Global asymptotic stability of stochastic reaction-diffusion recurrent neural networks with Markovian jumping parameters and mixed delays
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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.
KeywordsStability Recurrent neural networks Time delay Reaction-diffusion terms Markovian jump parameter
Mathematics Subject Classification35K57 60H15 60J75 93E15
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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Conflict of interest
The authors declare that they have no conflict of interest.
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