用神经网络方法预测钢管背散射声压

A. Dariouchy, E. Aassif, G. Maze, R. Latif, D. Decultot, M. Laaboubi
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引用次数: 4

摘要

提出了一种利用人工神经网络(ann)技术预测管内压力背散射的新方法。所研究的管子是由钢构成的。在网络开发过程中,对不同半径比b/a (a:管外半径,b:管内半径)下的几种配置进行了评估。本研究采用了多层感知器(MLP)。选择的最优模型是一个只有一个隐藏层的网络。该模型能够预测压力背散射,平均相对误差(MRE)约为1.6%。仿真结果与实验结果的比较表明,人工神经网络方法适用于该参数的预测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of the Acoustic Pressure Backscattered by a Steel Tube Using Neural Networks Approach
A new approach is used to predict the pressure backscattered by a tube using the artificial neural networks (ANNs) techniques. The studied tube consists of steel. During the development of the network, several configurations are evaluated for various radius ratio b/a (a: outer radius, b: inner radius of tube). The multilayer perceptron (MLP) is used in the current study. The optimal model selected is a network with one hidden layer. This model is able to predict the pressure backscattered with a mean relative error (MRE) of about a 1.6%. The comparison of the obtained and the experimental results indicate that the ANN method is suitable to be used to predict this one.
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