Fast neural networks learning techniques for signal compression

In this paper the comparative study of two learning techniques of fast artificial neural networks with application to lossy signal compression is presented. The experimental results prove faster convergence and better final results for energy maximization technique. Moreover the artificial neural network approach gives significantly smaller errors of signal reconstruction than transforms with fixed basis functions.