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

Shrazi Kia, S., Noroozi, S., Carse, B., Vinney, J. and Rabbani, M., 2006. Prediction of Failure in Pin-Joints Using Hybrid Adaptive Neuro-Fuzzy Approach. In: IEEE 2006 International Conference on Fuzzy Systems, 16-21 July 2006. IEEE , pp. 671-677.

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DOI: 10.1109/FUZZY.2006.1681783

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.

Item Type:Book Section
ISBN:0-7803-9488-7
Uncontrolled Keywords:C4.5 algorithm , adaptive neuro fuzzy inference system , aluminum joint , decision tree classifier , learning process , parameter identification , pin-joint failure prediction
Subjects:Technology > Manufacturing and Design > Design
Group:School of Design, Engineering & Computing > Design Simulation Research Centre
ID Code:7487
Deposited By:INVALID USER
Deposited On:21 Nov 2008 19:07
Last Modified:07 Mar 2013 14:57
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