• DocumentCode
    2739247
  • Title

    Neural networks with node gates

  • Author

    Myint, H.M. ; Murata, J. ; Nakazono, T. ; Hirasawa, K.

  • Author_Institution
    Graduate Sch. of Inf. Sci. & Electr. Eng., Kyushu Univ., Fukuoka, Japan
  • fYear
    2000
  • fDate
    2000
  • Firstpage
    253
  • Lastpage
    257
  • Abstract
    Function approximation problems for ordinary neural networks may be rather difficult, if the function becomes complicated, due to the necessity of big network size and the possibilities of many local minima. A promising way to solve these difficulties is the localization of the problem. According to this concept, a new architecture of a neural network is proposed namely neural network with node gates. In the paper, a function approximation example is provided to demonstrate the better performance of the proposed network than the ordinary neural network
  • Keywords
    feedforward neural nets; function approximation; learning (artificial intelligence); multilayer perceptrons; neural net architecture; local minima; localization; node gates; Artificial neural networks; Biological neural networks; Brain modeling; Feedforward neural networks; Feeds; Function approximation; Fuzzy neural networks; Information science; Neural networks; Transfer functions;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Robot and Human Interactive Communication, 2000. RO-MAN 2000. Proceedings. 9th IEEE International Workshop on
  • Conference_Location
    Osaka
  • Print_ISBN
    0-7803-6273-X
  • Type

    conf

  • DOI
    10.1109/ROMAN.2000.892504
  • Filename
    892504