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Exponential Synchronization for Stochastic Neural Networks with Mixed Time Delays and Markovian Jump Parameters via Sampled Data

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  • Yingwei Li
  • Xueqing Guo

Abstract

The exponential synchronization issue for stochastic neural networks (SNNs) with mixed time delays and Markovian jump parameters using sampled‐data controller is investigated. Based on a novel Lyapunov‐Krasovskii functional, stochastic analysis theory, and linear matrix inequality (LMI) approach, we derived some novel sufficient conditions that guarantee that the master systems exponentially synchronize with the slave systems. The design method of the desired sampled‐data controller is also proposed. To reflect the most dynamical behaviors of the system, both Markovian jump parameters and stochastic disturbance are considered, where stochastic disturbances are given in the form of a Brownian motion. The results obtained in this paper are a little conservative comparing the previous results in the literature. Finally, two numerical examples are given to illustrate the effectiveness of the proposed methods.

Suggested Citation

  • Yingwei Li & Xueqing Guo, 2014. "Exponential Synchronization for Stochastic Neural Networks with Mixed Time Delays and Markovian Jump Parameters via Sampled Data," Abstract and Applied Analysis, John Wiley & Sons, vol. 2014(1).
    • Handle: RePEc:wly:jnlaaa:v:2014:y:2014:i:1:n:505164
    DOI: 10.1155/2014/505164
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    References listed on IDEAS

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    3. Huaiqin Wu & Luying Zhang, 2013. "Almost Periodic Solution for Memristive Neural Networks with Time-Varying Delays," Journal of Applied Mathematics, Hindawi, vol. 2013, pages 1-12, April.
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