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基于神经网络校正算法的酒精非接触测量方法

赵雷红 潘冬宁 李英杰 宋源清 王蕾 杜丽华

赵雷红, 潘冬宁, 李英杰, 宋源清, 王蕾, 杜丽华. 基于神经网络校正算法的酒精非接触测量方法[J]. 红外技术, 2021, 43(2): 192-197.
引用本文: 赵雷红, 潘冬宁, 李英杰, 宋源清, 王蕾, 杜丽华. 基于神经网络校正算法的酒精非接触测量方法[J]. 红外技术, 2021, 43(2): 192-197.
ZHAO Leihong, PAN Dongning, LI Yingjie, SONG Yuanqing, WANG Lei, DU Lihua. A Non-contact Alcohol Measurement Method Based on Neural Network Correction Algorithm[J]. Infrared Technology , 2021, 43(2): 192-197.
Citation: ZHAO Leihong, PAN Dongning, LI Yingjie, SONG Yuanqing, WANG Lei, DU Lihua. A Non-contact Alcohol Measurement Method Based on Neural Network Correction Algorithm[J]. Infrared Technology , 2021, 43(2): 192-197.

基于神经网络校正算法的酒精非接触测量方法

详细信息
    作者简介:

    赵雷红(1992-),女,硕士,主要从事光谱分析、光谱成像等方面的研究。E-mail:zhaolh@qdaoe.cn

  • 中图分类号: TN911

A Non-contact Alcohol Measurement Method Based on Neural Network Correction Algorithm

  • 摘要: 为了解决酒精气体测量过程中其他外界因素对测量浓度影响的问题,本文结合酒精气体在红外谱段吸收的特性以及BP神经网络算法的非线性处理方法提出了一种基于神经网络校正算法的酒精气体非接触测量方法。该算法考虑气体吸收过程中温度、湿度对光强的影响,把其作为神经网络的输入和测量参数一起进行训练,同时与常规的数据拟合模型算法进行对比实验,实验证明该算法取得了较好的效果。
  • 图  1  红外气体检测模块

    Figure  1.  Infrared gas detection module

    图  2  BP神经网络的计算流程简图

    Figure  2.  Schematic diagram of calculation flow of BP neural network

    图  3  酒精标气与电压关系模型

    Figure  3.  Relationship model between alcohol standard gas and voltage

    图  4  误差随训练次数变化

    Figure  4.  Error changes with training times

    图  5  三层神经网络

    Figure  5.  Three-layer neural network

    图  6  两种算法输出结果比较图

    Figure  6.  Output results of the two algorithms

    表  1  酒精浓度-电压表

    Table  1.   Table of alcohol concentration and voltmeter

    Alcohol concentration/ppm Signal voltage/ Reference voltage
    60 1.025
    80 1.0245
    100 1.023
    120 1.0212
    140 1.020
    160 1.018
    180 1.0175
    200 1.0165
    下载: 导出CSV

    表  2  模型评价参数表

    Table  2.   Table of model evaluation parameter

    Evaluation parameters Sum of squares due to error R-square Adjusted R-square Root mean square error
    Value 9.169×10-7 0.9876 0.9784 0.0004788
    下载: 导出CSV

    表  3  部分预测误差表

    Table  3.   Table of partial prediction error

    Standard gas concentration Prediction concentration of exponential fitting method Prediction concentration of BP neural network method Prediction error of exponential fitting method Prediction error of BP neural network method
    100 91.7 96.1 8.266669052 3.920268702
    100 105.4 91.6 -5.438678342 8.427306293
    100 94.1 91.7 5.886509571 8.315740568
    100 97.8 101.8 2.234567379 -1.766418361
    100 101.0 94.4 -1.035570639 5.626445231
    100 94.6 99.1 5.42093496 0.946998419
    100 102.8 104.5 -2.838812408 -4.466340008
    100 99.7 96.5 0.310392552 3.51272509
    100 93.0 100.4 6.963089498 -0.421282919
    100 105.6 101.4 -5.638639332 -1.371489129
    100 92.0 96.5 7.987873553 3.510365108
    100 95.9 99.8 4.118673325 0.167529403
    100 94.7 91.6 5.252539606 8.353674239
    下载: 导出CSV

    表  4  误差标准差

    Table  4.   Standard deviation of error

    Method Standard deviation of error
    Exponential fitting method 4.85271647048
    BP neural network method 4.47312610406
    下载: 导出CSV
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出版历程
  • 收稿日期:  2020-05-08
  • 修回日期:  2020-07-15
  • 刊出日期:  2021-02-20

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