Anti-periodicity on high-order inertial Hopfield neural networks involving mixed delays

Journal of Inequalities and Applications

Qian Cao¹  Luogen Yao² 

[1] School of Mathematics and Statistics, Hunan University of Technology and Business;Key Laboratory of Hunan Province for Statistical Learning and Intelligent Computation;

关键词: High-order inertial neural networks;    Anti-periodic solution;    Global exponential stability;    Mixed delay;   

DOI  :  10.1186/s13660-020-02444-3

来源: DOAJ

【 摘 要 】

Abstract This paper deals with a class of high-order inertial Hopfield neural networks involving mixed delays. Utilizing differential inequality techniques and the Lyapunov function method, we obtain a sufficient assertion to ensure the existence and global exponential stability of anti-periodic solutions of the proposed networks. Moreover, an example with a numerical simulation is furnished to illustrate the effectiveness and feasibility of the theoretical results.

【 授权许可】

Unknown