An Artificial Neural Network Approach to Automatic Classification of Stellar Spectra

  • Alejandra Rodríguez
  • Carlos Dafonte
  • Bernardino Arcay
  • Minia Manteiga
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2687)


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.


Radial Basis Function Spectral Parameter Input Pattern Spectral Type Radial Basis Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Kurtz, M. J.: Handbook of Astronomy and Astrophysics. 2nd edn. Cambridge University Press, Cambridge (1990)Google Scholar
  2. 2.
    Zombeck, M. V.: The MK Process and Stellar Classification of Stars. Cambridge University Press, Cambridge (1984)Google Scholar
  3. 3.
    Haykin, S.: Neural Networks. A Comprehensive Foundation. Macmillan College Publishing, New York (1994)Google Scholar
  4. 4.
    Rodriguez, A., Arcay, B., Dafonte, C., Suarez, O., Manteiga, M.: Classification of Stellar Spectra using Fuzzy Logic and Neural Networks. Proc. 20th IASTED International Conference, Applied Informatics (AI’02). Series on Applied Informatics. ACTA Press, Calgary (2002) 155Google Scholar
  5. 5.
    Gulati, R.K., Singh, H.P.: Stellar Classification using Principal Component Analysis and Artificial Neural Networks. Monthly Notices Royal Astronomical Society, Vol. 295. Royal Astronomical Society, London (1998) 312Google Scholar
  6. 6.
    Weaver, Wm.B., Torres-Dodgen, A.V.: Neural Network Classification of the Near-Infrared Spectra of A-Type Stars. The Astrophysical Journal, Vol. 446. University of Chicago Press, Chicago (1995) 300Google Scholar
  7. 7.
    Pickles, A.J.: A Stellar Spectral Flux Library. The Astrophysical Journal Suppl., Vol. 81(2). University of Chicago Press, Chicago (1992) 865Google Scholar
  8. 8.
    Silva, D.R., Cornell, M.E.: A New Library of Stellar Optical Spectra. Publications of the Astronomical Society of the Pacific, Vol. 110. University of Chicago Press, Chicago (1998) 863Google Scholar
  9. 9.
    Hilera, J.R., Martnez, V.: Redes Neuronales Artificiales. Fundamentos, modelos y aplicaciones. RA-MA Eds, Madrid (1995)Google Scholar
  10. 10.
    Kohonen, T.: Self-Organization and Associative Memory. Springer-Verlag, New York (1987)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Alejandra Rodríguez
    • 1
  • Carlos Dafonte
    • 1
  • Bernardino Arcay
    • 1
  • Minia Manteiga
    • 2
  1. 1.Dep. of Information Technologies and CommunicationsA Coruña UniversityA CoruñaSpain
  2. 2.Dep. of Navigation and Earth SciencesA Coruña UniversityA CoruñaSpain

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