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

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 enum...

Full description

Saved in:
Bibliographic Details
Published in:Applied mathematics and computation 2017-12, Vol.314, p.322-333
Main Authors: Wang, Bo, Yan, Juan, Cheng, Jun, Zhong, Shouming
Format: Article
Language:English
Subjects:
Generalized neural networks
Less conservative
Linear matrix inequality
Stability analysis
Time-varying delay
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:0096-3003
1873-5649
DOI:10.1016/j.amc.2017.06.031