Structural Analysis with Fuzzy Data and Neural Network Based Material Description
Structural Analysis with Fuzzy Data and Neural Network Based Material Description
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DOI：
10.1111/j.1467-8667.2012.00779.x
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发表时间：
2012-10
期刊：
影响因子：
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通讯作者：
W. Graf;S. Freitag;J. Sickert;M. Kaliske
中科院分区：
文献类型：
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作者：
W. Graf;S. Freitag;J. Sickert;M. Kaliske
翻译后摘要：在这篇文章中，提出了一种新的方法，利用人工神经网络的不确定的时间依赖性的结构行为。模糊数据的递归神经网络（RNN）可以通过不确定的实验数据来训练，以描述任意的应力-应变-时间依赖关系。其优点是一个通用的公式，可以应用于描述几种材料的行为，而无需定义特定的材料模型。无模型材料描述可用作有限元方法中的数值有效材料公式。为了进行模糊或模糊随机有限元分析，介绍了一种新的方法。一级优化用于模糊数据的信号计算和RNN的训练。通过实例证明了该方法的适用性。
Abstract: In the article, a new approach is presented utilizing artificial neural networks for uncertain time‐dependent structural behavior. Recurrent neural networks (RNNs) for fuzzy data can be trained by uncertain experimental data to describe arbitrary stress–strain–time dependencies. The benefit is a generalized formulation, which can be applied to describe the behavior of several materials without definition of a specific material model. Model‐free material descriptions can be used as numerical efficient material formulations within the finite element method. To perform fuzzy or fuzzy stochastic finite element analyses, a new approach is introduced. An ‐level optimization is utilized for signal computation and training of RNNs for fuzzy data. The applicability is demonstrated by means of examples.