The Comparison of Spectral Classification Based on DBN, BP Neural Network and SVM
LI Jun-feng1, WANG Yue-le1, HU Sheng2, HE Hui-ling2*
1. College of Computer and Information, China Three Gorges University, Yichang 443002, China 2. College of Science, China Three Gorges University, Yichang 443002, China
Abstract：The stellar classification was an important research field for understanding the formation and evolution of stars and galaxies. With large sky surveys and its massive data, the speed and accuracy of the celestial automatic classification was very important. The depth confidence neural network (DBN), support vector machines (SVM) and BP neural networks used in the star classification were compared in this paper. And the applicability of star classification with these three methods was analyzed. First, K, F stars are classified according to the depth of confidence neural network and BP neural network and support vector machine.Then the K1, K3, K5 sub-type and F2, F5, F9 sub-type were separately identified. Finally, the data which did not belong to the k sub-type were excluded by a secondary classification model based on SVM support vector machine . The results shows that: the depth of belief networks is better for K, F-type star classification, but it is poor for K, F sub-type classification results; The recognition rate of SVM is high for the K, F-type stars and the classification effects of this method is better for K, F-type stars than the corresponding sub-type stars by comparison; The recognition rate of BP neural network is ordinary general for K, F-type stars and their sub-types. The experiment showed that the accuracy of excluding non-k-sub-type data can be up to 100% which indicates that the unknown spectral data can be screened and classified with SVM.
李俊峰1，汪月乐1，胡 升2，何慧灵2* . 基于DBN，SVM和BP神经网络的光谱分类比较 [J]. 光谱学与光谱分析, 2016, 36(10): 3261-3264.
LI Jun-feng1, WANG Yue-le1, HU Sheng2, HE Hui-ling2* . The Comparison of Spectral Classification Based on DBN, BP Neural Network and SVM. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2016, 36(10): 3261-3264.
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