Hybrid neural networks for frequency estimation of unevenly sampled data

We present a hybrid system composed of a neural network based estimator system and genetic algorithms. 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 over fitting. Furthermore, genetic algorithms are used to optimize the neural net weight initialization