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

S.H. Lee, M.J. Park, O.M. Kwon and S.G. Choi

Applied Mathematics and Computation, 2022, vol. 420, issue C

Abstract: 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.

Keywords: Stability; Zero equalities; Time-varying delay; Neural networks (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0096300321009693
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:420:y:2022:i:c:s0096300321009693

DOI: 10.1016/j.amc.2021.126886

Access Statistics for this article

Applied Mathematics and Computation is currently edited by Theodore Simos

More articles in Applied Mathematics and Computation from Elsevier
Bibliographic data for series maintained by Catherine Liu ().