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

26.04.2018

Finite-Time Non-fragile Dissipative Stabilization of Delayed Neural Networks

verfasst von: S. Saravanan, M. Syed Ali, R. Saravanakumar

Erschienen in: Neural Processing Letters | Ausgabe 2/2019

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Abstract

This article studies the finite-time non-fragile dissipativity issue of time-delayed neural networks. The main purpose of this study is to synthesize the finite-time non-fragile and dissipativity controller guaranteeing the finite-time boundedness of the resulting neural networks (NNs) with optimal dissipative performance index. By constructing appropriate Lyapunov–Krasovskii functional, combining with Jensen’s inequality and Wirtinger’s based integral inequality, a new delay-dependent finite-time boundedness of dissipativity criteria is obtained in terms of linear matrix inequalities techniques. The finite-time non-fragile state-feedback controller is designed to ensure the strict dissipativeness of the concerned NNs. In addition, these conditions are obtained with less conservative results than those in the existing approaches, which has been shown through numerical examples.
Vorheriger Artikel EEG Emotion Recognition Based on Graph Regularized Sparse Linear Regression
Nächster Artikel Stabilization of Switched Stochastic Genetic Regulatory Networks with Leakage and Impulsive Effects

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Metadaten
Titel
Finite-Time Non-fragile Dissipative Stabilization of Delayed Neural Networks
verfasst von
S. Saravanan
M. Syed Ali
R. Saravanakumar
Publikationsdatum
26.04.2018
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/s11063-018-9844-2

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