An operationally adaptive system for rapid acoustic transmission loss prediction

Authors:
Michael McCarron;Mahmood R. Azimi-Sadjadi;Michael Mungiole
Affiliations:
Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523-1373, USA;Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523-1373, USA;US Army Research Laboratory, Attn: AMSRD-ARL-CI-ES, 2800 Powder Mill Road, Adelphi, MD 20783-1197, USA
Venue:
Neural Networks
Year:
2012

Citing 2
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2007 Special Issue: Environmentally adaptive acoustic transmission loss prediction in turbulent and nonturbulent atmospheres

Neural Networks
Training feedforward networks with the Marquardt algorithm

IEEE Transactions on Neural Networks

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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.