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Robust Exponential Synchronization for Stochastic Delayed Neural Networks with Reaction–Diffusion Terms and Markovian Jumping Parameters

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Abstract

This paper investigates robust exponential synchronization for stochastic delayed neural networks with reaction–diffusion terms and Markovian jumping parameters driven by infinite dimensional Wiener processes. The novelty of this paper lives in the use of a new Lyapunov–Krasovskii functional and Poincaré inequality to present some criteria for robust exponential synchronization in terms of linear matrix inequalities (LMIs) and matrix measure under Robin boundary conditions. Finally, two numerical examples are provided to illustrate the effectiveness of the easily verifiable synchronization LMIs in MATLAB toolbox.

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Acknowledgements

The authors would like to thank the editor and reviewers for their insightful and constructive comments. The work of authors was partly supported by National Natural Science Foundation of China (Nos. 11771014, 31772844, 31302182, 11171374) and Natural Science Foundation of Shandong Province (No. ZR2011AZ001).

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Authors and Affiliations

  1. College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao, 266100, China

    Tengda Wei

  2. Department of Mathematics, University of Dundee, Dundee, DD1 4HN, Scotland, United Kingdom

    Tengda Wei

  3. College of Marine Life Science, Ocean University of China, Qingdao, 266100, China

    Yangfan Wang

  4. School of Mathematical Sciences, Ocean University of China, Qingdao, 266100, China

    Linshan Wang

Authors
  1. Tengda Wei
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  2. Yangfan Wang
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  3. Linshan Wang
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Correspondence to Yangfan Wang or Linshan Wang.

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Wei, T., Wang, Y. & Wang, L. Robust Exponential Synchronization for Stochastic Delayed Neural Networks with Reaction–Diffusion Terms and Markovian Jumping Parameters. Neural Process Lett 48, 979–994 (2018). https://doi.org/10.1007/s11063-017-9756-6

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  • DOI: https://doi.org/10.1007/s11063-017-9756-6

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