New stability results for delayed neural networks

This paper is concerned with the stability for delayed neural networks. By more fully making use of the information of the activation function, a new Lyapunov–Krasovskii functional (LKF) is constructed. Then a new integral inequality is developed, and more information of the activation function is t...

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Bibliographic Details
Published in:Applied mathematics and computation 2017-10, Vol.311, p.324-334
Main Authors: Shao, Hanyong, Li, Huanhuan, Zhu, Chuanjie
Format: Article
Language:English
Subjects:
Asymptotic stability
Integral inequality
Lyapunov–Krasovskii functional
Neural networks
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Summary:This paper is concerned with the stability for delayed neural networks. By more fully making use of the information of the activation function, a new Lyapunov–Krasovskii functional (LKF) is constructed. Then a new integral inequality is developed, and more information of the activation function is taken into account when the derivative of the LKF is estimated. By Lyapunov stability theory, a new stability result is obtained. Finally, three examples are given to illustrate the stability result is less conservative than some recently reported ones.
ISSN:0096-3003
1873-5649
DOI:10.1016/j.amc.2017.05.023