• DocumentCode
    1654012
  • Title

    Representing generalized fuzzy automata in recurrent neural networks

  • Author

    Doostfatemeh, Mansoor ; Kremer, Stefan C.

  • Author_Institution
    CIS Dept, Guelph Univ., Ont., Canada
  • Volume
    4
  • fYear
    2004
  • Firstpage
    1901
  • Abstract
    In this paper we present a new architecture for the representation of general fuzzy automata (GFA). It is based on second-order recurrent neural networks (2ORNN). The architecture implements the functions F1 and F2, used in GFA, into the structure of 2ORNN. The performance of this representation method is compared with a previous method for embedding fuzzy automata, and it is shown that GFA are more efficiently representable in 2ORNN.
  • Keywords
    automata theory; fuzzy neural nets; fuzzy set theory; knowledge representation; recurrent neural nets; 2ORNN; GFA representation; generalized fuzzy automata; neural network architecture; performance; second-order recurrent neural networks; Automata; Computational Intelligence Society; Fuzzy neural networks; Fuzzy sets; History; Intelligent networks; Iris; Neural networks; Recurrent neural networks;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electrical and Computer Engineering, 2004. Canadian Conference on
  • ISSN
    0840-7789
  • Print_ISBN
    0-7803-8253-6
  • Type

    conf

  • DOI
    10.1109/CCECE.2004.1347583
  • Filename
    1347583
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1654012