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

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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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Acknowledgments

This work was supported by the National Science Foundation of China with Grant Nos. 11101434, 61005089, 51104157, and the Fundamental Research Funds for the Central Universities of 2012QNA48.

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Authors and Affiliations

  1. College of Sciences, China University of Mining and Technology, Xuzhou, 221116, China

    Song Zhu

  2. Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China

    Yi Shen

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  1. Song Zhu
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  2. Yi Shen
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Correspondence to Song Zhu.

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Zhu, S., Shen, Y. Robustness analysis of global exponential stability of neural networks with Markovian switching in the presence of time-varying delays or noises. Neural Comput & Applic 23, 1563–1571 (2013). https://doi.org/10.1007/s00521-012-1105-0

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  • DOI: https://doi.org/10.1007/s00521-012-1105-0

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