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Sensors (Basel). 2015 Sep 22;15(9):24428-44. doi: 10.3390/s150924428.

Optimizing the De-Noise Neural Network Model for GPS Time-Series Monitoring of Structures.

Author information

1
Department of Civil and Environmental Engineering, Incheon National University, 12-1 Songdo-dong, Yeonsu-gu, Incheon 406-840, Korea. mosbeh.kaloop@gmail.com.
2
Department of Public Works and Civil Engineering, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt. mosbeh.kaloop@gmail.com.
3
Department of Civil and Environmental Engineering, Incheon National University, 12-1 Songdo-dong, Yeonsu-gu, Incheon 406-840, Korea. jongp24@incheon.ac.kr.
4
Incheon Disaster Prevention Research Center, Incheon National University, 12-1 Songdo-dong, Yeonsu-gu, Incheon 406-840, Korea. jongp24@incheon.ac.kr.

Abstract

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.

KEYWORDS:

GPS; de-noise; monitoring; neural network

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