Extended dissipative analysis for memristive neural networks with two additive time-varying delay components

被引：46

作者：

Wei, Hongzhi ^{[1
]}

Li, Ruoxia ^{[2
,3
]}

Chen, Chunrong ^{[1
]}

Tu, Zhengwen ^{[4
]}

机构：

[1] Chongqing Univ, Coll Math & Stat, Chongqing 401331, Peoples R China

[2] Southeast Univ, Dept Math, Nanjing 210096, Jiangsu, Peoples R China

[3] Southeast Univ, Res Ctr Complex Syst & Network Sci, Nanjing 210096, Jiangsu, Peoples R China

[4] Chongqing Three Gorges Univ, Sch Math & Stat, Wanzhou 404100, Peoples R China

来源：

NEUROCOMPUTING | 2016年 / 216卷

关键词：

Memristor; Extended dissipative; Quadratically stability; Additive time-varying delays; Linear matrix inequalities (LMIs); STABILITY ANALYSIS; PASSIVITY ANALYSIS; STATE ESTIMATION; H-INFINITY; ASYMPTOTIC STABILITY; DEPENDENT STABILITY; SYSTEMS; SYNCHRONIZATION; PERIODICITY; CRITERIA;

D O I：

10.1016/j.neucom.2016.07.054

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper concentrates on the extended dissipativity of memristive neural networks with two additive time-varying delays. After giving a foundation to the memristive model, the paper establishes some fundamental results on quadratically stability and extended dissipativity criteria by means of the Lyapunov functional, integral inequality, as well as the relationship between time-varying delays. The novel extended dissipative inequality contains several weighting matrices, by converting the weighting matrices in a new performance index, the extended dissipativity will be degraded to the H-infinity performance, L-2 - L-infinity performance, passivity and dissipativity, respectively. Finally, one example is given to substantiate the significant improvement of the theoretical approaches. (C) 2016 Elsevier B.V. All rights reserved.

引用

页码：429 / 438

页数：10

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