@inproceedings{43915f4e09b542acb1d5b8e1f8bde911,
title = "Online fault detection of HRG based on an improved support vector machine",
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.",
keywords = "HRG, Least square method, Moving window, Prediction model, SVM",
author = "Qi, \{Zi Yang\} and Li, \{Qing Hua\} and Yi, \{Guo Xing\} and Xie, \{Yang Guang\} and Dang, \{Hong Tao\}",
note = "Publisher Copyright: {\textcopyright} 2013 IEEE.; 12th International Conference on Machine Learning and Cybernetics, ICMLC 2013 ; Conference date: 14-07-2013 Through 17-07-2013",
year = "2013",
doi = "10.1109/ICMLC.2013.6890487",
language = "英语",
series = "Proceedings - International Conference on Machine Learning and Cybernetics",
publisher = "IEEE Computer Society",
pages = "316--319",
booktitle = "Proceedings - International Conference on Machine Learning and Cybernetics",
address = "美国",
}