Basic theorem and global exponential stability of differential-algebraic neural networks with delay

被引：5
作者：
Chen, Jiejie [1 ,4 ]
Chen, Boshan [2 ]
Zeng, Zhigang [3 ,4 ]
机构：
[1] Hubei Normal Univ, Coll Comp Sci & Informat Engn, Huangshi 435002, Hubei, Peoples R China
[2] Hubei Normal Univ, Coll Math & Stat, Huangshi 435002, Hubei, Peoples R China
[3] Huazhong Univ Sci & Technol, Sch Automat, Wuhan 430074, Peoples R China
[4] Educ Minist China, Key Lab Image Proc & Intelligent Control, Wuhan 430074, Peoples R China
来源：
NEURAL NETWORKS | 2021年 / 140卷
基金：
中国国家自然科学基金;
关键词：
Global existence and uniqueness theorem; Global exponential stability; Differential-algebraic neural networks; Singular neural networks; Neutral-type neural networks; NEUTRAL-TYPE; SYSTEMS; PERIODICITY; SYNCHRONIZATION; STABILIZATION; DYNAMICS; FORM;
D O I：
10.1016/j.neunet.2021.01.017
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
A differential-algebraic neural network (DANN) with delay (DDANN) is proposed. Firstly, the global existence and uniqueness theorems are established for a DDANN, respectively. Next, a new differential-algebraic inequality is established. Then, a theorem on global exponential stability of DDANN is shown by using this inequality. As an application of DDANN, a very concise criterion on global exponential stability for a neutral-type neural network is given by using DDANNs. Finally, two examples are given to illustrate the theoretical results. (C) 2021 Published by Elsevier Ltd.
引用
收藏
页码：336 / 343
页数：8
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