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首页> 外文会议>IMAC Conference on Structural Dynamics >Localization and Identification of Structural Nonlinearities Using Neural Networks
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Localization and Identification of Structural Nonlinearities Using Neural Networks

机译：使用神经网络的结构非线性的本地化和识别

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In this study, a new approach is proposed for identification of structural nonlinearities by employing neural networks. Linear finite element model of the system and frequency response functions measured at arbitrary locations of the system are used in this approach. Using the finite element model, a training data set is created, which appropriately spans the possible nonlinear configurations space of the system. A classification neural network trained on these data sets then localizes and determines the type of nonlinearity associated with the corresponding degree of freedom in the system. A new training data set spanning the parametric space associated with the determined nonlinearities is created to facilitate parametric identification. Utilizing this data set, a feed forward regression neural network is trained, which parametrically identifies the related nonlinearity. The application of the proposed approach is demonstrated on an example system with nonlinear elements. The proposed approach does not require data collection from the degrees of freedoms related with nonlinear elements, and furthermore, the proposed approach is sufficiently accurate even in the presence of measurement noise.
机译：在本研究中，提出了一种通过采用神经网络来识别结构非线性的新方法。在该方法中使用了在系统任意位置测量的系统和频率响应函数的线性有限元模型。使用有限元模型，创建训练数据集，其适当地跨越了系统的可能的非线性配置空间。在这些数据集上培训的分类神经网络然后定位并确定与系统中相应的自由度相关联的非线性类型。创建了一种新的训练数据集，该培训数据集跨越与所确定的非线性相关联的参数空间以促进参数识别。利用该数据集，培训前馈回归神经网络，参数标识相关的非线性。在具有非线性元素的示例系统上证明了所提出的方法的应用。所提出的方法不需要与非线性元件相关的自由度的数据收集，此外，即使在存在测量噪声的情况下，所提出的方法也足够准确。

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