After the scheduled completion of LAMOST project in China at the end of 2004, about 20,000 to 40,000 spectra will be collected at each observation night,such voluminous data urgently demand to explore automatic recognition and parameters measurement methods consequently.This thesis is focused on automated recognition methods of celestial objects via their spectra.Since it is expected that the collected spectra in LAMOST will be of low SNR,and of uncalibrated flux in part of targes, the special attention is also dedicated to deal with such problems in our work.The main points of our work can be summarized as follows: (1)The study on automated recognition between normal objects(NOs)and emission-line objects(ELOs). The celestial objects are classified as normal and emission-line objects depending on whether their spectra are of absorption or emission type.The traditional method for the classification is to estimate the respective number of absorption and emission lines emerged in the spectra,its performance usually degrades rapidly with low SNR data.In this work,a PCA+SVMs method is proposed for the classification.Firstly, the first three principal components in the PCA are used to extract features,then SVM is followed to classify an incoming spectrum as either an NO or an ELO.In order to offset the shortage of spectra under various redshifts,simulated data perturbed by Gaussian noise under different noise level are generated during both the training and verification stages.Experiments show that the proposed method is both efficient and robust. (2)The study o f automated recognition between stars and normal galaxies(NGs) In case the spectra redshifts are unknown,the stars and NGs can usually be distinguished by locating the absorption line on 6563 A.However.when the SNR of spectra is low,the absorption line could be submerged by noise.In this work,an alternative method is proposed,which is based on statistical mixture modeling with RBF neural networks,in short SMM-RBFNN.In this method,a 50-dimensional feature space is created by PCA firstly, then the SMM-RBFNN is used for the final classification. Since SMM-RBFNN is a statistical mixture model of RBFNNS and the parameters of different modules are trained concurrently with an EM-like algorithm,it has some inherent advantages over an individual RBFNN.Experiments show that the proposed method is of high computational efficiency, good accuracy and strong robustness. (3)The study of automated recognition between normal and active galaxies(AGs) A PCA+ODP(Optimal discrimination plane)method is used to separate the NGs and AGs.It is shown that the extracted features by PCA from NGs and AGs can well be clustered separately on the ODP obtained by the Fisher Discrimination Criterion. Besides,this discrimination is undertaken with unknown redshifts. (4)The study of automated recognition of spectral class from stellar spectra Two methods are proposed for stellar re
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