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A modal parameters identification method based on recurrent neural networks

机译:基于递归神经网络的模态参数识别方法

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This paper presents a recurrent neural network-based approach for modal parameters identification of structure-unknown systems. The proposed approach involves two steps. The first step is to build a recurrent neural network to map the complex nonlinear relation between the excitations and responses of the structure-unknown system by off-line learning. The second step is to propose a procedure to determine the modal parameters of the system from the trained neural networks. The dynamic characteristics of the structure are directly evaluated from the weighting matrices of the trained recurrent neural network. Furthermore, an illustrative example demonstrates the feasibility of using the proposed method to identify modal parameters of structure-unknown systems. The method proposed can be used to research on fault diagnosis of engineer structure.
机译:本文提出了一种基于递归神经网络的结构未知系统模态参数识别方法。拟议的方法涉及两个步骤。第一步是构建一个递归神经网络,以通过离线学习映射未知结构的激励和响应之间的复杂非线性关系。第二步是提出从受过训练的神经网络确定系统模态参数的过程。结构的动态特性直接从训练后的递归神经网络的权重矩阵进行评估。此外,一个示例性例子说明了使用所提出的方法来识别结构未知系统的模态参数的可行性。该方法可用于工程结构故障诊断研究。

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