• DocumentCode
    2895640
  • Title

    Generating Weighted Fuzzy Production Rules using Neural Networks

  • Author

    Fan, Tie-gang ; Wang, Shu-tian ; Chen, Jun-Min

  • Author_Institution
    Fac. of Math. & Comput. Sci., Hebei Univ., Baoding
  • fYear
    2006
  • fDate
    13-16 Aug. 2006
  • Firstpage
    3059
  • Lastpage
    3062
  • Abstract
    Weighted fuzzy production rules enhance the knowledge representation power of rule. This paper proposes a way to generate weighted fuzzy production rules using neural networks. First the knowledge in the data is transformed into neural network. Through analysis of the weights of the neural network, a matrix of importance index is constructed. Then weighted fuzzy production rules are extracted from the neural network. In order to reflect the knowledge implied in the neural network accurately, a corresponding reasoning algorithm is constructed. The effective of the approach is demonstrated by the experiment
  • Keywords
    data mining; fuzzy set theory; inference mechanisms; knowledge representation; learning (artificial intelligence); matrix algebra; neural nets; knowledge representation; matrix algebra; neural network; reasoning algorithm; rule extraction; weighted fuzzy production rule generation; Artificial neural networks; Cybernetics; Data mining; Fuzzy neural networks; Fuzzy reasoning; Fuzzy sets; Knowledge representation; Machine learning; Machine learning algorithms; Neural networks; Production; Rules extraction; neural networks; reasoning algorithm; weighted fuzzy production rules;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Machine Learning and Cybernetics, 2006 International Conference on
  • Conference_Location
    Dalian, China
  • Print_ISBN
    1-4244-0061-9
  • Type

    conf

  • DOI
    10.1109/ICMLC.2006.258366
  • Filename
    4028589