• DocumentCode
    315256
  • Title

    An upper bound on the node complexity of depth-2 multilayer perceptrons

  • Author

    Arai, Masahiko

  • Author_Institution
    Dept. of Inf. & Commun. Technol., Tokai Univ., Shizuoka, Japan
  • Volume
    2
  • fYear
    1997
  • fDate
    9-12 Jun 1997
  • Firstpage
    1144
  • Abstract
    This paper shows that an upper bound on the node complexity of depth-2 perceptrons is 2N-2+2 for N-dimensional binary valued inputs. This result is obtained by structuring an N-dimensional hypercube. It is shown that the nodes are divided into the totally ordered sets of linearly independent nodes. For each set there is a hyperplane corresponding to one of the hidden units of a depth-2 perceptron which recognizes input patterns. The result of this paper is given by estimating the number of the sets
  • Keywords
    hypercube networks; multilayer perceptrons; neural net architecture; artificial neural networks; depth-2 multilayer perceptrons; hyperplane; linearly independent nodes; mulitdimensional binary valued inputs; multidimensional hypercube; node complexity; totally ordered sets; upper bound; Artificial neural networks; Circuits; Communications technology; Computer networks; Hypercubes; Logic; Multilayer perceptrons; Nonhomogeneous media; Pattern recognition; Upper bound;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks,1997., International Conference on
  • Conference_Location
    Houston, TX
  • Print_ISBN
    0-7803-4122-8
  • Type

    conf

  • DOI
    10.1109/ICNN.1997.616192
  • Filename
    616192
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=315256