• DocumentCode
    2416365
  • Title

    Prediction of Failure in Pin-joints Using Hybrid Adaptive Neuro-Fuzzy Approach

  • Author

    Kia, S. Shirazi ; Noroozi, S. ; Carse, B. ; Vinney, J. ; Rabbani, M.

  • Author_Institution
    Univ. of the West England, Bristol
  • fYear
    0
  • fDate
    0-0 0
  • Firstpage
    671
  • Lastpage
    677
  • Abstract
    An analysis was performed to evaluate the strength of pin-loaded composite and aluminum joints. The analysis involved using three classifiers: decision tree, adaptive neuro fuzzy inference system and the combination of two. By using the well-known C4.5 algorithm, as a quick process, the structure of fuzzy inference system (number of membership functions and fuzzy rules) could be roughly estimated. Then, the parameter identification is carried out by adaptive neuro-fuzzy system. The comparison of performance of three methods indicates that mentioned hybridization speeds up learning processes and reduced errors.
  • Keywords
    decision trees; failure (mechanical); fuzzy logic; fuzzy neural nets; fuzzy reasoning; learning (artificial intelligence); maintenance engineering; pattern classification; structural engineering computing; C4.5 algorithm; adaptive neuro fuzzy inference system; aluminum joint; decision tree classifier; learning process; parameter identification; pin-joint failure prediction; Adaptive systems; Aluminum; Classification tree analysis; Decision trees; Fuzzy neural networks; Fuzzy systems; Inference algorithms; Parameter estimation; Performance analysis; Performance evaluation;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Fuzzy Systems, 2006 IEEE International Conference on
  • Conference_Location
    Vancouver, BC
  • Print_ISBN
    0-7803-9488-7
  • Type

    conf

  • DOI
    10.1109/FUZZY.2006.1681783
  • Filename
    1681783