Characteristic Curve Fitting of Capacitive Rainfall Sensor Based on BP Neural Network
Authors
Long Zhang, Song Ye, Shu-dao Zhou, Feng Liu
Corresponding Author
Long Zhang
Available Online December 2016.
- 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/).
Cite this article
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 -