• DocumentCode
    3099864
  • Title

    Sizing of the multilayer perceptron via modular networks

  • Author

    Chandrasekaran, Hema ; Kim, Kyung K. ; Manry, Michael T.

  • Author_Institution
    Dept. of Electr. Eng., Texas Univ., Arlington, TX, USA
  • fYear
    1999
  • fDate
    36373
  • Firstpage
    215
  • Lastpage
    224
  • Abstract
    A fast method for sizing the multilayer perceptron is proposed. The principal assumption is that a modular network with the same theoretical pattern storage as the multilayer perceptron has the same training error. This assumption is analyzed for the case of random patterns. Using several benchmark datasets, the validity of the approach is demonstrated
  • Keywords
    convergence; learning (artificial intelligence); multilayer perceptrons; probability; modular networks; random patterns; sizing method; theoretical pattern storage; training error; Aircraft propulsion; Clustering algorithms; Convergence; Electronic mail; Linear approximation; Multilayer perceptrons; Pattern analysis; Piecewise linear approximation; Piecewise linear techniques; Vectors;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks for Signal Processing IX, 1999. Proceedings of the 1999 IEEE Signal Processing Society Workshop.
  • Conference_Location
    Madison, WI
  • Print_ISBN
    0-7803-5673-X
  • Type

    conf

  • DOI
    10.1109/NNSP.1999.788140
  • Filename
    788140
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3099864