• DocumentCode
    958489
  • Title

    Geometrical learning algorithm for multilayer neural networks in a binary field

  • Author

    Park, Sung-Kwon ; Kim, Jung H.

  • Author_Institution
    Dept. of Electron. Commun. Eng., Hanyang Univ., Seoul, South Korea
  • Volume
    42
  • Issue
    8
  • fYear
    1993
  • fDate
    8/1/1993 12:00:00 AM
  • Firstpage
    988
  • Lastpage
    992
  • Abstract
    A geometrical expansion learning algorithm for multilayer neural networks using unipolar binary neurons with integer connection weights, which guarantees convergence for any Boolean function, is introduced. Neurons in the hidden layer develop as necessary without supervision. In addition, the computational amount is much less than that of the backpropagation algorithm
  • Keywords
    Boolean functions; feedforward neural nets; learning (artificial intelligence); Boolean function; binary field; geometrical learning algorithm; hidden layer; integer connection weights; multilayer neural networks; unipolar binary neurons; Backpropagation algorithms; Fault diagnosis; Hypercubes; IEEE Computer Society Press; Intelligent networks; Interconnected systems; Multi-layer neural network; Multiprocessing systems; Neural networks; Neurons;
  • fLanguage
    English
  • Journal_Title
    Computers, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9340
  • Type

    jour

  • DOI
    10.1109/12.238491
  • Filename
    238491
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=958489