Finite-time stability analysis for neutral-type neural networks with hybrid time-varying delays without using Lyapunov method

被引：43
作者：
Zheng, Mingwen [1 ,2 ]
Li, Lixiang [3 ]
Peng, Haipeng [3 ]
Xiao, Jinghua [1 ,4 ]
Yang, Yixian
Zhao, Hui [1 ]
机构：
[1] Beijing Univ Posts & Telecommun, Sch Sci, Beijing 100876, Peoples R China
[2] Shandong Univ Technol, Sch Sci, Zibo 255000, Peoples R China
[3] Beijing Univ Posts & Telecommun, Informat Secur Ctr, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China
[4] Beijing Univ Posts & Telecommun, State Key Lab Informat Photon & Opt Commun, Beijing 100876, Peoples R China
来源：
NEUROCOMPUTING | 2017年 / 238卷
基金：
中国国家自然科学基金;
关键词：
Finite-time stability; Neutral-type neural network; Hybrid time-varying delays; Gronwall Bellman inequality; DEPENDENT STABILITY; ROBUST STABILITY; ADAPTIVE SYNCHRONIZATION; SYSTEMS; STABILIZATION; DISCRETE;
D O I：
10.1016/j.neucom.2017.01.037
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, we study the finite-time stability problem of neutral-type neural networks with various hybrid time-varying delays. These delays include general time-varying delays, finite distributed time-varying delays, neutral-type time-varying delays and infinite distributed time-varying delays. Based on the definition of finite-time stability instead of Lyapunov function method, with the aid of inequality techniques, some simple and novel sufficient conditions are derived to guarantee the finite-time stability of our proposed network model. The deduction process is simple and easy to understand. Finally, three simulation examples are given to show the effectiveness of our main results. (C) 2017 Elsevier B.V. All rights reserved.
引用
收藏
页码：67 / 75
页数：9
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