Abstract
This paper describes the basic steps of a novel approach to weather classification by remote and local atmosphere sensing. Atmospheric data on the troposphere are gathered by 10 GHz radio links and a number of meteorological sensors. Classification is performed by artificial neural networks (ANN) and is crucial for further processing, because of the different RF propagation influences under a variety of weather conditions. Reasons for using ANN compared to other means of classification are discussed. Differences in the size and number of hidden layers of back-propagation networks used are discussed. Different learning sets of measured data and their construction are also evaluated.
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Mudroch, M., Pechac, P., Grábner, M., Kvicera, V. (2009). Classification and Prediction of Lower Troposphere Layers Influence on RF Propagation Using Artificial Neural Networks. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5506. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02490-0_109
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DOI: https://doi.org/10.1007/978-3-642-02490-0_109
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02489-4
Online ISBN: 978-3-642-02490-0
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