Name
Abstract | ||
|---|---|---|
An improved support vector machine (SVM) model is proposed to perform online fault detection of the navigation system with hemispherical resonator gyro (HRG). The proposed model is based on sliding window SVM prediction and least square (LS) method, which can satisfy the prediction demand of the HRG output characteristic of nonlinearity, non-determinism and randomness. The proposed model can overcome the explosion of calculation of traditional SVM method, and it also improves the prediction accuracy compared to the GM(1,1) model and BP neural network. Finally, simulations of HRG fault patterns are used to verify the correctness and effectiveness of the online fault detection model. |
Year | Venue | Keywords |
|---|---|---|
2013 | PROCEEDINGS OF 2013 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS (ICMLC), VOLS 1-4 | HRG, SVM, Moving window, Least square method, Prediction model |
DocType | ISSN | Citations |
Conference | 2160-133X | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zi-Yang Qi | 1 | 0 | 1.01 |
| Qing-Hua Li | 2 | 1563 | 88.15 |
| Guo-Xing Yi | 3 | 0 | 0.34 |
| Yang-Guang Xie | 4 | 0 | 0.34 |
| Hong-Tao Dang | 5 | 0 | 0.34 |