Applications of generalized RBF-NN for path loss prediction

This paper presents the results of the generalized radial basis function neural networks applications for the prediction of propagation path loss in urban and suburban environment. We have studied two types of neural network based models; the first one is used for path loss prediction while the second one is a hybrid prediction model based on error control. The performances of the neural models are compared to the path loss values measured in the city of Kavala and in Oia village on Santorini Island, Greece, based on the absolute mean error, standard deviation and root mean square error between predicted and measured values.