Multiple integral Lyapunov approach to mixed-delay-dependent stability of neutral neural networks

被引：28
作者：
Zhang, Guobao [1 ]
Wang, Ting [2 ]
Li, Tao [3 ]
Fei, Shumin [1 ]
机构：
[1] Southeast Univ, Minist Educ, Sch Automat, Key Lab Measurement & Control CSE, Nanjing 210096, Jiangsu, Peoples R China
[2] Nanjing Forestry Univ, Coll Informat Sci & Technol, Nanjing 210037, Jiangsu, Peoples R China
[3] Nanjing Univ Aeronaut & Astronaut, Sch Automat Engn, Nanjing 210016, Jiangsu, Peoples R China
来源：
NEUROCOMPUTING | 2018年 / 275卷
基金：
中国国家自然科学基金;
关键词：
Neutral neural networks; Robust stability; Multiple integral Lyapunov technique; Mixed-delay-dependence; Time-varying delay; TIME-VARYING DELAYS; GLOBAL ROBUST STABILITY; DISTRIBUTED DELAYS; LEAKAGE DELAYS; EXPONENTIAL STABILITY; CRITERIA; DISCRETE; SYSTEMS; COMPONENTS;
D O I：
10.1016/j.neucom.2017.10.021
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This work studies the robust stability for a class of uncertain neutral neural networks with mixed time-varying delays. Through utilizing some novel Wirtinger-based integral inequalities and extending the convex combination technique, the upper bound on derivative of Lyapunov-Krasovskii (L-K) functional can be estimated more tightly and two mixed-delay-dependent criteria are proposed in terms of linear matrix inequalities (LMIs), in which some previously ignored information can be utilized. Different from those existent works, based on interconnected relationship between the neutral delay and neural one, some multiple integral Lyapunov terms are constructed and the conservatism can be effectively reduced. Finally, two numerical examples are given to show the benefits of the proposed criteria. (C) 2017 Elsevier B.V. All rights reserved.
引用
收藏
页码：1782 / 1792
页数：11
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