• DocumentCode
    1577237
  • Title

    Continual neural networks

  • Author

    Galushkin, A.I.

  • Author_Institution
    Sci. Neurocomput. Centre, Acad. of Sci., Moscow, Russia
  • fYear
    1992
  • Firstpage
    1056
  • Abstract
    It is necessary to introduce many parameters describing the structure and input signal of a pattern recognition system during the construction of open-loop structures of multilayer neural networks in order to provide maximum probability of correct recognition in practice. The availability of a large number of parameters, i.e., hundreds and thousands, poses some difficulties for learning and for the technical implementation of such networks. A transition to an attributes continuum and a continuum of neurons in the layer is considered for some specific neural network structures
  • Keywords
    feedforward neural nets; pattern recognition; input signal; learning; multilayer neural networks; neurons; open-loop structures; pattern recognition system; Artificial neural networks; Concrete; Image sampling; Multi-layer neural network; Neural networks; Neurons; Optical fiber networks; Particle beam optics; Pattern recognition; Signal generators;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neuroinformatics and Neurocomputers, 1992., RNNS/IEEE Symposium on
  • Conference_Location
    Rostov-on-Don
  • Print_ISBN
    0-7803-0809-3
  • Type

    conf

  • DOI
    10.1109/RNNS.1992.268523
  • Filename
    268523