A Neural Network and Post-processing for Estimating the Values of Error Data

Jihoon Lee, Yousok Kim, Se-Woon Choi, Hyo-Seon Park

Abstract

A sensor network is a key factor for successful structural health monitoring (SHM). Although stable sensor network system is deployed in the structure for measurement, it is often inevitable to face measurement faults. In order to secure the continuous evaluation of targeted structure in cases where the measurement faults occur, appropriate techniques to estimate omitted or error data are necessary. In this research, back-propagation neural network is adopted as a basic estimation method. Then, a concept of post-processing is proposed to improve an accuracy of estimation obtained from the neural network. The results of simulation to verify performance of estimation are also shown.

References

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  4. 50 100 150 200 250 300 Estimation number 350 400 450 500 Figure 7: Accuracies of estimated displacements through BPNN trained by 200 typical sets.
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Paper Citation


in Harvard Style

Lee J., Kim Y., Choi S. and Park H. (2013). A Neural Network and Post-processing for Estimating the Values of Error Data . In Proceedings of the 2nd International Conference on Sensor Networks - Volume 1: SENSORNETS, ISBN 978-989-8565-45-7, pages 205-208. DOI: 10.5220/0004207202050208


in Bibtex Style

@conference{sensornets13,
author={Jihoon Lee and Yousok Kim and Se-Woon Choi and Hyo-Seon Park},
title={A Neural Network and Post-processing for Estimating the Values of Error Data},
booktitle={Proceedings of the 2nd International Conference on Sensor Networks - Volume 1: SENSORNETS,},
year={2013},
pages={205-208},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004207202050208},
isbn={978-989-8565-45-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Sensor Networks - Volume 1: SENSORNETS,
TI - A Neural Network and Post-processing for Estimating the Values of Error Data
SN - 978-989-8565-45-7
AU - Lee J.
AU - Kim Y.
AU - Choi S.
AU - Park H.
PY - 2013
SP - 205
EP - 208
DO - 10.5220/0004207202050208