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An Artificial Neural Network Approach to Automatic Classification of Stellar Spectra

机译:人工神经网络方法自动分类恒星光谱

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This paper presents the design and implementation of several models of artificial neural networks for the automatic classification of low-resolution spectra of stars. In previous works, we have developed knowledge-based systems for the analysis of spectra. We shall now use these analysis methods to extract the most important spectral features, training the proposed neural networks with this numeric characterization. Although there are published works about neural networks applied to the classification problem, our final purpose is the integration of several artificial techniques in a unique hybrid system. In the development of such a system we have combined signal processing techniques, knowledge-based systems, fuzzy logic and artificial neural networks, integrating them by means of a relational database which allow us to structure the collected astronomical data and also contrast the results achieved with the different classification methods.
机译:本文介绍了几种用于低分辨率恒星光谱自动分类的人工神经网络模型的设计和实现。在以前的工作中,我们已经开发了基于知识的光谱分析系统。现在,我们将使用这些分析方法来提取最重要的光谱特征,并使用此数字特征训练拟议的神经网络。尽管已经发表了有关将神经网络应用于分类问题的著作,但我们的最终目的是将几种人工技术集成到一个独特的混合系统中。在此类系统的开发中,我们将信号处理技术,基于知识的系统,模糊逻辑和人工神经网络相结合,并通过关系数据库将其集成在一起,这使我们能够构造收集到的天文数据,并对比得出的结果。不同的分类方法。

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