• DocumentCode
    2386476
  • Title

    Learning method of fuzzy functional inference model

  • Author

    Seki, Hirosato ; Mizumoto, Masaharu

  • Author_Institution
    Dept. of Technol. Manage., Osaka Inst. of Technol., Osaka, Japan
  • fYear
    2011
  • fDate
    9-12 Oct. 2011
  • Firstpage
    3524
  • Lastpage
    3529
  • Abstract
    Seki and Mizumoto have proposed a fuzzy functional inference method in which the consequent parts of the T-S inference method is generalized to fuzzy functions, However, it is noted that the adjustment of fuzzy functions is generally difficult. Therefore, in this paper, we propose a learning method of the fuzzy functional inference method by using the base of the fuzzy functions. Moreover, the fuzzy functional inference method by using the proposed learning method is shown to be superior to the conventional method by applying to a medical diagnosis as an example of application to real systems.
  • Keywords
    fuzzy set theory; inference mechanisms; learning (artificial intelligence); medical computing; patient diagnosis; T-S inference method; consequent parts; fuzzy functional inference model; learning method; medical diagnosis; Bismuth; Diabetes; Fuzzy sets; Gravity; Learning systems; Medical diagnosis; Silicon; Fuzzy inference; base function; fuzzy function; fuzzy functional inference method; medical diagnosis;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Systems, Man, and Cybernetics (SMC), 2011 IEEE International Conference on
  • Conference_Location
    Anchorage, AK
  • ISSN
    1062-922X
  • Print_ISBN
    978-1-4577-0652-3
  • Type

    conf

  • DOI
    10.1109/ICSMC.2011.6084215
  • Filename
    6084215