Characteristic Curve Fitting of Capacitive Rainfall Sensor Based on BP Neural Network

DOI

10.2991/iceeecs-16.2016.146How to use a DOI?

Keywords

Capacitive Rainfall Sensor, Curve Fitting, Least Square Method, BP Neural Network, Mean Square Error

Abstract

Rainfall is a kind of common weather phenomenon. Accurate measurement of rainfall is of great significance for weather and natural disasters forecasting. In this paper, the capacitive sensor was applied to the measurement of rainfall and the working principle was introduced. Furthermore, the output characteristic curve of the sensor was fitted by BP neural network. The sensor's calibration data were taken as training samples and BP network model was established. The results showed that the fitting algorithm based on BP neural network had faster convergence speed and higher accuracy, and its fitting error was much smaller than that of the least square method.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Long Zhang
AU  - Song Ye
AU  - Shu-dao Zhou
AU  - Feng Liu
PY  - 2016/12
DA  - 2016/12
TI  - Characteristic Curve Fitting of Capacitive Rainfall Sensor Based on BP Neural Network
BT  - Proceedings of the 2016 4th International Conference on Electrical & Electronics Engineering and Computer Science (ICEEECS 2016)
PB  - Atlantis Press
SP  - 737
EP  - 742
SN  - 2352-538X
UR  - https://doi.org/10.2991/iceeecs-16.2016.146
DO  - 10.2991/iceeecs-16.2016.146
ID  - Zhang2016/12
ER  -