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Exponential Stability of Uncertain Stochastic Neural Networks with Markovian Switching

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Abstract

This paper is concerned with the exponential stability analysis problem for a class of uncertain stochastic neural networks with Markovian switching. The parameter uncertainties are assumed to be norm bounded. Based on Lyapunov–Krasovskii stability theory and the nonnegative semimartingale convergence theorem, delay-dependent and delay- independent sufficient stability conditions are established. It is also shown that the result in this paper cover some recently published works. Two examples are provided to demonstrate the usefulness of the proposed criteria.

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Authors and Affiliations

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

    Song Zhu, Yi Shen & Lei Liu

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  1. Song Zhu
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  2. Yi Shen
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  3. Lei Liu
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Correspondence to Yi Shen.

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Zhu, S., Shen, Y. & Liu, L. Exponential Stability of Uncertain Stochastic Neural Networks with Markovian Switching. Neural Process Lett 32, 293–309 (2010). https://doi.org/10.1007/s11063-010-9158-5

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  • DOI: https://doi.org/10.1007/s11063-010-9158-5

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