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Neural Processing Letters
Erschienen in: Neural Processing Letters 3/2018

19.08.2017

Decentralized Event-Triggered Exponential Stability for Uncertain Delayed Genetic Regulatory Networks with Markov Jump Parameters and Distributed Delays

verfasst von: M. Syed Ali, R. Vadivel

Erschienen in: Neural Processing Letters | Ausgabe 3/2018

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Abstract

This paper is concerned with the stability problem for a class of decentralized event-triggered exponential stability for uncertain delayed genetic regulatory networks (GRNs) with Markov jump parameters and distributed delays. In order to reduce the information communication burden, the decentralized event-triggered mechanism is proposed in this paper. Exponential stability for the proposed GRNs are studied by the Lyapunov method and the matrix inequality techniques. Some new sufficient conditions are obtained to ensure the global exponential stability of the proposed GRNs. Furthermore, the proposed LMI results are computationally efficient which are easy to be verified via the Matlab LMI toolbox. In addition, four numerical examples are provided to illustrate the effectiveness of the theoretical results.
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Metadaten
Titel
Decentralized Event-Triggered Exponential Stability for Uncertain Delayed Genetic Regulatory Networks with Markov Jump Parameters and Distributed Delays
verfasst von
M. Syed Ali
R. Vadivel
Publikationsdatum
19.08.2017
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 3/2018
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-017-9695-2

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