Earth-space rain attenuation model based on EPNet-evolved artificial neural network

Investigations into the suitability of artificial neural network for the pr ediction of rain attenuation based on radio, meteorological and geographica l data from ITU-R data bank are presented. First successful steps towards a prediction model of rain attenuation for radio communication based on adap tive learning from the measurement are made. Rain attenuation prediction wi th the model based on artificial neural network shows good conformity with the measurement. Moreover, a new evolutionary system, EPNet is used to evol ve the artificial neural network rain attenuation model obtained both in ar chitecture and weight, and an optimal rain attenuation model with simpler a rchitecture and better prediction accuracy based on EPNet-evolved artificia l neural network is obtained. Compared with the ITU-R model, the EPNet-evol ved artificial neural network model of rain attenuation proposed in this pa per improves the accuracy of rain attenuation prediction and creates a nove l way to predict rain attenuation.