Global exponential stability of periodic solution of delayed discontinuous Cohen–Grossberg neural networks and its applications-Reference-Cited by-同舟云学术

Global exponential stability of periodic solution of delayed discontinuous Cohen–Grossberg neural networks and its applications

Published:2021-06-04 Issue:0 Volume:0 Page:
ISSN:1565-1339
Container-title:International Journal of Nonlinear Sciences and Numerical Simulation
language:en
Short-container-title:

Author:

Chai Yiyuan¹,Feng Jiqiang²,Qin Sitian³,Pan Xinyu³

Affiliation:

1. School of Electronics and Information Engineering , Shenzhen Institute of Computing Sciences, Shenzhen University , Shenzhen 518060 , China

2. Department of Mathematics and Statistics , Shenzhen Institute of Computing Sciences, Shenzhen University , Shenzhen 518060 , China

3. Department of Mathematics , Harbin Institute of Technology , Weihai 264209 , China

Abstract

Abstract This paper is concerned with the existence and global exponential stability of the periodic solution of delayed Cohen–Grossberg neural networks (CGNNs) with discontinuous activation functions. The activations considered herein are non-decreasing but not required to be Lipschitz or continuous. Based on differential inclusion theory, Lyapunov functional theory and Leary–Schauder alternative theorem, some sufficient criteria are derived to ensure the existence and global exponential stability of the periodic solution. In order to show the superiority of the obtained results, an application and some detailed comparisons between some existing related results and our results are presented. Finally, some numerical examples are also illustrated.

Publisher

Walter de Gruyter GmbH

Subject

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

Link

https://www.degruyter.com/document/doi/10.1515/ijnsns-2020-0157/pdf

Reference45 articles.

1. M. Cohen and S. Grossberg, “Absolute stability of global pattern formation and parallel memory storage by competitive neural networks,” IEEE Trans. Syst. Man Cybern. Syst., vol. 5, pp. 815–826, 1983. https://doi.org/10.1109/tsmc.1983.6313075.

2. P. Balasubramaniam and V. Vembarasan, “Asymptotic stability of BAM neural networks of neutral-type with impulsive effects and time delay in the leakage term,” Int. J. Comput. Math., vol. 88, no. 15, pp. 3271–3291, 2011. https://doi.org/10.1080/00207160.2011.591388.

3. Z. Cai and L. Huang, “Finite-time synchronization by switching state-feedback control for discontinuous Cohen–Grossberg neural networks with mixed delays,” Int. J. Mach. Learn. Cybern., vol. 9, no. 10, pp. 1683–1695, 2018.

4. L. Chua, “Resistance switching memories are memristors,” Appl. Phys. A, vol. 102, no. 4, pp. 765–783, 2011. https://doi.org/10.1007/s00339-011-6264-9.

5. C. Aouiti and I. Ben Gharbia, “Dynamics behavior for second-order neutral Clifford differential equations: inertial neural networks with mixed delays,” Comput. Appl. Math., vol. 39, no. 2, p. 120, 2020. https://doi.org/10.1007/s40314-020-01148-0.

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Exponential Synchronization of Cohen-Grossberg Neural Networks With Delays;IEEE Access;2023