Less conservative stability criteria for general neural networks through novel delay-dependent functional

被引：8
作者：
Lee, S. H. [1 ]
Park, M. J. [2 ]
Kwon, O. M. [1 ]
Choi, S. G. [3 ]
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] Chungbuk Natl Univ, Sch Informat & Commun Engn, Cheongju 28644, South Korea
来源：
APPLIED MATHEMATICS AND COMPUTATION | 2022年 / 420卷
基金：
新加坡国家研究基金会;
关键词：
Stability; Zero equalities; Time-varying delay; Neural networks; TIME-VARYING DELAYS; LYAPUNOV-KRASOVSKII FUNCTIONALS; INTEGRAL INEQUALITY APPLICATION; GLOBAL ASYMPTOTIC STABILITY; LINEAR-SYSTEMS;
D O I：
10.1016/j.amc.2021.126886
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
This work investigates the improved stability conditions for neural networks with time-varying delay. By the construction of newly augmented Lyapunov-Krasovskii functionals including the integral inequality and the use of the augmented zero equality approach, three improved results are proposed in the form of linear matrix inequalities. Two delay-dependent Lyapunov-Krasovskii functionals based on the integral inequality are proposed for the first time. Also, by utilizing the augmented zero equality approach, a less conservative result is obtained while reducing computation complexity. Through some numerical examples, the effectiveness and superiority of the proposed results are confirmed by comparing the existing works. (C) 2021 Elsevier Inc. All rights reserved.
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页数：14
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