Periodic solutions for stochastic Cohen–Grossberg neural networks with time-varying delays-Reference-Cited by-同舟云学术

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

Published:2020-08-10 Issue:1 Volume:22 Page:13-21
ISSN:1565-1339
Container-title:International Journal of Nonlinear Sciences and Numerical Simulation
language:en
Short-container-title:

Author:

Wu Wanqin¹,Yang Li²,Ren Yaping²

Affiliation:

1. Yunnan Minzu University , Kunming , Yunnan, 650500 , People’s Republic of China

2. School of Statistics and Mathematics , Yunnan University of Finance and Economics , Kunming , Yunnan, 650221 , People’s Republic of China

Abstract

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.

Funder

Tian Yuan Fund of NSFC

Yunnan Province Education Department Scientific Research Fund Project

Publisher

Walter de Gruyter GmbH

Subject

Applied Mathematics,General Physics and Astronomy,Mechanics of Materials,Engineering (miscellaneous),Modelling and Simulation,Computational Mechanics,Statistical and Nonlinear Physics

Link

https://www.degruyter.com/document/doi/10.1515/ijnsns-2019-0142/pdf

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