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Damage identification of multi-story steel frames using neural networks

机译:使用神经网络损坏多层钢帧的损伤识别

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In this study the basic operations and training method of neural network are introduced and the learning capability of network is explained from the mathematical point of view. The "knowledge" or "memory" can be stored in the parameters of Taylor can be regarded as a general type of NARMA model which is a suitable representation of nonlinear discrete time system. Application of the method to the identification of building seismic response data was performed. The results show that the input-output mappling model from acceleration response is possible for emulating the mdof building structure.
机译:在本研究中,引入了神经网络的基本操作和培训方法,并从数学的角度解释了网络的学习能力。 “知识”或“记忆”可以存储在泰勒的参数中,可以被视为作为非线性离散时间系统的合适表示的一般类型的线条模型。该方法在识别建筑地震响应数据的应用中的应用。结果表明,用于仿真MDOF构建结构的加速响应的输入输出映射模型。

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