@inproceedings{f4ed93bcee5a4fdaaa3b78b239b6b034,
title = "Spacecraft electrical characteristics identification study based on offline FCM clustering and online SVM classifier",
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 = "FCM clustering, SVM classifier, spacecraft electrical characteristics",
author = "Yi Liu and Ke Li and Yong Huang and Jun Wang and Shimin Song and Yi Sun",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.; 2014 International Conference on Multisensor Fusion and Information Integration for Intelligent Systems, MFI 2014 ; Conference date: 28-09-2014 Through 30-09-2014",
year = "2014",
month = dec,
day = "23",
doi = "10.1109/MFI.2014.6997666",
language = "英语",
series = "Proceedings of 2014 International Conference on Multisensor Fusion and Information Integration for Intelligent Systems, MFI 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "Proceedings of 2014 International Conference on Multisensor Fusion and Information Integration for Intelligent Systems, MFI 2014",
address = "美国",
}