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