Synchronization of fractional-order memristor-based complex-valued neural networks with uncertain parameters and time delays

被引：88
|
作者：
Yang, Xujun [1 ]
Li, Chuandong [1 ]
Huang, Tingwen [2 ]
Song, Qiankun [3 ]
Huang, Junjian [4 ]
机构：
[1] Southwest Univ, Chongqing Key Lab Nonlinear Circuits & Intelligen, Coll Elect & Informat Engn, Chongqing 400715, Peoples R China
[2] Texas A&M Univ Qatar, Doha 23874, Qatar
[3] Chongqing Jiaotong Univ, Coll Math & Stat, Chongqing 400074, Peoples R China
[4] Chongqing Univ Educ, Dept Comp Sci, Chongqing 400067, Peoples R China
来源：
CHAOS SOLITONS & FRACTALS | 2018年 / 110卷
基金：
中国国家自然科学基金;
关键词：
Synchronization; Fractional order; Memristor; Complex-valued neural networks; Uncertain parameter; Time delay; GLOBAL EXPONENTIAL STABILITY; MITTAG-LEFFLER STABILITY; SYSTEMS;
D O I：
10.1016/j.chaos.2018.03.016
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
This paper talks about the global asymptotical synchronization problem of delayed fractional-order memristor-based complex-valued neural networks with uncertain parameters. Under the framework of Filippov solution and differential inclusion theory, several sufficient criteria ensuring the global asymptotical synchronization for the addressed drive-response models are derived, by means of Lyapunov direct method and comparison theorem. In addition, two numerical examples are designed to verify the correctness and effectiveness of the theoretical results. (C) 2018 Elsevier Ltd. All rights reserved.
引用
收藏
页码：105 / 123
页数：19
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