• DocumentCode
    1653229
  • Title

    Composition methods of fuzzy neural networks

  • Author

    Horikawa, Shin-ichi ; Furuhashi, Takeshi ; Okuma, Shigeru ; Uchikawa, Yoshiki

  • Author_Institution
    Dept. of Electron. Mech. Eng., Nagoya Univ., Japan
  • fYear
    1990
  • Firstpage
    1253
  • Abstract
    Fuzzy neural networks (FNNs) are systems which apply neural networks to fuzzy reasoning. Two types of FNN are presented. In the first type, the consequences of fuzzy reasoning are realized by constants. In the second type, the consequences are expressed by first-order linear equations. The FNNs can automatically identify fuzzy rules and tune membership functions. Their performance on fuzzy reasoning is examined by simulations. The features of the two types of FNNs are clarified
  • Keywords
    fuzzy logic; neural nets; first-order linear equations; fuzzy neural networks; fuzzy reasoning; fuzzy rule identification; membership function tuning; neural network composition; Fuzzy neural networks; Fuzzy reasoning; Fuzzy systems; Input variables; Marine vehicles; Mechanical engineering; Neural networks; Nonlinear systems;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Industrial Electronics Society, 1990. IECON '90., 16th Annual Conference of IEEE
  • Conference_Location
    Pacific Grove, CA
  • Print_ISBN
    0-87942-600-4
  • Type

    conf

  • DOI
    10.1109/IECON.1990.149317
  • Filename
    149317
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1653229