利用人工神经网络对材料类型和力学性能进行分类

Intan Maisarah Abd Rahim, F. Mat, S. Yaacob, R. Siregar
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引用次数: 2

摘要

本文针对振动技术测试材料力学性能的实验数据进行了研究。通过对材料的振动分析和测试,可以确定结构的固有频率、阻尼比和模态振型。然而,在本研究中,我们只考虑材料的固有频率作为训练所需的输入数据。作为本研究的延伸,用神经网络的各种训练算法对系统进行了测试。将Levenberg-Marquardt反向传播算法应用于人工神经网络系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Classifying material type and mechanical properties using artificial neural network
This paper focused on experimental data and study for the testing of the material mechanical properties using vibration technique. By applying vibration analysis and testing on the material, we could determine the natural frequencies, the damping ratio and mode shapes of the structure. However, in this study, we only considering the natural frequencies of the material as the input data needed for training. As an extension for the study, the system tested with various method of neural network training algorithm. The Levenberg-Marquardt Backpropagation used as the algorithm in an artificial neural network system developed.
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