Name
Abstract | ||
|---|---|---|
The running status of hydraulic tube tester is reflected by the boosting pressure curve in Hydrostatic testing process. The authors present the extreme learning machine (ELM), a novel good learning scheme much faster than traditional gradient-based learning algorithms, as a mechanism for clustering the pressure curves. However, it caused low accuracy for clustering pressure curves for hydraulic tube tester. In this paper, a multi-stage ELM is proposed to improve the accuracy of clustering. During the process of this new ELM, the input data were divided into several stages, then, every stage was analyzed independently. At last, this method has been used in hydraulic tube tester data. Compared with individual ELM, it has better function for considering the characteristics of input data. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1007/s00521-007-0139-1 | Neural Computing and Applications |
Keywords | DocType | Volume |
clustering pressure curve,individual ELM,fault diagnosis,input data,Multi-stage extreme,new ELM,hydraulic tube tester,novel good learning scheme,hydraulic tube tester data,pressure curve,extreme learning machine,multi-stage ELM | Journal | 17 |
Issue | ISSN | Citations |
4 | 1433-3058 | 8 |
PageRank | References | Authors |
0.61 | 6 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xue-fa Hu | 1 | 10 | 1.11 |
| Zhen Zhao | 2 | 8 | 0.61 |
| Shu Wang | 3 | 8 | 0.61 |
| Fu-li Wang | 4 | 11 | 3.30 |
| Da-kuo He | 5 | 9 | 4.02 |
| Shui-kang Wu | 6 | 8 | 0.61 |