Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
406659 | Neural Networks | 2012 | 9 Pages |
Abstract
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Michael McCarron, Mahmood R. Azimi-Sadjadi, Michael Mungiole,