• DocumentCode
    3494177
  • Title

    Analysis and Robust Design for Illusion CNNs

  • Author

    Li, Weidong ; Min, Lequan

  • Author_Institution
    Univ. of Sci. & Technol. Beijing, Beijing
  • fYear
    2008
  • fDate
    6-8 April 2008
  • Firstpage
    827
  • Lastpage
    831
  • Abstract
    The Cellular Neural Network (CNN), proposed by Chua et al in 1988, is a neural network with local connectivity. The CNN has been applied to the fields of image processing, pattern identification, biological visions and so on. The robust design is one of the important issues for the researches in CNN. This paper establishes the local rules of the Muller-Lyer Illusion (MLI) CNN introduced by Chua et. al. The robust design for the MLI CNN is given. A Ponzo Illusion (PI) CNN is introduced. The local rules and the theorem for the PI CNN are set up. Six numerical simulation examples are provided to illustrate the effectiveness of the methodology.
  • Keywords
    cellular neural nets; Muller-Lyer illusion cellular neural network; biological vision; image processing; local connectivity; pattern identification; robust design; Cellular neural networks; Design engineering; Displays; Hopfield neural networks; Image edge detection; Image processing; Mathematical model; Neural networks; Numerical simulation; Robustness;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Networking, Sensing and Control, 2008. ICNSC 2008. IEEE International Conference on
  • Conference_Location
    Sanya
  • Print_ISBN
    978-1-4244-1685-1
  • Electronic_ISBN
    978-1-4244-1686-8
  • Type

    conf

  • DOI
    10.1109/ICNSC.2008.4525330
  • Filename
    4525330