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Delayed impulsive control for exponential synchronization of stochastic reaction–diffusion neural networks with time-varying delays using general integral inequalities

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Abstract

The intention of this paper is to explore the exponential synchronization of delayed stochastic reaction–diffusion neural networks. By constructing appropriate Lyapunov–Krasovskii functional (LKF) with triple integral terms and by utilizing improved double integral inequalities, new synchronization criteria are developed in the form of linear matrix inequalities (LMIs) to assure the exponential synchronization of the neural networks under study. The resulting criteria use more data of the delay bounds, and by means of new inequalities, they are substantiated to be less conservative and also computationally appealing than certain existing works. At the end, numerical simulations are furnished to reveal the potency of the acquired theoretical results.

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Acknowledgements

This work was 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 Mathematics, Gandhigram Rural Institute (Deemed to be University), Gandhigram, Dindigul, 624 302, India

    S. Dharani & P. Balasubramaniam

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  1. S. Dharani
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  2. P. Balasubramaniam
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Correspondence to P. Balasubramaniam.

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Dharani, S., Balasubramaniam, P. Delayed impulsive control for exponential synchronization of stochastic reaction–diffusion neural networks with time-varying delays using general integral inequalities. Neural Comput & Applic 31, 8719–8726 (2019). https://doi.org/10.1007/s00521-019-04223-8

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  • DOI: https://doi.org/10.1007/s00521-019-04223-8

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