• DocumentCode
    1644015
  • Title

    Robustness of attractor networks and an improved convex corner detector

  • Author

    Nachbar, P. ; Schuler, A.J. ; Füssl, T. ; Nossek, Josef A. ; Chua, Leon O.

  • Author_Institution
    Inst. of Network Theory & Circuit Design, Tech. Univ. of Munich, Germany
  • fYear
    1992
  • Firstpage
    55
  • Lastpage
    61
  • Abstract
    The authors point out that by defining several notions of robustness for an attractor network, it is possible to augment previous results about the AdaTron algorithm by explicit values for the robustness of the optimal weights. It is shown that the symmetry of a problem is reflected by the invariance of the optimal weights. This enables one to deduce that a convex corner detection, using a discrete-time cellular neural network (DTCNN), cannot be accomplished with just one clock cycle, and an improved convex corner detector is proposed
  • Keywords
    edge detection; invariance; neural nets; AdaTron algorithm; attractor networks; convex corner detector; discrete-time cellular neural network; image recognition; invariance; optimal weights; robustness; Cellular neural networks; Clocks; Constraint theory; Detectors; Educational institutions; Neurons; Quadratic programming; Reflection; Robustness; Stability;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Cellular Neural Networks and their Applications, 1992. CNNA-92 Proceedings., Second International Workshop on
  • Conference_Location
    Munich
  • Print_ISBN
    0-7803-0875-1
  • Type

    conf

  • DOI
    10.1109/CNNA.1992.274355
  • Filename
    274355