Capacitive rain sensor and fitting methods of its characteristic curve

ZHANG Long; YANG Chang-ye; WANG Xiao-lei; YE Song

doi:10.13873/J.1000-9787(2017)10-0027-04

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Capacitive rain sensor and fitting methods of its characteristic curve

更新时间：2025-09-11

- Capacitive rain sensor and fitting methods of its characteristic curve
- Vol. 36, Issue 10, Pages: 27-30(2017)
- 作者机构：
  
  解放军理工大学气象海洋学院
- 作者简介：
- 基金信息：
- DOI：10.13873/J.1000-9787(2017)10-0027-04
  CLC： P414.95
- Published：2017
- 稿件说明：
移动端阅览
[1]张龙,杨长业,王晓蕾,叶松.电容式降雨传感器及其特性曲线拟合方法[J].传感器与微系统,2017,36(10):27-30.

ZHANG Long, YANG Chang-ye, WANG Xiao-lei, et al. Capacitive rain sensor and fitting methods of its characteristic curve[J]. 2017, 36(10): 27-30.
[1]张龙,杨长业,王晓蕾,叶松.电容式降雨传感器及其特性曲线拟合方法[J].传感器与微系统,2017,36(10):27-30. DOI： 10.13873/J.1000-9787(2017)10-0027-04.

ZHANG Long, YANG Chang-ye, WANG Xiao-lei, et al. Capacitive rain sensor and fitting methods of its characteristic curve[J]. 2017, 36(10): 27-30. DOI： 10.13873/J.1000-9787(2017)10-0027-04.

摘要

针对将电容式传感器应用于降雨测量时

特性曲线的非线性误差较大问题

分别采用最小二乘曲线拟合法和径向基函数（RBF）神经网络对其输出特性曲线进行拟合。结果表明:RBF神经网络模型具有更强的非线性映射能力

其拟合精度明显高于最小二乘多项式模型。

Abstract

Aiming at problem of large nonlinear error of characteristic curve of capacitive sensor applied to measurement of rainfall

least square curve fitting method and radial basis function（ RBF） neural network are used to fit output characteristic curve. The results show that the RBF neural network model has stronger nonlinear mapping ability

and its fitting precision is significantly higher than that of the least square polynomial model.

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references

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