DocumentCode :
1783058
Title :
Spacecraft electrical characteristics identification study based on offline FCM clustering and online SVM classifier
Author :
Yi Liu ; Ke Li ; Yong Huang ; Jun Wang ; Shimin Song ; Yi Sun
Author_Institution :
Beijing Univ. of Aeronaut. & Astronaut., Beijing, China
fYear :
2014
fDate :
28-29 Sept. 2014
Firstpage :
1
Lastpage :
4
Abstract :
As most electronic system structure is complex and uncertain, this paper presents a new efficiency method for spacecraft electrical characteristics identification. Offline FCM clustering and online SVM classifier is introduced into the registration model. At first step of the algorithm, using FCM clustering method to get an expert training set. By get expert training set for SVM classifier make this method fast and effective which is the foundation of online spacecraft electrical characteristics identification. A series of spacecraft electrical characteristics data experiments prove that the proposed method is more accuracy than the traditional way.
Keywords :
aerospace computing; avionics; pattern classification; pattern clustering; support vector machines; electronic system structure; offline FCM clustering; online SVM classifier; online spacecraft electrical characteristics identification; registration model; Classification algorithms; Clustering algorithms; Electric variables; Feature extraction; Space vehicles; Support vector machines; Training; FCM clustering; SVM classifier; spacecraft electrical characteristics;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Multisensor Fusion and Information Integration for Intelligent Systems (MFI), 2014 International Conference on
Conference_Location :
Beijing
Print_ISBN :
978-1-4799-6731-5
Type :
conf
DOI :
10.1109/MFI.2014.6997666
Filename :
6997666
Link To Document :
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