• DocumentCode
    2286096
  • Title

    mL-CNN: a CNN model for reaction-diffusion processes in m-component systems

  • Author

    Selikhov, A.

  • Author_Institution
    Inst. of Computational Math. & Math. Geophys., Acad. of Sci., Novosibirsk, Russia
  • fYear
    2002
  • fDate
    22-24 Jul 2002
  • Firstpage
    98
  • Lastpage
    106
  • Abstract
    A mL-CNN is presented in this paper as a generalization of CNN models of reaction-diffusion processes in nonlinear media with m components. Main properties of the model are considered in accordance with imaginations of the process "mechanisms". Two particular CNN models, an autonomous 2L-CNN and a 2L-CNN with external inputs, are presented as examples of special cases of the mL-CNN. Emergence of some complex phenomena in such particular models are also shown.
  • Keywords
    cellular neural nets; digital simulation; physics computing; reaction-diffusion systems; autonomous 2L-CNN; cellular neural networks; external inputs; mL-CNN; multicomponent systems; nonlinear media; reaction-diffusion processes; Cellular neural networks; Circuit simulation; Electronic circuits; Geophysics computing; Mathematical model; Mathematics; Neurons; Piecewise linear techniques;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Cellular Neural Networks and Their Applications, 2002. (CNNA 2002). Proceedings of the 2002 7th IEEE International Workshop on
  • Print_ISBN
    981-238-121-X
  • Type

    conf

  • DOI
    10.1109/CNNA.2002.1035041
  • Filename
    1035041