摘要
温漂会影响催化甲烷传感器检测精度,为减小这种影响,提高传感器检测精度,本文在不同温度环境下进行甲烷传感器环境影响实验,并利用一种基于主成分分析的BP神经网络温度补偿模型对实验数据进行处理,补偿温漂对检测精度的影响,结果表明:本文提出的模型能提高甲烷传感器的稳定性和准确性,减少温漂的影响。
Temperature drift will influence on the catalytic methane sensor detection accuracy, to reduce the influence of sensor, improve the detection accuracy, methane sensor environmental impact experiments were carried out in different temperature conditions, and using a BP neural network model of temperature compensation based on principal component analysis of the experimental data processing, compensation of temperature drift impact, on detection accuracy the results show that： the model proposed in this paper can improve the methane sensor＇s stability and accuracy, reduce the influence of temperature drift.
出处
《电子设计工程》
2015年第7期15-17,21,共4页
Electronic Design Engineering
基金
中煤科工集团重庆研究院有限公司青年基金项目(2013QNJJ37)
关键词
甲烷传感器
温度补偿
主成分分析
BP神经网络
methane sensor
temperature compensation
principal component analysis
BP neural network
