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Periodic solutions for stochastic Cohen–Grossberg neural networks with time-varying delays

  • Wanqin Wu , Li Yang EMAIL logo and Yaping Ren

Abstract

This paper is concerned with the periodic solutions for a class of stochastic Cohen–Grossberg neural networks with time-varying delays. Since there is a non-linearity in the leakage terms of stochastic Cohen–Grossberg neural networks, some techniques are needed to overcome the difficulty in dealing with the nonlinearity. By applying fixed points principle and Gronwall–Bellman inequality, some sufficient conditions on the existence and exponential stability of periodic solution for the stochastic neural networks are established. Moreover, a numerical example is presented to validate the theoretical results. Our results are also applicable to the existence and exponential stability of periodic solution for the corresponding deterministic systems.

2010 Mathematics Subject Classification: 34K09; 34K20; 92B05

Corresponding author: Li Yang, School of Statistics and Mathematics, Yunnan University of Finance and Economics, Kunming, Yunnan, 650221, People’s Republic of China, E-mail:

Funding source: Tian Yuan Fund of NSFC

Award Identifier / Grant number: 11526180

Funding source: Yunnan Province Education Department Scientific Research Fund Project

Award Identifier / Grant number: Nos.2018JS315, 2018JS309

Acknowledgments

All authors are grateful to the anonymous referees for their constructive comments and helpful suggestions.

  1. Author contribution: All authors contributed equally to the manuscript and typed, read and approved the final manuscript.

  2. Research funding: This work is supported by Tian Yuan Fund of NSFC (No. 11526180), Yunnan Province Education Department Scientific Research Fund Project (Nos. 2018JS315, 2018JS309), Special training program for outstanding young teachers of colleges and universities in Yunnan Province.

  3. Conflict of interest statement: All authors declare that they have no competing interests.

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Received: 2019-05-06
Accepted: 2020-03-08
Published Online: 2020-08-10
Published in Print: 2021-02-23

© 2020 Walter de Gruyter GmbH, Berlin/Boston

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