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

Journal of inequalities and applications

article

Luogen Yao¹  Qian Cao² 

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

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

DOI  :  10.1186/s13660-020-02444-3

学科分类：电力

来源: SpringerOpen

【 摘 要 】

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】

CC BY