The 13th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications
DOI: 10.1109/pimrc.2002.1046748
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Cited by 19 publications
(19 citation statements)
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“…The goal is finding a suitable input vector x and an estimate f (x) that best approximates the propagation loss. Learning Machines, which are useful tools for solving regression problems, can be efficiently applied for obtaining a reliable prediction of wave propagation [1,6,7,13,20]. In regression, nothing is known about the function we want to represent.…”
Section: Introductionmentioning
confidence: 99%
“…The goal is finding a suitable input vector x and an estimate f (x) that best approximates the propagation loss. Learning Machines, which are useful tools for solving regression problems, can be efficiently applied for obtaining a reliable prediction of wave propagation [1,6,7,13,20]. In regression, nothing is known about the function we want to represent.…”
Section: Introductionmentioning
confidence: 99%
“…Their accuracy is sufficient and of equal value or better than respective ANN methodologies proposed in the litterature. Indicatively, in [20] statistical results for MAE, (4 dB-6 dB) are presented and also in [10], (2.65 dB-6.12 dB), in [12], (3.67 dB-5.04 dB) and in [14], (3.3 dB-5.1 dB) have been obtained.…”
Section: General Conclusionmentioning
confidence: 76%
“…The neural network which is effective for modeling and characterization of complex systems has been developed for many applications [7].…”
Section: Comparison Between Experimental and Simulated Resultsmentioning
confidence: 99%