Atmospheric refractivity evaluation improved using artificial neural networks

This paper describes a new progress in evaluating measured data from a unique, experimental, multiple-receiver terrestrial radio link used for remote sensing. The refractivity index height profile of the lowest troposphere layers is evaluated and the classification process is discussed. We are comparing different sizes and architectures of feed-forward artificial neural networks and their learning parameters.