This source preferred by John Vinney and Siamak Noroozi

Authors: Shrazi Kia, S., Noroozi, S., Carse, B., Vinney, J. and Rabbani, M.

Pages: 671-677

Publisher: IEEE

DOI: 10.1109/FUZZY.2006.1681783

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.

This data was imported from DBLP:

Authors: Kia, S.S., Noroozi, S., Carse, B., Vinney, J. and Rabbani, M.

http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=11093

Pages: 671-677

Publisher: IEEE

This data was imported from Scopus:

Authors: Kia, S.S., Noroozi, S., Carse, B., Vinney, J. and Rabbani, M.

Pages: 671-677

ISBN: 9780780394889

DOI: 10.1109/FUZZY.2006.1681783

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.

This data was imported from Web of Science (Lite):

Authors: Kia, S.S., Noroozi, S., Carse, B., Vinney, J., Rabbani, M. and IEEE

Pages: 671-+

ISBN: 978-0-7803-9488-9