• DocumentCode
    2135290
  • Title

    Neufuz: neural network based fuzzy logic design algorithms

  • Author

    Khan, Emdad ; Venkatapuram, Prahlad

  • Author_Institution
    Nat. Semiconductor, Santa Clara, CA, USA
  • fYear
    1993
  • fDate
    1993
  • Firstpage
    647
  • Abstract
    A novel fuzzy logic design, called Neufuz, using neural net learning is proposed. Artificial neural net algorithms are used to generate fuzzy rules and membership functions. The combination of learned fuzzy rules, membership functions, and a fuzzy design technique based on new fuzzy inferencing and defuzzification methods significantly improves performance, accuracy, and reliability and reduces design time. Neufuz also minimizes system cost by optimizing the number of rules and membership functions. Simulation results are very encouraging
  • Keywords
    fuzzy logic; fuzzy set theory; inference mechanisms; logic CAD; neural nets; uncertainty handling; Neufuz; defuzzification; fuzzy inferencing; fuzzy logic design; fuzzy rules; learning; membership functions; neural net; Algorithm design and analysis; Artificial neural networks; Biological neural networks; Embedded system; Fuzzy logic; Fuzzy neural networks; Fuzzy systems; Inference algorithms; Mathematical model; Neural networks;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Fuzzy Systems, 1993., Second IEEE International Conference on
  • Conference_Location
    San Francisco, CA
  • Print_ISBN
    0-7803-0614-7
  • Type

    conf

  • DOI
    10.1109/FUZZY.1993.327412
  • Filename
    327412