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The Journal of Analysis

, Volume 27, Issue 1, pp 277–292 | Cite as

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

  • C. Vidhya
  • S. Dharani
  • P. BalasubramaniamEmail author
Original Research Paper
  • 51 Downloads

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.

Keywords

Stability Recurrent neural networks Time delay Reaction-diffusion terms Markovian jump parameter 

Mathematics Subject Classification

35K57 60H15 60J75 93E15 

Notes

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.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Forum D'Analystes, Chennai 2018

Authors and Affiliations

  1. 1.Department of Agricultural Economics and Extension and MathematicsPandit Jawaharlal Nehru College of Agriculture and Research InstituteKaraikalIndia
  2. 2.Department of MathematicsGandhigram Rural Institute (Deemed to be University)GandhigramIndia

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