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)

Abstract

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.

Keywords

Covariance Peri 

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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

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