Study on temperature compensation approaches of humidity sensitive capacitor

YE Xiao-ling; LIAO Jun-ling; SUN Ning

doi:10.13873/j.1000-97872013.06.017

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Study on temperature compensation approaches of humidity sensitive capacitor

更新时间：2025-09-11

- Study on temperature compensation approaches of humidity sensitive capacitor
- Vol. 32, Issue 6, Pages: 70-72+76(2013)
- 作者机构：
  
  南京信息工程大学信息与控制学院
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-97872013.06.017
  CLC： TM53
- Published：2013
- 稿件说明：
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[1]叶小岭,廖俊玲,孙宁.湿敏电容器的温度补偿方法研究[J].传感器与微系统,2013,32(06):70-72+76.

YE Xiao-ling, LIAO Jun-ling, SUN Ning. Study on temperature compensation approaches of humidity sensitive capacitor[J]. 2013, 32(6): 70-72+76.
[1]叶小岭,廖俊玲,孙宁.湿敏电容器的温度补偿方法研究[J].传感器与微系统,2013,32(06):70-72+76. DOI： 10.13873/j.1000-97872013.06.017.

YE Xiao-ling, LIAO Jun-ling, SUN Ning. Study on temperature compensation approaches of humidity sensitive capacitor[J]. 2013, 32(6): 70-72+76. DOI： 10.13873/j.1000-97872013.06.017.

摘要

为了精确辨识湿敏电容器的温度补偿模型

减小系统测量误差

分析了湿敏电容器的温度补偿原理

提出了一种基于改进的遗传算法（GA）和支持向量机（SVM）相结合的补偿算法。基于湿敏电容器

建立了GA-SVM补偿模型

并与BP神经网络方法进行了比较。共144个样本点

经过补偿后GA-SVM和BP模型的相对误差绝对值小于3%的个数分别为143

110;最大分别为0.34%

19.68%。结果表明:改进的GA-SVM算法有效地补偿了温度影响

提高了湿敏电容器的测量精度

同时该算法的逼近能力和泛化能力均要优于BP神经网络。因此

该方法用于湿度传感器温度补偿是有效可行的。

Abstract

To accurately identify the temperature compensation model of humidity sensitive capacitor

and reduce error of system measurement

the principle of temperature compensation of humidity sensitive capacitor is analyzed.A kind of improved compensation algorithm combined genetic algorithm（GA） with support vector machine（SVM） is proposed.The GA-SVM compensation model is established based on the humidity sensitive capacitor

and compared with BP neural network method.A total of 144 sample point

after compensation

the number relative error absolute value of GA-SVM and BP model less than 3 % is 143

110.The maximum value is 0.34 %

19.68 %.The results show that the improved method can compensate temperature influence effectively

the measurement precision of humidity sensitive capacitor is improved.At the same time

the approximation ability and generalization ability of this algorithm is superior to BP neural network.Therefore

using this method for temperature compensation of humidity sensitive capacitor sensor is effective and feasible.

关键词

Keywords

references

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