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Feasibility of neural networks in modelling radio propagation for field strength prediction
Author(s) -
Leros A. P.,
Alexandridis A. A.,
Dangakis K.,
Kostarakis P.
Publication year - 1998
Publication title -
international journal of communication systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.344
H-Index - 49
eISSN - 1099-1131
pISSN - 1074-5351
DOI - 10.1002/(sici)1099-1131(199811/12)11:6<359::aid-dac377>3.0.co;2-9
Subject(s) - computer science , mean squared error , artificial neural network , standard deviation , terrain , backpropagation , field strength , radio propagation , approximation error , field (mathematics) , radio propagation model , predictive modelling , decibel , mean absolute error , statistics , data mining , machine learning , algorithm , telecommunications , mathematics , physics , quantum mechanics , magnetic field , pure mathematics , ecology , biology
A typical back‐propagation neural network (BPN) model is developed for modelling radio propagation for field strength prediction based on data measurements of propagation loss (in decibels) with terrain information taken in an urban area (Athens region) in the 900 MHz band. The feasibility of the BPN model is checked against the performance of a conventional semiempirical reference model. The performance of both models is quantified by statistical methods. The evaluation is done by comparing their prediction error statistics of average absolute, standard deviation and root mean square and by comparing their percentage accuracy and correlation of predicted values relative to true data measurements. © 1998 John Wiley & Sons, Ltd.