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