• DocumentCode
    1903728
  • Title

    Recursive neural networks with high capacity

  • Author

    Chen, Chang-Jiu ; Cheung, John Y.

  • Author_Institution
    Sch. of Comput. Sci., Oklahoma Univ., Norman, OK, USA
  • fYear
    1993
  • fDate
    1993
  • Firstpage
    462
  • Abstract
    √3n-1 is derived as the lower bound of maximum capacity in n-neuron recursive neural networks. It is shown that if n→∞, the number of stable vectors of (n +1)-neuron net is two times that of n-neuron net and the number of stable vectors of n-neuron net is C2n with 0<C<1. To obtain these results, the SOR method proposed by Oh and Kothari is employed
  • Keywords
    neural nets; relaxation theory; stability; SOR method; maximum capacity; n-neuron recursive neural networks; stable vectors; successive over-relaxation; Associative memory; CADCAM; Computer aided manufacturing; Computer science; Equations; Geometry; Hebbian theory; Hypercubes; Neural networks; Neurons;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1993., IEEE International Conference on
  • Conference_Location
    San Francisco, CA
  • Print_ISBN
    0-7803-0999-5
  • Type

    conf

  • DOI
    10.1109/ICNN.1993.298601
  • Filename
    298601
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1903728