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.
Full text not available from this repository.
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 |
| Repository Staff Only - | |
| BU Staff Only - | |
| Help Guide - | Editing Your Items in BURO |

Tools
Tools