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

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, an...

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Bibliographic Details
Published inComputers & mathematics with applications (1987) Vol. 73; no. 2; pp. 233 - 245
Main Authors Duan, Lian, Huang, Lihong, Guo, Zhenyuan, Fang, Xianwen
Format Journal Article
LanguageEnglish
Published Oxford Elsevier Ltd 15.01.2017
Elsevier BV
Subjects
Diffusion
Dirichlet problem
Dissipation
Global exponential stability
High-order Hopfield neural network
Neural networks
Numerical analysis
Periodic mild solution
Reaction–diffusion
Studies
Time-varying delay
High-order Hopfield neural network
Global exponential stability
Time-varying delay
Reaction–diffusion
Periodic mild solution
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Summary: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.
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ISSN:0898-1221
1873-7668
DOI:10.1016/j.camwa.2016.11.010