Robust Stabilization of Memristor-based Coupled Neural Networks with Time-varying Delays

被引：20
|
作者：
Fu, Qianhua [1 ,2 ]
Cai, Jingye [2 ]
Zhong, Shouming [3 ]
机构：
[1] Xihua Univ, Sch Elect Engn & Elect Informat, Chengdu 610039, Sichuan, Peoples R China
[2] Univ Elect Sci & Technol China, Sch Informat & Software Engn, Chengdu 610054, Sichuan, Peoples R China
[3] Univ Elect Sci & Technol China, Sch Math Sci, Chengdu 611731, Sichuan, Peoples R China
来源：
INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS | 2019年 / 17卷 / 10期
基金：
中国国家自然科学基金;
关键词：
Coupled neural networks; memristor; robust stabilization; T-S fuzzy; time-varying delays; EXPONENTIAL SYNCHRONIZATION; STABILITY; SYSTEMS;
D O I：
10.1007/s12555-018-0936-6
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The robust stabilization problem of memristor-based coupled neural networks (MNNs) is addressed in this paper. Firstly, the fuzzy model of MNNs is obtained by considering the properties of memristor and corresponding circuit, some predictable assumptions on the boundedness and Lipschitz continuity of activation functions are formulated. Secondly, based on T-S fuzzy theory and Lyapunov-Krasovskii functional method, robust stabilization criteria are derived in form of linear matrix inequalities (LMIs). Finally a numerical example is presented to demonstrate the effectiveness of the proposed robust stabilization criteria, which well supports theoretical results.
引用
收藏
页码：2666 / 2676
页数：11
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