基于概率神经网络的ELODIE恒星光谱库自动分类
Automated Classification of ELODIE Stellar Spectral Library Using Probabilistic Artificial Neural Networks
摘要：
用概率神经网络模型对包含约2000个光谱的ESDIE恒星光谱库进行了自动分类，将其分为158个已知的分光光度类。具有561个通量箱和57、26和16个分量的PCA缩减集的全光谱已用于训练和测试会话。结果表明，光谱类型分类精度为3.2分光谱类型和光度类的精度为2.7的全光谱和精度分别为3.1和2.6的PCA集。这种技术将有助于未来即将到来的大型数据库及其快速分类。
英文摘要：
A Probabilistic Neural Network model has been used for automated classification of ELODIE stellar spectral library consisting of about 2000 spectra into 158 known spectro-luminosity classes. The full spectra with 561 flux bins and a PCA reduced set of 57, 26 and 16 components have been used for the training and test sessions. The results shows a spectral type classification accuracy of 3.2 sub-spectral type and luminosity class accuracy of 2.7 for the full spectra and an accuracy of 3.1 and 2.6 respectively with the PCA set. This technique will be useful for future upcoming large data bases and their rapid classification.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
