• DocumentCode
    2400014
  • Title

    Uniform approximation and the complexity of neural networks

  • Author

    Ferreira, Paulo J S G ; Cao, Si-Qi

  • Author_Institution
    Dept. de Electron. e Telecoms, Aveiro Univ., Portugal
  • fYear
    1997
  • fDate
    24-26 Sep 1997
  • Firstpage
    141
  • Lastpage
    148
  • Abstract
    Studies some of the approximating properties of feedforward neural networks as a function of the number of nodes. Two cases are considered: sigmoidal and radial basis function networks. Bounds for the approximation error are given. The methods through which we arrive at the bounds are constructive. The error studied is the L∞ or sup error
  • Keywords
    computational complexity; feedforward neural nets; function approximation; L∞ error; approximating properties; approximation error; complexity; feedforward neural networks; radial basis function networks; sigmoidal networks; sup error; uniform approximation; Approximation error; Computer networks; Electronic mail; Feedforward neural networks; Feedforward systems; Neural networks; Radial basis function networks; Radiofrequency interference;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks for Signal Processing [1997] VII. Proceedings of the 1997 IEEE Workshop
  • Conference_Location
    Amelia Island, FL
  • ISSN
    1089-3555
  • Print_ISBN
    0-7803-4256-9
  • Type

    conf

  • DOI
    10.1109/NNSP.1997.622392
  • Filename
    622392
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2400014