Mungiole, Michael, authorAzimi-Sadjadi, Mahmood R., authorWichern, Gordon, authorElsevier Ltd., publisher2007-01-032007-01-032007Wichern, Gordon, Mahmood R. Azimi-Sadjadi, and Michael Mungiole, Environmentally Adaptive Acoustic Transmission Loss Prediction in Turbulent and Nonturbulent Atmospheres, Neural Networks 20, no. 4 (May 2007): [484]-497.http://hdl.handle.net/10217/67877An 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.born digitalarticleseng©2007 Elsevier Ltd.Copyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.turbulent scatteringfuzzy-logic fusionparabolic equationatmospheric acousticsText