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Neural Networks 20 (2007) 484497 www.elsevier.com/locate/neunet

Summary: Neural Networks 20 (2007) 484497
2007 Special Issue
Environmentally adaptive acoustic transmission loss prediction in turbulent
and nonturbulent atmospheres
Gordon Wicherna, Mahmood R. Azimi-Sadjadia,, Michael Mungioleb
a Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523-1373, USA
b US Army Research Laboratory, Attn: AMSRD-ARL-CI-ES, 2800 Powder Mill Road, Adelphi, MD 20783-1197, USA
An environmentally adaptive system for prediction of acoustic transmission loss (TL) in the atmosphere is developed in this paper. This system
uses several back propagation neural network predictors, each corresponding to a specific environmental condition. The outputs of the expert
predictors are combined using a fuzzy confidence measure and a nonlinear fusion system. Using this prediction methodology the computational
intractability of traditional acoustic model-based approaches is eliminated. The proposed TL prediction system is tested on two synthetic acoustic
data sets for a wide range of geometrical, source and environmental conditions including both nonturbulent and turbulent atmospheres. Test results
of the system showed root mean square (RMS) errors of 1.84 dB for the nonturbulent and 1.36 dB for the turbulent conditions, respectively, which
are acceptable levels for near real-time performance. Additionally, the environmentally adaptive system demonstrated improved TL prediction
accuracy at high frequencies and large values of horizontal separation between source and receiver.
c 2007 Elsevier Ltd. All rights reserved.
Keywords: Atmospheric acoustics; Turbulent scattering; Parabolic equation; Fuzzy-logic fusion
1. Introduction


Source: Azimi-Sadjadi, Mahmood R. - Department of Electrical and Computer Engineering, Colorado State University


Collections: Computer Technologies and Information Sciences