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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 6/2013

01.11.2013 | Original Article

Robustness analysis of global exponential stability of neural networks with Markovian switching in the presence of time-varying delays or noises

verfasst von: Song Zhu, Yi Shen

Erschienen in: Neural Computing and Applications | Ausgabe 6/2013

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Abstract

In this paper, we analyze the robustness of global exponential stability of neural networks with Markovian switching (NNwMS) subject to random disturbances or time-varying delays. Given a globally exponentially stable neural network with Markovian switching, the problems to be addressed herein are how much noises or time delays that the neural networks can remain to be globally exponentially stable. We characterize the upper bounds of the time delays or noise intensity for the NNwMS to sustain global exponential stability. Two numerical examples are provided to illustrate the theoretical results.
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Metadaten
Titel
Robustness analysis of global exponential stability of neural networks with Markovian switching in the presence of time-varying delays or noises
verfasst von
Song Zhu
Yi Shen
Publikationsdatum
01.11.2013
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 6/2013
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-012-1105-0

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