• DocumentCode
    2776779
  • Title

    Dynamic behaviors of hybrid Lotka-Volterra recurrent neural networks with memristor characteristics

  • Author

    Wu, Ailong ; Zeng, Zhigang

  • Author_Institution
    Dept. of Control Sci. & Eng., Huazhong Univ. of Sci. & Technol., Wuhan, China
  • fYear
    2012
  • fDate
    10-15 June 2012
  • Firstpage
    1
  • Lastpage
    8
  • Abstract
    In this paper, a general class of hybrid Lotka-Volterra recurrent neural networks with memristor characteristics is formulated and studied. Some sufficient conditions on nondivergence, global attractivity and complete stability of the network are obtained, respectively. These results can be applied to the memristive dynamic memories. The analysis in the paper employs results from the theory of differential equations with discontinuous right-hand side as introduced by Filippov. These theoretical analysis can characterize the fundamental electrical properties of memristor devices and provide convenience for applications. A numerical example is given to illustrate the theoretical findings via computer simulations.
  • Keywords
    differential equations; memristors; recurrent neural nets; computer simulations; differential equations; discontinuous right-hand side; dynamic behaviors; electrical properties; hybrid Lotka-Volterra recurrent neural networks; memristive dynamic memories; memristor characteristics; memristor devices; network complete stability; network global attractivity; network nondivergence; Fires; Sensor arrays; Switches;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks (IJCNN), The 2012 International Joint Conference on
  • Conference_Location
    Brisbane, QLD
  • ISSN
    2161-4393
  • Print_ISBN
    978-1-4673-1488-6
  • Electronic_ISBN
    2161-4393
  • Type

    conf

  • DOI
    10.1109/IJCNN.2012.6252746
  • Filename
    6252746