New results on passivity analysis of memristor-based neural networks with time-varying delays

被引：27
|
作者：
Wang, Leimin [1 ,2 ]
Shen, Yi [1 ,2 ]
机构：
[1] Huazhong Univ Sci & Technol, Sch Automat, Wuhan 430074, Peoples R China
[2] Educ Minist China, Key Lab Image Proc & Intelligent Control, Wuhan 430074, Peoples R China
来源：
NEUROCOMPUTING | 2014年 / 144卷
基金：
美国国家科学基金会; 中国国家自然科学基金;
关键词：
Passivity; Memristor-based neural networks; Filippov solution; Time-varying delays; INFINITY STATE ESTIMATION; EXPONENTIAL PASSIVITY; STABILITY ANALYSIS; COMPLEX NETWORKS; DISCRETE; SYNCHRONIZATION; SYSTEMS;
D O I：
10.1016/j.neucom.2014.05.032
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, the passivity problem of memristor-based neural networks (MNNs) with time-varying delays is investigated. New delay-dependent criteria are established for the passivity of MNNs. The time-varying delays of our paper are not necessary to be differentiable, so our results are less conservative, which enrich and improve the earlier publications. An example is given to demonstrate the effectiveness of the obtained results. (C) 2014 Elsevier B.V. All rights reserved.
引用
收藏
页码：208 / 214
页数：7
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