交叉验证的BP神经网络恒星光谱分类

LAMOST作为国家重大科学工程项目，目前在世界上对光谱的观测、获取率最高，为天文学的研究与发展提供大量的数据和信息资源.根据LAMOST发布的恒星光谱数据文件，从中提取出关于恒星光谱波长的数据信息，对数据进行噪声剔除、数据降维、数据规范化、数据降维处理.利用BP神经网络算法对数据进行分类处理，根据分类结果正确率来判断BP神经网络模型的优劣.但是BP神经网络对测试集数据的测试效果并不代表对其他数据具有同样的测试效果而且易产生过拟合，所以采用交叉验证与BP神经网络相结合的方法，BP神经网络算法可对多组不同的数据进行测试，得到多组测试结果并求得平均值，可得到BP神经网络模型相对稳定的测试结果并降低结果的随机性.

Cross-Validation BP Neural Network Stellar Spectral Classification

As a national major scientific engineering project, LAMOST currently has the highest observation and acquisition rate of the spectrum in the world, and provides a large amount of data and information resources for the research and development of astronomy. According to the stellar spectral data file released by LAMOST, the data about the wavelength of the stellar spectrum is extracted, and the data is subjected to noise culling, data dimensionality reduction, data normalization, and data dimensionality reduction processing. The BP neural network algorithm is used to classify the data, and the pros and cons of the BP neural network model are judged according to the correct rate of the classification results. However, the BP neural network test results of the test set data do not mean that it has the same test effect on other data and is easy to produce over-fitting, so the method of cross-validation combined with BP neural network is adopted. The BP neural network algorithm can test multiple sets of different data, obtain multiple sets of test results and obtain the average value, and obtain the relatively stable test results of the BP neural network model and reduce the randomness of the results.