• DocumentCode
    2432231
  • Title

    Real-time pattern recognition. I. Neural network algorithms for normal models

  • Author

    Mallya, Satyanarayana ; Jannarone, Robert

  • Author_Institution
    Dept. of Electr. & Comput. Eng., South Carolina Univ., Columbia, SC, USA
  • fYear
    1991
  • fDate
    10-12 Mar 1991
  • Firstpage
    580
  • Lastpage
    583
  • Abstract
    Traditional neural network models, when contrasted with normal models, have some distinct similarities and differences. The paper describes the salient features of perceptrons and their extensions, relative to normal models. Conjunctoid neural network models when contrasted with normal models also have some relative advantages, the paper describes these advantages and disadvantages. Normal neural network models are introduced which combine some of the features of the conjunctoid and traditional normal models. The paper also introduces the algorithm to implement a normal neural network model for the airplane example described in an earlier part of the paper. The paper briefly describes the architectures for implementing the algorithms on parallel machines, along with conclusions and future research directions
  • Keywords
    computerised pattern recognition; neural nets; parallel algorithms; parallel architectures; conjunctoid models; neural network models; normal models; parallel machines; Airplanes; Algorithm design and analysis; Covariance matrix; Libraries; Neural networks; Parameter estimation; Pattern recognition; Predictive models; Statistics; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    System Theory, 1991. Proceedings., Twenty-Third Southeastern Symposium on
  • Conference_Location
    Columbia, SC
  • ISSN
    0094-2898
  • Print_ISBN
    0-8186-2190-7
  • Type

    conf

  • DOI
    10.1109/SSST.1991.138634
  • Filename
    138634