Name
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. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1109/FUZZY.2006.1681783 | 2006 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-5 |
Keywords | Field | DocType |
fuzzy logic,c4 5 algorithm,adaptive neuro fuzzy inference system,decision tree,maintenance engineering,neuro fuzzy,learning artificial intelligence,decision trees,membership function,decision tree classifier | Decision tree,Neuro-fuzzy,Fuzzy classification,Defuzzification,Pattern recognition,Computer science,Fuzzy logic,C4.5 algorithm,Artificial intelligence,Adaptive neuro fuzzy inference system,Machine learning,Decision tree learning | Conference |
ISSN | Citations | PageRank |
1098-7584 | 1 | 0.35 |
References | Authors | |
1 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shima Shirazi Kia | 1 | 1 | 0.35 |
| Siamak Noroozi | 2 | 9 | 4.93 |
| Brian Carse | 3 | 259 | 26.31 |
| John Vinney | 4 | 9 | 3.58 |
| Masoud Rabbani | 5 | 248 | 27.19 |