• DocumentCode
    2516803
  • Title

    Design and learning with cellular neural networks

  • Author

    Nossek, Josef A.

  • Author_Institution
    Inst. for Network Theory & Circuit Design, Tech. Univ. Munchen, Germany
  • fYear
    1994
  • fDate
    18-21 Dec 1994
  • Firstpage
    137
  • Lastpage
    146
  • Abstract
    The template coefficients (weights) of a CNN, which will give a desired performance, can either be found by design or by learning; “By design” means, that the desired function to be performed could be translated into a set of local dynamic rules, while “by learning” is based exclusively on pairs of input and corresponding output signals, the relationship of which may be by far too complicated for the explicit formulation of local rules. An overview of design and learning methods applicable to CNNs, which sometimes are not clearly distinguishable,is given here. Both technological constraints imposed by specific hardware implementation and practical constraints caused by the specific application and system embedding are influencing design and learning
  • Keywords
    cellular neural nets; learning (artificial intelligence); cellular neural networks; learning; local dynamic rules; system embedding; technological constraints; template coefficients; Cellular neural networks; Circuit synthesis; Design methodology; Design optimization; Electronic mail; Equations; Hardware; Image processing; Linear feedback control systems; Signal design;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Cellular Neural Networks and their Applications, 1994. CNNA-94., Proceedings of the Third IEEE International Workshop on
  • Conference_Location
    Rome
  • Print_ISBN
    0-7803-2070-0
  • Type

    conf

  • DOI
    10.1109/CNNA.1994.381694
  • Filename
    381694