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.
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