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Erschienen in: Neural Processing Letters 1/2017

02.12.2016

\({\varvec{p}}\)th Moment Exponential Stability of Hybrid Delayed Reaction–Diffusion Cohen–Grossberg Neural Networks

verfasst von: Weiyuan Zhang, Junmin Li, Chenyang Ding, Keyi Xing

Erschienen in: Neural Processing Letters | Ausgabe 1/2017

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Abstract

In this paper, we propose hybrid reaction–diffusion Cohen–Grossberg neural networks (RDCGNNs) with variable coefficients and mixed time delays. By using the Lyapunov–Krasovkii functional approach, stochastic analysis technique and Hardy inequality, some novel sufficient conditions are derived to ensure the pth moment exponential stability of hybrid RDCGNNs with mixed time delays. The obtained sufficient conditions are relevant to the diffusion terms. The results of this paper are novel and improve some of the previously known results. Finally, two numerical examples are provided to verify the usefulness of the obtained results.

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Metadaten
Titel
th Moment Exponential Stability of Hybrid Delayed Reaction–Diffusion Cohen–Grossberg Neural Networks
verfasst von
Weiyuan Zhang
Junmin Li
Chenyang Ding
Keyi Xing
Publikationsdatum
02.12.2016
Verlag
Springer US
Erschienen in
Neural Processing Letters / Ausgabe 1/2017
Print ISSN: 1370-4621
Elektronische ISSN: 1573-773X
DOI
https://doi.org/10.1007/s11063-016-9572-4

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