In this study, an output-based neuro controller was built based on the idea of the adaptive neuro-fuzzy inference system (ANFIS) and its capabilities in response simulation, data cleansing and restoration capability were verified using measurement data from actual structural testing. The ANFIS is a family of the deep learning algorithm, which incorporates the benefits of adaptive control technique, artificial neural network, and the fuzzy inference system. Thus, it is expected to produce very accurate predictions even for the highly nonlinear system. Forced vibration responses of a five-story steel building were simulated by ANFIS and its accuracy was compared with the results of Recurrent Neural Network (RNN), which is a type of traditional artificial neural networks. Simulations by ANFIS were very accurate with a much lower root means square error (RMSE) than RNN. Simulated data by ANFIS showed an almost perfect match with the original. Even the small ripples in the power spectrum plot outside the dominant frequency were successfully reproduced. In addition, the ANFIS was used to increase the sampling rate of dynamic data. It was shown that missing high-frequency contents could be successfully reproduced when the ANFIS was properly trained. Lastly, The ANFIS was applied to remove the noise in the measured data from RC column cyclic load tests. The outliers were corrected effectively, but the tendency of flattening the peak values was observed.

中文翻译：

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基于深度学习算法的动态和静态测量的响应模拟、数据清洗和恢复

在本研究中，基于自适应神经模糊推理系统（ANFIS）的思想构建了基于输出的神经控制器，并使用来自实际结构测试的测量数据验证了其在响应模拟、数据清理和恢复能力方面的能力。ANFIS 是一个深度学习算法家族，它结合了自适应控制技术、人工神经网络和模糊推理系统的优点。因此，即使对于高度非线性的系统，它也有望产生非常准确的预测。用 ANFIS 模拟了五层钢结构建筑的受迫振动响应，并将其精度与循环神经网络 (RNN) 的结果进行了比较，这是一种传统的人工神经网络。ANFIS 的模拟非常准确，均方根误差 (RMSE) 比 RNN 低得多。ANFIS 的模拟数据显示与原始数据几乎完美匹配。即使是主频率之外的功率谱图中的小波纹也能成功再现。此外，ANFIS 用于提高动态数据的采样率。结果表明，当 ANFIS 得到适当训练时，可以成功再现丢失的高频内容。最后，应用 ANFIS 去除来自 RC 柱循环负载测试的测量数据中的噪声。异常值得到了有效校正，但观察到峰值趋于平缓的趋势。即使是主频率之外的功率谱图中的小波纹也能成功再现。此外，ANFIS 用于提高动态数据的采样率。结果表明，当 ANFIS 得到适当训练时，可以成功再现丢失的高频内容。最后，应用 ANFIS 去除来自 RC 柱循环负载测试的测量数据中的噪声。异常值得到了有效校正，但观察到峰值趋于平缓的趋势。即使是主频率之外的功率谱图中的小波纹也能成功再现。此外，ANFIS 用于提高动态数据的采样率。结果表明，当 ANFIS 得到适当训练时，可以成功再现丢失的高频内容。最后，应用 ANFIS 去除来自 RC 柱循环负载测试的测量数据中的噪声。异常值得到了有效校正，但观察到峰值趋于平缓的趋势。