Estimation of absorber performance using reverberation techniques and artificial neural network models

Corey Vyhlidal, V. Rajamani, C. Bunting, Praveen Damacharla, V. Devabhaktuni
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引用次数: 2

Abstract

The Quality factors of an empty and loaded reverberant cavity were measured using time domain techniques. Measurements were performed for a set of frequencies under different loading conditions achieved by varying the material type and material amount. The measured data were used to develop an artificial neural network (ANN) model that predicts the amount of material required for a desired change in Q at a certain frequency for the cavity under consideration. The results show good comparison between the measured and the predicted values, thereby supporting the benefit of the ANN paradigm for studies like this, where experiments tend to be expensive.
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利用混响技术和人工神经网络模型估计吸收器性能
利用时域技术对空腔和加载腔的混响质量因子进行了测量。在不同的加载条件下,通过改变材料类型和材料量来实现一组频率的测量。测量数据用于开发人工神经网络(ANN)模型,该模型可以预测在所考虑的腔中在特定频率下所需的Q变化所需的材料量。结果显示了实测值和预测值之间的良好比较,从而支持了人工神经网络范式在这类实验往往昂贵的研究中的优势。
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