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Rapid acoustic transmission loss prediction using an operationally adaptive system

机译:使用运算自适应系统进行快速的声传输损耗预测

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New fusion methods for an operationally adaptive (OA) system for prediction of acoustic transmission loss (TL) in the atmosphere are developed in this paper. The OA system uses expert neural network predictors, each corresponding to a specific range of source elevation. The outputs of the expert predictors are combined using two new nonlinear fusion methods. Using this prediction methodology the computational intractability of traditional acoustic propagation models is eliminated. The proposed fusion methods are tested on a synthetically generated acoustic data set for a wide range of geometric, source, and environmental conditions.
机译:本文开发了一种新的融合方法,用于预测大气中的声传输损耗(TL)的作战自适应(OA)系统。 OA系统使用专家神经网络预测器,每个预测器对应于特定范围的源仰角。专家预测变量的输出使用两种新的非线性融合方法进行组合。使用这种预测方法,可以消除传统声学传播模型的计算难点。在广泛的几何,源和环境条件下,对合成的声学数据集测试了建议的融合方法。

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