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
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;
Conference_Titel :
Fuzzy Systems, 2006 IEEE International Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-9488-7
DOI :
10.1109/FUZZY.2006.1681783