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

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. and Rabbani, M.

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

https://ieeexplore.ieee.org/xpl/conhome/11093/proceeding

Source: DBLP