• DocumentCode
    2973012
  • Title

    NeuFuz: an intelligent combination of fuzzy logic with neural nets

  • Author

    Khan, Emdad

  • Author_Institution
    Embedded Syst. Div., Nat. Semicond. Corp., Santa Clara, CA, USA
  • Volume
    3
  • fYear
    1993
  • fDate
    25-29 Oct. 1993
  • Firstpage
    2945
  • Abstract
    A novel method is presented to combine neural nets with fuzzy logic. The combined technology, NeuFuz, generates fuzzy logic rules and membership functions by learning the system behavior using input-output data. The generated rules and membership functions are then processed using new fuzzy logic algorithms for defuzzification, rule evaluation and antecedent processing which are developed based on neural network architecture and learning. These fuzzy logic algorithms replace conventional heuristic fuzzy logic algorithms and enable full mapping of neural net to fuzzy logic. Full mapping provides an important key feature of generating fuzzy rules and membership functions to meet a pre-specified accuracy level. NeuFuz also significantly improves performance, reliability, reduces design time and minimizes system cost by optimizing number of rules and membership functions.
  • Keywords
    fuzzy logic; fuzzy neural nets; fuzzy set theory; learning (artificial intelligence); NeuFuz; antecedent processing; defuzzification; fuzzy logic rule generation; fuzzy neural net; membership functions; neural nets; neural network architecture; rule evaluation; system behavior learning; Cost function; Fuzzy logic; Fuzzy neural networks; Fuzzy systems; Heuristic algorithms; Inference algorithms; Mathematical model; Neural networks; Neurons; Process design;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Neural Networks, 1993. IJCNN '93-Nagoya. Proceedings of 1993 International Joint Conference on
  • Print_ISBN
    0-7803-1421-2
  • Type

    conf

  • DOI
    10.1109/IJCNN.1993.714340
  • Filename
    714340