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Neural Processing Letters
Erschienen in: Neural Processing Letters 2/2019

03.10.2018

Robust Exponential Stabilization for Switched Neutral Neural Networks with Mixed Time-Varying Delays

verfasst von: Yali Dong, Liangliang Guo, Jing Hao, Tianrui Li

Erschienen in: Neural Processing Letters | Ausgabe 2/2019

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Abstract

This paper studies the problems of exponential stabilization for a class of uncertain switched neutral neural networks with mixed time-varying delays. Based on the multiple Lyapunov-like functional method and the average dwell time method, the sufficient conditions which guarantee exponential stabilization of the uncertain switched neutral neural networks with mixed time-varying delays are presented. Averaged well time of switching signals is also given. Moreover, a design scheme for the stabilizing feedback controllers is proposed to guarantee exponential stability of corresponding closed-loop systems. Finally, two examples are given to illustrate the applicability and the effectiveness of the proposed method.
Vorheriger Artikel Calibrated Multi-label Classification with Label Correlations
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Metadaten
Titel
Robust Exponential Stabilization for Switched Neutral Neural Networks with Mixed Time-Varying Delays
verfasst von
Yali Dong
Liangliang Guo
Jing Hao
Tianrui Li
Publikationsdatum
03.10.2018
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 2/2019
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-018-9928-z

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