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Earth-Space Rain Attenuation Model Based on EPNet-Evolved Artificial Neural Network
Hongwei YANG Chen HE Hongwen ZHU Wentao SONG
IEICE TRANSACTIONS on Communications
Publication Date: 2001/09/01
Print ISSN: 0916-8516
Type of Manuscript: Special Section PAPER (Special Issue on Innovation in Antennas and Propagation for Expanding Radio Systems)
rain attenuation, rain attenuation prediction, ANN, artificial neural network, evolutionary algorithm,
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Investigations into the suitability of artificial neural network for the prediction of rain attenuation based on radio, meteorological and geographical data from ITU-R data bank are presented. First successful steps towards a prediction model of rain attenuation for radio communication based on adaptive learning from the measurement are made. Rain attenuation prediction with the model based on artificial neural network shows good conformity with the measurement. Moreover, a new evolutionary system, EPNet is used to evolve the artificial neural network rain attenuation model obtained both in architecture and weight, and an optimal rain attenuation model with simpler architecture and better prediction accuracy based on EPNet-evolved artificial neural network is obtained. Compared with the ITU-R model, the EPNet-evolved artificial neural network model of rain attenuation proposed in this paper improves the accuracy of rain attenuation prediction and creates a novel way to predict rain attenuation.