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Neural Computing and Applications

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07.08.2018 | Original Article

Robust exponential stabilization for uncertain neutral neural networks with interval time-varying delays by periodically intermittent control

Erschienen in: Neural Computing and Applications | Ausgabe 7/2020

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Abstract

This paper studies the robust exponential stabilization for a class of uncertain neutral neural networks with mixed interval time-varying delays. The aim of the paper is to design periodically intermittent control such that the closed-loop system is exponentially stable. By constructing a suitable Lyapunov–Krasovskii functional and by using some useful lemmas and some new analysis techniques, the researchers generate novel exponential stabilization criteria to ensure the robust exponential stabilization of considered uncertain neutral neural networks in terms of linear matrix inequalities. Based on the proposed criteria, an intermittent state-feedback controller design approach is introduced. Some numerical examples are given to show the effectiveness and benefits of the theoretical results.

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Titel: Robust exponential stabilization for uncertain neutral neural networks with interval time-varying delays by periodically intermittent control
Publikationsdatum: 07.08.2018
Erschienen in: Neural Computing and Applications / Ausgabe 7/2020
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-018-3671-2

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