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Mean Square Exponential Stability for Uncertain Delayed Stochastic Neural Networks with Markovian Jump Parameters

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Abstract

This paper is concerned with the problem of delay-dependent mean square exponential stability for a class of delayed stochastic Hopfield neural networks with Markovian jump parameters. The delays here are time-varying delays. Based on a new Lyapunov–Krasovskii functional, delay-dependent stability conditions are derived by means of linear matrix inequalities (LMIs). It is shown that the proposed results can contain some existing stability conditions as a special case. Finally, three numerical examples are given to illustrate the effectiveness of the proposed method, and the simulations show that our results are less conservative than the existing ones.

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Authors and Affiliations

  1. School of Automation, Nanjing University of Science and Technology, Nanjing, 210094, Jiangsu, P.R. China

    Qi Zhou

  2. Institute of Complexity Science, Qingdao University, Qingdao, 266071, Shandong, P.R. China

    Bing Chen & Chong Lin

  3. Space Control and Inertial Technology Research Centre, Harbin Institute of Technology, Harbin, 150080, Heilongjiang, P.R. China

    Hongyi Li

Authors
  1. Qi Zhou
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  2. Bing Chen
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  3. Chong Lin
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  4. Hongyi Li
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Corresponding author

Correspondence to Qi Zhou.

Additional information

This work is partially supported by the Natural Science Foundation of China (60674055, 60774047), and the Taishan Scholar Programme of Shandong Province.

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Zhou, Q., Chen, B., Lin, C. et al. Mean Square Exponential Stability for Uncertain Delayed Stochastic Neural Networks with Markovian Jump Parameters. Circuits Syst Signal Process 29, 331–348 (2010). https://doi.org/10.1007/s00034-009-9138-z

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  • DOI: https://doi.org/10.1007/s00034-009-9138-z

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