id: 06097881
dt: j
an: 06097881
au: McCarron, Michael; Azimi-Sadjadi, Mahmood R.; Mungiole, Michael
ti: An operationally adaptive system for rapid acoustic transmission loss
    prediction.
so: Neural Netw. 27, 91-99 (2012).
py: 2012
pu: Elsevier Science (Pergamon), Boston, MA
la: EN
cc: 
ut: acoustic propagation; feedforward neural network; fusion method
ci: 
li: doi:10.1016/j.neunet.2011.11.004
ab: Summary: An operationally adaptive (OA) system for prediction of acoustic
    transmission loss (TL) in the atmosphere is developed in this paper.
    This system uses expert neural network predictors, each corresponding
    to a specific range of source elevation. The outputs of the expert
    predictors are combined using a weighting mechanism and a nonlinear
    fusion system. Using this prediction methodology the computational
    intractability of traditional acoustic propagation models is
    eliminated. The proposed system is tested on a synthetically generated
    acoustic data set for a wide range of geometric, source, environmental,
    and operational conditions. The results show a significant improvement
    in both accuracy and reliability over a benchmark prediction system.
rv: