APPLICATIONS OF GENERAL REGRESSION NEURAL NETWORKS FOR PATH LOSS PREDICTION

This paper presents the results of the General Regression Neural Networks applications for the prediction of propagation path loss in a specific urban environment. We have studied two neural network models; the first one is used for path loss prediction while the second one is a prediction model using error control. The performances of the neural models are compared to the path loss values measured in the city of Kavala, Greece, based on the absolute mean error, standard deviation and root mean square error between predicted and measured values.