河北工业科技2018，Vol.35Issue(4)：273-277,5.DOI:10.7535/hbgykj.2018yx04008
L-M贝叶斯正则化BP神经网络在红外CO2传感器的应用
Application of BP neural network with L-M Bayesian regularization in infrared CO2 sensor
摘要
Abstract
Aiming at the influence of temperature on the output voltage of infrared CO2 sensor and the detection error of CO2 concentration,a temperature compensation method based on L-M Bayes regularization BP neural network is proposed.The out-put voltage ratio of the infrared CO2 sensor and temperature are taken as input of neural network,CO2 concentration is used as output of neural network,and neural network is optimized by L-M algorithm.The experimental simulation shows that the maximum relative error of the measured output is 4.5578％ after temperature compensation,which has high accuracy.There-fore,the L-M Bayesian regularization BP neural network can effectively compensate the temperature of the infrared CO2 sen-sor,which provides a reference for the improvement of related infrared sensor instruments.关键词
计算机神经网络/红外CO2传感器/BP神经网络/L-M算法/贝叶斯正则化/温度补偿Key words
computer neural network/infrared CO2 sensor/BP neural network/L-M algorithm/Bayesian regularization/tem-perature compensation分类
信息技术与安全科学引用本文复制引用
赵久强,王震洲..L-M贝叶斯正则化BP神经网络在红外CO2传感器的应用[J].河北工业科技,2018,35(4):273-277,5.基金项目
河北省科技支撑计划项目(16273705D) （16273705D）
