Stabilization of distributed parameter Hopfield neural networks based on operator spectral theory

Hang Yin, Xianlin Huang, Hongyang Zhang, Xiao-zhi Gao

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


In this paper, we analyze the stability of distributed Hopfield neural networks. Distributed parameter Hopfield neural networks model is established by PDEs so state space of the controlled system belongs infinity dimensions, which is different from ODEs and DAEs models. Reaction-Diffusion Hopfield neural networks model is a new class of net systems which exist widely in control science, intelligent computation, cells of neurology and biology mathematica. Most of papers about Hopfield neural networks apply average Lyapunov function and M-matrix theory to study stability. Now, we use operator spectral theory to obtain stability of the system, which does not need complex calculation. At last, we make some simulations verify our criterions.

Original languageEnglish
Title of host publicationProceedings of the 35th Chinese Control Conference, CCC 2016
Number of pages6
ISBN (Electronic)9789881563910
Publication statusPublished - 26 Aug 2016
MoE publication typeA4 Article in a conference publication
EventChinese Control Conference - Chengdu, China
Duration: 27 Jul 201629 Jul 2016
Conference number: 35


ConferenceChinese Control Conference
Abbreviated titleccc2016
Internet address


  • Hopfield Neural Network
  • Operator Spectral Theory
  • Reaction-Diffusion System
  • Stability

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