电子测量与仪器学报

姜力,贺晓雷,行鸿彦.改进GA-SVM的湿度传感器温度补偿研究[J].电子测量与仪器学报,2017,31(9):1420-1426

改进GA-SVM的湿度传感器温度补偿研究

Research of temperature compensation for humidity sensor based on improved GA SVM

DOI：10.13382/j.jemi.2017.09.011

英文关键词:humidity sensor temperature compensation genetic algorithm support vector machine GA SVM

基金项目:国家自然科学基金（61671248）、江苏省高校自然科学研究重大项目（15KJA460008）、江苏省“六大人才高峰”计划和江苏省“信息与通信工程”优势学科资助项目

作者	单位
姜力	1.南京信息工程大学气象灾害预报预警与评估协同创新中心南京210044；2.南京信息工程大学江苏省气象探测与信息处理重点实验室南京210044
贺晓雷	中国气象局气象探测中心北京100081
行鸿彦	1.南京信息工程大学气象灾害预报预警与评估协同创新中心南京210044；2.南京信息工程大学江苏省气象探测与信息处理重点实验室南京210044

Author	Institution
Jiang Li	1. Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China;2. Jiangsu Key Laboratory of Meteorological Observation and Information Processing, Nanjing University of Information Science & Technology, Nanjing 210044, China
He Xiaolei	Atmospheric Observation Technology Center, China Meteorological Administration, Beijing 100081, China
Xing Hongyan	1. Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science & Technology, Nanjing 210044, China;2. Jiangsu Key Laboratory of Meteorological Observation and Information Processing, Nanjing University of Information Science & Technology, Nanjing 210044, China

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中文摘要:

针对地面遥测自动气象站采用的HMP 45D湿度传感器测量准确度易受温度影响的问题，通过改进遗传算法(GA)的适应度函数、选择、交叉、变异操作优化支持向量机(SVM)的惩罚函数、径向基核函数、不敏感损失函数，利用不同温湿度条件下的多组实测数据，建立了温度补偿模型，并与传统的SVM回归模型补偿结果对比分析。实验结果表明，利用GA SVM模型进行温度补偿最大误差绝对值为0.1367%，比传统SVM温度补偿模型提高了2.8351%，GA SVM算法克服了传统SVM补偿算法补偿精度低、处理速度慢的问题，具有全局寻优能力强、收敛速度快、补偿精度高的特点，能够有效地对湿度传感器进行温度补偿。

英文摘要:

Aiming at the problem that the measurement accuracy of HMP 45D temperature and humidity sensors used in automatic weather station is susceptible to temperature, the fitness function, selection, crossover, mutation in genetic algorithm (GA) is improved, the improved GA is used to optimize the penalty function, radial basis function and insensitive loss function in support vector machine (SVM). Based on the multiple sets of experimental data under different temperature and humidity, this method is used to establish a model and the results are compared with the traditional SVM regression model for temperature compensation. The experimental results show that the absolute error using the GA SVM model is 0.1367%, reduced by 2.8351% than traditional SVM model. The proposed algorithm overcomes traditional SVM compensation model with low precision, slow process speed and has global optimization, convergence speed, higher compensation accuracy, effectively compensates temperature effect and greatly increases the measurement accuracy.

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