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...

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Bibliographic Details
Published inApplied mathematics and computation Vol. 314; pp. 322 - 333
Main Authors Wang, Bo, Yan, Juan, Cheng, Jun, Zhong, Shouming
Format Journal Article
LanguageEnglish
Published Elsevier Inc 01.12.2017
Subjects
Generalized neural networks
Less conservative
Linear matrix inequality
Stability analysis
Time-varying delay
Linear matrix inequality
Less conservative
Stability analysis
Generalized neural networks
Time-varying delay
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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