Stability and dissipativity criteria for neural networks with time-varying delays via an augmented zero equality approach

被引：27
作者：
Lee, S. H. [1 ]
Park, M. J. [2 ]
Ji, D. H. [3 ]
Kwon, O. M. [1 ]
机构：
[1] Chungbuk Natl Univ, Sch Elect Engn, Cheongju 28644, South Korea
[2] Kyung Hee Univ, Ctr Global Converging Humanities, Yongin 17104, South Korea
[3] Samsung Elect, Samsung Adv Inst Technol, Suwon 16678, South Korea
来源：
NEURAL NETWORKS | 2022年 / 146卷
基金：
新加坡国家研究基金会;
关键词：
Stability; Neural Network; Time-varying delay; Dissipativity analysis; Lyapunov method; INTEGRAL INEQUALITY APPLICATION; LINEAR-SYSTEMS; SYNCHRONIZATION; DISCRETE;
D O I：
10.1016/j.neunet.2021.11.007
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 investigates the stability and dissipativity problems for neural networks with time-varying delay. By the construction of new augmented Lyapunov-Krasovskii functionals based on integral inequality and the use of zero equality approach, three improved results are proposed in the forms of linear matrix inequalities. And, based on the stability results, the dissipativity analysis for NNs with time-varying delays was investigated. Through some numerical examples, the superiority and effectiveness of the proposed results are shown by comparing the existing works. (C) 2021 Elsevier Ltd. All rights reserved.
引用
收藏
页码：141 / 150
页数：10
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