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Satellite Fault Diagnosis Using Support Vector Machines Based on a Hybrid Voting Mechanism
Joint Authors
Yin, Hong
Yang, Shuqiang
Zhu, Xiaoqian
Jin, Songchang
Wang, Xiang
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-08-12
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Medicine
Information Technology and Computer Science
Abstract EN
The satellite fault diagnosis has an important role in enhancing the safety, reliability, and availability of the satellite system.
However, the problem of enormous parameters and multiple faults makes a challenge to the satellite fault diagnosis.
The interactions between parameters and misclassifications from multiple faults will increase the false alarm rate and the false negative rate.
On the other hand, for each satellite fault, there is not enough fault data for training.
To most of the classification algorithms, it will degrade the performance of model.
In this paper, we proposed an improving SVM based on a hybrid voting mechanism (HVM-SVM) to deal with the problem of enormous parameters, multiple faults, and small samples.
Many experimental results show that the accuracy of fault diagnosis using HVM-SVM is improved.
American Psychological Association (APA)
Yin, Hong& Yang, Shuqiang& Zhu, Xiaoqian& Jin, Songchang& Wang, Xiang. 2014. Satellite Fault Diagnosis Using Support Vector Machines Based on a Hybrid Voting Mechanism. The Scientific World Journal،Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-1050193
Modern Language Association (MLA)
Yin, Hong…[et al.]. Satellite Fault Diagnosis Using Support Vector Machines Based on a Hybrid Voting Mechanism. The Scientific World Journal No. 2014 (2014), pp.1-11.
https://search.emarefa.net/detail/BIM-1050193
American Medical Association (AMA)
Yin, Hong& Yang, Shuqiang& Zhu, Xiaoqian& Jin, Songchang& Wang, Xiang. Satellite Fault Diagnosis Using Support Vector Machines Based on a Hybrid Voting Mechanism. The Scientific World Journal. 2014. Vol. 2014, no. 2014, pp.1-11.
https://search.emarefa.net/detail/BIM-1050193
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1050193