Automated stellar classification for large surveys with EKF and RBF neural networks

CASIA OpenIR > 09年以前成果

	Automated stellar classification for large surveys with EKF and RBF neural networks
	Bai, L ; Guo, P ; Hu, ZY
发表期刊	CHINESE JOURNAL OF ASTRONOMY AND ASTROPHYSICS
	2005-04-01
卷号	5 期号:2 页码:203-210
文章类型	Article
摘要	An automated classification technique for large size stellar surveys is proposed. It uses the extended Kalman filter as a feature selector and pre-classifier of the data, and the radial basis function neural networks for the classification. Experiments with real data have shown that the correct classification rate can reach as high as 93%, which is quite satisfactory. When different system models are selected for the extended Kalman filter, the classification results are relatively stable. It is shown that for this particular case the result using extended Kalman filter is better than using principal component analysis.
关键词	Methods : Data Analysis Techniques : Spectroscopic Stars : General Galaxies : Stellar Content
WOS标题词	Science & Technology ; Physical Sciences
关键词[WOS]	EXTENDED KALMAN FILTER ; SPECTRA ; GALAXIES
收录类别	SCI
语种	英语
WOS研究方向	Astronomy & Astrophysics
WOS类目	Astronomy & Astrophysics
WOS记录号	WOS:000228645800012
引用统计	被引频次：11[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/9082
专题	09年以前成果
作者单位	1.Beijing Normal Univ, Dept Comp Sci, Beijing 100875, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100080, Peoples R China
推荐引用方式 GB/T 7714	Bai, L,Guo, P,Hu, ZY. Automated stellar classification for large surveys with EKF and RBF neural networks[J]. CHINESE JOURNAL OF ASTRONOMY AND ASTROPHYSICS,2005,5(2):203-210.
APA	Bai, L,Guo, P,&Hu, ZY.(2005).Automated stellar classification for large surveys with EKF and RBF neural networks.CHINESE JOURNAL OF ASTRONOMY AND ASTROPHYSICS,5(2),203-210.
MLA	Bai, L,et al."Automated stellar classification for large surveys with EKF and RBF neural networks".CHINESE JOURNAL OF ASTRONOMY AND ASTROPHYSICS 5.2(2005):203-210.