Name
Abstract | ||
|---|---|---|
In daily life fault diagnosis is widely used production. With the rapid development of science and technology, the new high-tech products emerged. It is not enough data of samples. Conventional approach is ineffective. It is need to find a good method. The least squares support vector machine algorithm and the proximal of support vector machine applied to fault diagnosis. Through experiments when learning samples is not enough, equipment failure does not reduce and the classification accuracy has increased even. On fault diagnosis the training speed has been to improve and the cost of building has been reduced. Improve overall system performance of fault diagnosis. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-27452-7_26 | Communications in Computer and Information Science |
Keywords | Field | DocType |
Support Vector Machine,Least squares,Proximal Support Vector Machine,Fault Diagnosis | Least squares,Data mining,Fault coverage,Least squares support vector machine,Computer science,Support vector machine,Artificial intelligence,Machine learning | Conference |
Volume | Issue | ISSN |
244 | PART 2 | 1865-0929 |
Citations | PageRank | References |
1 | 0.45 | 4 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yongli Zhang | 1 | 1 | 1.13 |
| Yanwei Zhu | 2 | 10 | 3.64 |
| Shu-Fei Lin | 3 | 4 | 4.57 |
| Xiaohong Liu | 4 | 1 | 0.45 |