使用SOFM方法进行恒星光谱自动分类

ＳＯＦＭ是人工神经网络的非监督学习算法，可以将数据组织到一个特征图上，而保存 大多数原始数据空间的拓扑特征．使用这种方法进行恒星光谱自动分类，分类结果与哈佛 序列十分相似．ＳＯＦＭ方法应该是进行大数量恒星光谱样本在线分类的有用方法，它能 够自动执行，因此可用于处理大数量天体光谱．

Automatic Classification of Stellar Spectra Using SOFM Method

In this paper, an automatic classification method of stellar spectra using the Self-Organization Feature Mapping (SOFM) method is given. The SOFM is an unsupervised learning algorithm of Artificial Neural Network (ANN). It allows the data to be organized onto a feature graph while conserving most of the topological features of the original data space. We used this method to classify stellar spectra automatically. The result is very similar to the Harvard sequence with an accuracy comparable to that obtained by human experts. The SOFM should be a useful method for on-line classification of stellar spectrum samples of very large size. The SOFM can be executed automatically, so it can be applied to process large numbers of target spectra.