Prediction of failure in pin-joints using hybrid adaptive neuro-fuzzy approach
Authors: Kia, S.S., Noroozi, S., Carse, B., Vinney, J. and Rabbani, M.
Pages: 671-677
ISBN: 9780780394889
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. © 2006 IEEE.
Source: Scopus
Prediction of failure in pin-joints using hybrid adaptive neuro-fuzzy approach
Authors: Kia, S.S., Noroozi, S., Carse, B., Vinney, J., Rabbani, M. and IEEE
Pages: 671-+
ISBN: 978-0-7803-9488-9
Source: Web of Science (Lite)
Prediction of Failure in Pin-Joints Using Hybrid Adaptive Neuro-Fuzzy Approach
Authors: Shrazi Kia, S., Noroozi, S., Carse, B., Vinney, J. and Rabbani, M.
Pages: 671-677
Publisher: IEEE
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.
Source: Manual
Preferred by: John Vinney and Siamak Noroozi
Prediction of Failure in Pin-joints Using Hybrid Adaptive Neuro-Fuzzy Approach.
Authors: Kia, S.S., Noroozi, S., Carse, B., Vinney, J. and Rabbani, M.
Pages: 671-677
Publisher: IEEE
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=11093
Source: DBLP