Name
Abstract | ||
|---|---|---|
This study proposed to employ Cross-Validation (CV) to evaluate reliability of the strength models generated by nonlinear regression analysis (NLRA), artificial neural network (ANN), and genetic operation tree (GOT), to make more sound comparisons between them. It was found that (1) the ANN was the most accurate modeling tool for the Low, Medium, and High water-binder ratio (w/b) data sets; (2) using t-statistic, under 1% of level of significance, GOT was more accurate than NLRA for the Low and the Medium w/b data sets. (3) GOT can generate creative formulas consisting with domain knowledge. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/ICMLC.2010.5580800 | ICMLC |
Keywords | Field | DocType |
t-statistic,concrete strength modeling,mechanical strength,nonlinear regression analysis,concrete,trees (mathematics),regression analysis,genetic operation trees,operation trees,ann,genetic algorithms,reliability,artificial neural network,cross-validation,mechanical engineering computing,neural nets,cross validation,domain knowledge,data models,artificial neural networks,mathematical model,genetic operator,optimization,genetics,t statistic,genetic algorithm,nonlinear regression | Data modeling,Data set,Regression analysis,Computer science,Nonlinear regression,Algorithm,Artificial intelligence,t-statistic,Artificial neural network,Cross-validation,Machine learning,Genetic algorithm | Conference |
Volume | ISBN | Citations |
3 | 978-1-4244-6526-2 | 1 |
PageRank | References | Authors |
0.39 | 2 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| I-Cheng Yeh | 1 | 339 | 22.45 |
| Che-hui Lien | 2 | 120 | 7.11 |
| Chien-Hua Peng | 3 | 29 | 2.20 |
| Li-Chuan Lien | 4 | 44 | 3.82 |