基于自适应径向基神经网络的类星体光谱自动识别方法

通过对光谱的研究来识别和认证类星体是天文学研究中的重要方法.文章提出了一种对类星体光谱进行自动识别的自适应径向基神经网络(RBFN)方法.该方法包括以下几个步骤:(1)先将训练样本归一化,再利用PCA变换进行降维,获得样本特征向量;(2)设计出K均值聚类算法与梯度下降法相结合的径向基神经网络结构的基本模型,再用SSE(sum of squares error)误差函数进行判断,对RBFN隐含层的神经元进行自动调节,直至满足给定误差阈值;(3)用训练得到的参数对用于测试的样本中的类星体光谱进行识别.该方法不但克服了经典RBFN算法选择隐层神经元数目的困难,而且还提高了对类星体识别的稳定性和正确率.研究结果对于大型光谱巡天所产生的海量数据的自动处理具有重要意义.

Automated Recognition of Quasars Based on Adaptive Radial Basis Function Neural Networks

Recognizing and certifying quasars through the research on spectra is an important method in the field of astronomy.This paper presents a novel adaptive method for the automated recognition of quasars based on the radial basis function neural networks(RBFN).The proposed method is composed of the following three parts:(1) The feature space is reduced by the PCA(the principal component analysis) on the normalized input spectra;(2) An adaptive RBFN is constructed and trained in this reduced space.At first,the K-means clustering is used for the initialization,then based on the sum of squares errors and a gradient descent optimization technique,the number of neurons in the hidden layer is adaptively increased to improve the recognition performance;(3) The quasar spectra recognition is effectively carried out by the above trained RBFN.The author's proposed adaptive RBFN is shown to be able to not only overcome the difficulty of selecting the number of neurons in hidden layer of the traditional RBFN algorithm,but also increase the stability and accuracy of recognition of quasars.Besides,the proposed method is particularly useful for automatic voluminous spectra processing produced from a large-scale sky survey project,such as our LAMOST,due to its efficiency.