Optimizing the De-Noise Neural Network Model for GPS Time-Series Monitoring of Structures.
Optimizing the De-Noise Neural Network Model for GPS Time-Series Monitoring of Structures.
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DOI：
10.3390/s150924428
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发表时间：
2015-09-22
期刊：
影响因子：
--
通讯作者：
Hu JW
中科院分区：
文献类型：
--
作者：
Kaloop MR;Hu JW
全球定位系统（GPS）近来在结构和其它应用中被广泛使用。尽管如此，GPS精度仍然受到影响测量的误差的影响，特别是结构部件的短周期位移。以前，多滤波器的方法是用来消除位移误差。本文旨在利用神经网络预测模型的一种新的应用，以改善GPS监测时间序列数据。四个预测模型的学习算法，并与神经网络的解决方案：反向传播，级联前向反向传播，自适应滤波器和扩展卡尔曼滤波器，估计哪个模型可以推荐。通过噪声仿真和桥梁短周期GPS监测位移分量的1Hz采样频率验证了上述四种模型和方法的有效性。结果表明，自适应神经网络滤波器是一种有效的消噪方法，尤其适用于GPS结构位移分量的消噪。同时，该模型对结构的低频响应设计和测试内容也有重要的影响。
The Global Positioning System (GPS) is recently used widely in structures and other applications. Notwithstanding, the GPS accuracy still suffers from the errors afflicting the measurements, particularly the short-period displacement of structural components. Previously, the multi filter method is utilized to remove the displacement errors. This paper aims at using a novel application for the neural network prediction models to improve the GPS monitoring time series data. Four prediction models for the learning algorithms are applied and used with neural network solutions: back-propagation, Cascade-forward back-propagation, adaptive filter and extended Kalman filter, to estimate which model can be recommended. The noise simulation and bridge’s short-period GPS of the monitoring displacement component of one Hz sampling frequency are used to validate the four models and the previous method. The results show that the Adaptive neural networks filter is suggested for de-noising the observations, specifically for the GPS displacement components of structures. Also, this model is expected to have significant influence on the design of structures in the low frequency responses and measurements’ contents.