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Stability of Impulsive Stochastic Reaction Diffusion Recurrent Neural Network

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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 the Lyapunov–Krasovskii functional method combined with linear matrix inequality approach, novel sufficient stability conditions are derived for impulsive stochastic reaction-diffusion recurrent neural networks with Markovian jumping parameters and mixed delays. Finally, numerical examples with simulation results are given to illustrate the derived theoretical results.

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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, Pudhucherry, 3, India

    C. Vidhya

  2. Department of Mathematics, Bannari Amman Institute of Technology, Sathyamangalam, Tamilnadu, 638 401, India

    S. Dharani

  3. Department of Mathematics, The Gandhigram Rural Institute-Deemed to be University, Gandhigram, Tamilnadu, 624 302, India

    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. Stability of Impulsive Stochastic Reaction Diffusion Recurrent Neural Network. Neural Process Lett 51, 1049–1060 (2020). https://doi.org/10.1007/s11063-019-10131-8

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