ONLINE FAULT DETECTION OF HRG BASED ON AN IMPROVED SUPPORT VECTOR MACHINE

被引：0

作者：

Qi, Zi-Yang ^{[1
]}

Li, Qing-Hua ^{[1
]}

Yi, Guo-Xing ^{[1
]}

Xie, Yang-Guang ^{[1
]}

Dang, Hong-Tao ^{[1
]}

机构：

[1] Harbin Inst Technol, Space Control & Inertial Technol Res Ctr, Harbin 150080, Peoples R China

来源：

PROCEEDINGS OF 2013 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS (ICMLC), VOLS 1-4 | 2013年

关键词：

HRG; SVM; Moving window; Least square method; Prediction model;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：316 / 319

页数：4

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