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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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|
|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|
|Deposited By:||INVALID USER|
|Deposited On:||21 Nov 2008 19:07|
|Last Modified:||07 Mar 2013 14:57|
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