• DocumentCode
    1749250
  • Title

    Equivalence between neural networks and fuzzy systems

  • Author

    Gaweda, Adam E. ; Zurada, Jacek M.

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Louisville Univ., KY, USA
  • Volume
    2
  • fYear
    2001
  • fDate
    2001
  • Firstpage
    1334
  • Abstract
    Demonstrates that a single-hidden layer feedforward neural network is equivalent to a fuzzy inference system with relational rule antecedents. The method establishes a link between networks weights and fuzzy system parameters and defines the upper bound on the number of fuzzy rules required to represent the network. An application example illustrates the proposed idea
  • Keywords
    feedforward neural nets; fuzzy logic; fuzzy systems; transfer functions; fuzzy inference system; networks weights; relational rule antecedents; single-hidden layer feedforward neural network; Computer networks; Feedforward neural networks; Fuzzy neural networks; Fuzzy sets; Fuzzy systems; Merging; Neural networks; Nonlinear systems; Transfer functions; Upper bound;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
  • Conference_Location
    Washington, DC
  • ISSN
    1098-7576
  • Print_ISBN
    0-7803-7044-9
  • Type

    conf

  • DOI
    10.1109/IJCNN.2001.939555
  • Filename
    939555
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1749250