New criteria of stability analysis for generalized neural networks subject to time-varying delayed signals

Bo Wang, Juan Yan, Jun Cheng and Shouming Zhong

Applied Mathematics and Computation, 2017, vol. 314, issue C, 322-333

Abstract: This paper focuses on the new criteria of stability analysis for generalized neural networks (GNNs) subject to time-varying delayed signals. A new methodology is employed with the aids of slack variables. By constructing an augmented Lyapunov–Krasovskii functional (LKF) involving Newton–Leibniz enumerating and triple integral term, some less conservative conditions are achieved in terms of linear matrix inequality (LMI). Numerical examples including real-time application are given to illustrate the superiority and effectiveness of proposed approach.

Keywords: Linear matrix inequality; Stability analysis; Generalized neural networks; Time-varying delay; Less conservative (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0096300317304447
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:314:y:2017:i:c:p:322-333

DOI: 10.1016/j.amc.2017.06.031

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 ().