Lyapunov-Krasovskii functionals and using a linear matrix inequality technique, some new sufficient conditions on global dissipativity and global exponential dissipativity of MCVNNs are derived. Finally, two numerical examples are presented to demonstrate the effectiveness of our proposed theoretical results.
引用
收藏
页码：629 / 649
页数：21
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