Relaxed dissipativity criteria for memristive neural networks with leakage and time-varying delays

被引：19
|
作者：
Xiao, Jianying [1 ,2 ]
Zhong, Shouming [1 ]
Li, Yongtao [3 ]
机构：
[1] Univ Elect Sci & Technol China, Sch Math Sci, Chengdu 611731, Peoples R China
[2] Southwest Petr Univ, Sch Sci, Chengdu 610050, Peoples R China
[3] Southwest Petr Univ, Coll Chem & Chem Engn, Chengdu 610050, Peoples R China
来源：
NEUROCOMPUTING | 2016年 / 171卷
基金：
中国国家自然科学基金;
关键词：
Dissipativity; Memristive neural networks; Leakage delay; Time-varying delay; Lyapunov functional; STABILITY ANALYSIS; ROBUST STABILITY; STATE ESTIMATION; DISCRETE; SYSTEMS; PASSIVITY; STABILIZATION; DESIGN;
D O I：
10.1016/j.neucom.2015.07.029
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 problem of strict (Q,S,R)-gamma-dissipativity analysis for memristive neural networks (MNNs) with leakage and time-varying delays is studied. By applying nonsmooth analysis, MNNs are converted into the conventional neural networks (NNs). Based on the construction of a novel Lyapunov-Krasovskii functional (LKF), the relaxed dissipativity criteria are obtained by combining Wirtinger-based integral inequality with free-weighting matrices technique. This superior proposed criteria do not really require all the symmetric matrices involved in the employed quadratic to be positive definite. Moreover, the derived criteria are less conservative. Finally, two numerical examples are given to show the effectiveness and less conservatism of the proposed criteria. (C) 2015 Elsevier B.V. All rights reserved.
引用
收藏
页码：708 / 718
页数：11
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