Periodic attractor for reaction-diffusion high-order Hopfield neural networks with time-varying delays

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• 作者：Lian Duan^a ; ^{lianduan0906@163.com} ; Lihong Huang^b ; ^{lhhuang@csust.edu.cn} ; Zhenyuan Guo^c ; ^{zyguo@hnu.edu.cn} ; Xianwen Fang^a ; ^{fangxianwen@hotmail.com}
• 关键词：High-order Hopfield neural network ; Time-varying delay ; Reaction&ndash ; diffusion ; Periodic mild solution ; Global exponential stability
• 刊名：Computers & Mathematics with Applications
• 出版年：2017
• 出版时间：15 January 2017
• 年：2017
• 卷：73
• 期：2
• 页码：233-245
• 全文大小：918 K
• 卷排序：73

文摘

This paper is concerned with a class of reaction–diffusion high-order Hopfield neural networks with time-varying delays subject to the Dirichlet boundary condition in a bounded domain. Easily verifiable delay-independent criteria are established to ensure the existence of periodic mild solutions, and the global exponential stability of the periodic mild solutions is also discussed by using the exponential dissipation property of semigroup of operators. The obtained results are easy to check and they effectually complement previously known results. A numerical example is given to show the effectiveness of theoretical results.