Stability analysis of neural networks with time-varying delay: Enhanced stability criteria and conservatism comparisons

•A new relaxed integral inequality is developed.•Several stability criteria of neural networks with time-varying delay are developed.•Theoretical comparisons and numerical examples show the advantages of the proposed inequality. This paper is concerned with the stability analysis of neural networks...

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Bibliographic Details
Published in:Communications in nonlinear science & numerical simulation 2018-01, Vol.54, p.118-135
Main Authors: Lin, Wen-Juan, He, Yong, Zhang, Chuan-Ke, Wu, Min
Format: Article
Language:English
Subjects:
Conservatism comparison
Delay
Inequalities
Integrals
Neural networks
Numerical analysis
Relaxed integral inequality
Stability analysis
Stability criteria
Studies
Systems stability
Time-varying delay
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Summary:•A new relaxed integral inequality is developed.•Several stability criteria of neural networks with time-varying delay are developed.•Theoretical comparisons and numerical examples show the advantages of the proposed inequality. This paper is concerned with the stability analysis of neural networks with a time-varying delay. To assess system stability accurately, the conservatism reduction of stability criteria has attracted many efforts, among which estimating integral terms as exact as possible is a key issue. At first, this paper develops a new relaxed integral inequality to reduce the estimation gap of popular Wirtinger-based inequality (WBI). Then, for showing the advantages of the proposed inequality over several existing inequalities that also improve the WBI, four stability criteria are derived through different inequalities and the same Lyapunov–Krasovskii functional (LKF), and the conservatism comparison of them is analyzed theoretically. Moreover, an improved criterion is established by combining the proposed inequality and an augmented LKF with delay-product-type terms. Finally, several numerical examples are used to demonstrate the advantages of the proposed method.
ISSN:1007-5704
1878-7274
DOI:10.1016/j.cnsns.2017.05.021