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Global exponential estimates for uncertain Markovian jump neural networks with reaction-diffusion terms

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Abstract

In this paper, the robust global exponential estimating problem is investigated for Markovian jumping reaction-diffusion delayed neural networks with polytopic uncertainties under Dirichlet boundary conditions. The information on transition rates of the Markov process is assumed to be partially known. By introducing a new inequality, some diffusion-dependent exponential stability criteria are derived in terms of relaxed linear matrix inequalities. Those criteria depend on decay rate, which may be freely selected in a range according to practical situations, rather than required to satisfy a transcendental equation. Estimates of the decay rate and the decay coefficient are presented by solving these established linear matrix inequalities. Numerical examples are provided to demonstrate the advantage and effectiveness of the proposed method.

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Authors and Affiliations

  1. School of Electrical Engineering and Information, Anhui University of Technology, Ma’anshan, 243002, China

    Hao Shen

  2. Shandong Key Laboratory of Robotics and Intelligent Technology, College of Information and Electrical Engineering, Shandong University of Science and Technology, Qingdao, 266590, China

    Xia Huang

  3. School of Computer Science, Anhui University of Technology, Ma’anshan, 243002, China

    Jianping Zhou

  4. College of Information Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590, China

    Zhen Wang

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  1. Hao Shen
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  2. Xia Huang
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  3. Jianping Zhou
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  4. Zhen Wang
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Correspondence to Xia Huang.

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Shen, H., Huang, X., Zhou, J. et al. Global exponential estimates for uncertain Markovian jump neural networks with reaction-diffusion terms. Nonlinear Dyn 69, 473–486 (2012). https://doi.org/10.1007/s11071-011-0278-x

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  • DOI: https://doi.org/10.1007/s11071-011-0278-x

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