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STRUCTURAL DYNAMIC MODEL IDENTIFICATION THROUGH NEURAL NETWORK

机译:神经网络结构动态模型识别

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Due to its attributes, such as parallelism, adaptability, robustness and inherent ability to handle non-linearality, the artificial neural network has shown great promise in the function mapping, pattern recognition, imagine processing, etc. The dynamic function mapping, dynamic pattern recognition and dynamic imagine processing are still challenging topics in neural network applications. A neural network approach for dynamic function mapping is presented in this paper. The network is trained, tested and verified by using the responses recorded in a real apartment building during earthquakes. The results show that the dynamic behaviors of the building can be very well modeled by the trained neural network. The results also explore the great potential of using neural network in dynamic function mapping/structural dynamic model identification.
机译:由于其属性,例如并行性,适应性,鲁棒性和固有的非线性正性能的能力,人工神经网络在函数映射,模式识别,想象的处理等中表现出了很大的希望。动态函数映射,动态模式识别动态想象的处理仍处于神经网络应用中的主题仍然具有挑战性。本文提出了一种动态函数映射的神经网络方法。通过在地震期间使用在真正的公寓大楼中记录的响应进行培训,测试和验证网络。结果表明,建筑物的动态行为由训练有素的神经网络建模非常良好。结果还探讨了使用神经网络在动态函数映射/结构动态模型识别中的巨大潜力。

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