• DocumentCode
    2272441
  • Title

    Self-tuning fuzzy inference based on spline function

  • Author

    Shimojima, Koji ; Fukuda, Toshio ; Arai, Fumihito

  • Author_Institution
    Dept. of Mechano-Inf. & Syst., Nagoya Univ., Japan
  • fYear
    1994
  • fDate
    26-29 Jun 1994
  • Firstpage
    690
  • Abstract
    Recently, fuzzy systems are used in many fields and places. In order to apply the fuzzy system to wider fields, it is necessary to study the tuning methods of the fuzzy system. Some self-tuning methods were proposed so far. However these conventional self-tuning methods do not have sufficient capability of generalization. In this paper, we propose new self-tuning fuzzy neural networks. The fuzzy neural networks consist of membership functions that are expressed by spline function. Delta rule is applied to tune the membership functions and consequent parts. The effectiveness of the proposed methods is shown by some numerical examples
  • Keywords
    fuzzy neural nets; fuzzy set theory; generalisation (artificial intelligence); inference mechanisms; self-adjusting systems; splines (mathematics); Delta rule; fuzzy neural networks; fuzzy systems; generalization; membership functions; self-tuning fuzzy inference; spline function; Biomedical engineering; Fuzzy control; Fuzzy logic; Fuzzy neural networks; Fuzzy reasoning; Fuzzy systems; Input variables; Neural networks; Shape control; Spline;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Fuzzy Systems, 1994. IEEE World Congress on Computational Intelligence., Proceedings of the Third IEEE Conference on
  • Conference_Location
    Orlando, FL
  • Print_ISBN
    0-7803-1896-X
  • Type

    conf

  • DOI
    10.1109/FUZZY.1994.343652
  • Filename
    343652