Neural Networks for Spectral Analysis of Unevenly Sampled Data

  • Roberto Tagliaferri
  • Angelo Ciaramella
  • Leopoldo Milano
  • Fabrizio Barone
Conference paper
Part of the Perspectives in Neural Computing book series (PERSPECT.NEURAL)


In this paper we present a neural network based estimator system which performs well the frequency extraction from unevenly sampled signals. It uses an unsupervised Hebbian nonlinear neural algorithm to extract the principal components which, in turn, are used by the MUSIC frequency estimator algorithm to extract the frequencies.

We generalize this method to avoid an interpolation preprocessing step and to improve the performance by using a new stop criterion to avoid overfrtting.

The experimental results are obtained comparing our methodology with the others known in literature.


Light Curve Stop Criterion Spectral Estimator Overfitting Problem Neural Network Weight 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    T. J. Deeming, Fourier analysis with unequally-spaced data, Astrophysics and Space Science, vol.36, pag.137–158, 1975.MathSciNetMATHCrossRefGoogle Scholar
  2. [2]
    J. D. Fernie, Photometry of the Classical Cepheid SU Cygni. Astronomical Society of the Pacific, vol.91, pag.67–70, 1979.CrossRefGoogle Scholar
  3. [3]
    S. Ferraz-Mello, Estimation of periods from unequally spaced obsevations. The Astronomical Journal, vol.86, pag.619, 1981CrossRefGoogle Scholar
  4. [4]
    J. H. Horne, S. L. Balius, A prespriction for period analysis of unevenly sampled time series, The Astrophisical Journal, vol.302, pag.757–763, 1986.CrossRefGoogle Scholar
  5. [5]
    J. Kharhunen, J. Joutsensalo, Representation and separation of signals using nonlinear PCA type learning. Neural Networks, vol.7, pag.113–127, 1994.CrossRefGoogle Scholar
  6. [6]
    S. M. Kay, Modern spectral estimation: Theory and application. Englewood Cliffs: Prentice-Hall, 1988.MATHGoogle Scholar
  7. [7]
    N. R. Lomb, Least-squares frequency analysis of unequally spaced data. Astrophysics and Space Science, vol.39, pag.447–462, 1976.CrossRefGoogle Scholar
  8. [8]
    S. L. Marple, Digital spectral analysis with applications, Prentice-Hall, Englewood Cliffs, N.J., 1987.Google Scholar
  9. [9]
    E. Oja, A simplified neuron model as principal component analyzer, Journal of Mathematical Biology, vol.15, pag.267–275, 1982.MathSciNetMATHCrossRefGoogle Scholar
  10. [10]
    A. V. Oppenhaim, R. W. Schafer, Digital Signal Processing, Prentince-Hall, 1965.Google Scholar
  11. [11]
    M. Rasile, L. Milano, R. Tagliaferri, G. Longo, Periodicity Analysis of Unvenly Spaced Data by Means of Neural Networks, in Neural Nets WIRN Vietri 97, M. Marinaro and R. Tagliaferri Editors, Springer Verlag, pag.201–212, 1997.Google Scholar
  12. [12]
    R. Roy, T. Kailath, ESPRIT-Estimation of Signal Parameters via Rotational Invariance Tecniques, in F.A. Grünbaum, J. W. Helton and P. Khargonear Editors, Signal Processing Part II: Control Theory and Applications, Springer Verlag, New York, pag.369–411, 1990.Google Scholar
  13. [13]
    T. D. Sanger, Optimal unsupervised learning in a single-layer linear feedforward network, Neural Networks, vol.2, pag.459–473, 1989.CrossRefGoogle Scholar
  14. [14]
    J. D. Scargle, Studies in astronomical time series analysis; statistical aspects of spectral analysis of unevenly spaced data, The Astrophisical Journal vol.263, pag.835–853, 1982.CrossRefGoogle Scholar
  15. [15]
    R. Tagliaferri, A. Ciaramella, L. Milano, F. Barone, G. Longo, Spectral Analysis of Stellar Light Curves by Means of Neural Networks, accepted for publication, Astronomy & Astrophysics Supplement Series, vol.137, June, 1999.Google Scholar

Copyright information

© Springer-Verlag London Limited 1999

Authors and Affiliations

  • Roberto Tagliaferri
    • 1
    • 2
  • Angelo Ciaramella
    • 1
    • 2
  • Leopoldo Milano
    • 3
  • Fabrizio Barone
    • 3
  1. 1.Dipartimento di Matematica ed InformaticaUniversità di Salerno, and INFM unità di SalernoBaronissi (SA)Italia
  2. 2.IIASS ”E. R. Caianiello”Vietri s/mItalia
  3. 3.Dipartimento di Scienze Fisiche, Istituto Nazionale di Fisica Nucleare, sez. NapoliUniversità di Napoli ”Federico II”, Complesso Universitario di Monte Sant’AngeloNapoliItalia

Personalised recommendations