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Global asymptotic stability of stochastic reaction-diffusion recurrent neural networks with Markovian jumping parameters and mixed delays

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Abstract

In this paper, the problem of global asymptotic stability of stochastic Markovian jumping reaction-diffusion neural networks with discrete and distributed delays is investigated. By utilizing Lyapunov–Krasovskii functional method combined with linear matrix inequality approach, novel sufficient stability conditions for delayed stochastic reaction-diffusion recurrent neural networks with Markovian jumping parameters and mixed delays are derived. Finally, numerical examples with simulation results are given to illustrate the derived theoretical results.

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Acknowledgements

This work was partially supported by the Science and Engineering Research Board (SERB), Department of Science and Technology (DST), Government of India under SERB National Post-Doctoral Fellowship scheme File Number: PDF/2017/001800.

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Authors and Affiliations

  1. Department of Agricultural Economics and Extension and Mathematics, Pandit Jawaharlal Nehru College of Agriculture and Research Institute, Karaikal, Tamil Nadu, 609603, India

    C. Vidhya

  2. Department of Mathematics, Gandhigram Rural Institute (Deemed to be University), Gandhigram, Tamil Nadu, 624 302, India

    S. Dharani & P. Balasubramaniam

Authors
  1. C. Vidhya
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  2. S. Dharani
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  3. P. Balasubramaniam
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Correspondence to P. Balasubramaniam.

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Vidhya, C., Dharani, S. & Balasubramaniam, P. Global asymptotic stability of stochastic reaction-diffusion recurrent neural networks with Markovian jumping parameters and mixed delays. J Anal 27, 277–292 (2019). https://doi.org/10.1007/s41478-018-0123-4

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  • DOI: https://doi.org/10.1007/s41478-018-0123-4

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