• DocumentCode
    2736179
  • Title

    Defuzzication using polynomial approximation

  • Author

    Mustafa, M. Marzuki

  • Author_Institution
    Fac. of Eng., Univ. Kebangsaan Malaysia, Selangor, Malaysia
  • Volume
    3
  • fYear
    2000
  • fDate
    2000
  • Firstpage
    342
  • Abstract
    A new fuzzy inference based on piecewise polynomial interpolation similar to spline technique is proposed. The signed membership function which can encode more information than the usual membership function is also introduced and used together with this new inference method. The fuzzy system using this inference method is more compact compared to other types of fuzzy systems. Online recursive training algorithm is also proposed to tune these polynomials if numerical training data is available. In contrast to neural network where the trained network is only a function of training data, both the heuristic prior knowledge and available training data are used. A simulation example is given to show how this new fuzzy inference can be applied in model reference closed-loop control system
  • Keywords
    closed loop systems; fuzzy logic; fuzzy set theory; fuzzy systems; inference mechanisms; interpolation; learning (artificial intelligence); closed-loop control; fuzzy inference; fuzzy system; membership function; piecewise polynomial interpolation; polynomial approximation; recursive training; Fuzzy logic; Fuzzy neural networks; Fuzzy sets; Fuzzy systems; Inference algorithms; Input variables; Interpolation; Neural networks; Polynomials; Training data;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    TENCON 2000. Proceedings
  • Conference_Location
    Kuala Lumpur
  • Print_ISBN
    0-7803-6355-8
  • Type

    conf

  • DOI
    10.1109/TENCON.2000.892286
  • Filename
    892286