Name
Abstract | ||
|---|---|---|
•The classification accuracy is improved by 5.52% after using the genetic algorithm.•The fault detection time of the Support Vector Domain Description (SVDD) algorithm is always pre-emptive when compared to the red line shutdown system, which is superior to the Back Propagation algorithm.•The SVDD algorithm based on the modified kernel function can significantly increase the separability between categories.•The method of drawing the SVDD model boundary based on equal loss involves smaller risks compared to other cutting planes and minimizes the losses caused by classifications. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1016/j.compeleceng.2017.06.016 | Computers & Electrical Engineering |
Keywords | Field | DocType |
Fault detection,Support vector domain description,Aircraft,Modifying kernel function | Scale factor,Anomaly detection,Fault coverage,Computer science,Fault detection and isolation,Support vector machine,Real-time computing,Classification rate,Genetic algorithm,Kernel (statistics) | Journal |
Volume | Issue | ISSN |
61 | C | 0045-7906 |
Citations | PageRank | References |
1 | 0.34 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yaoming Zhou | 1 | 16 | 4.48 |
| Kan Wu | 2 | 71 | 12.75 |
| Zhijun Meng | 3 | 30 | 6.37 |
| Mingjun Tian | 4 | 1 | 0.34 |