Prediction model of gas volume fraction based on golden section and BP network

YAN Dan2; ZHANG Ji-long1; WANG Zhi-bin1; CHEN Yuan-yuan2

doi:10.13873/j.1000-97872012.01.003

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Prediction model of gas volume fraction based on golden section and BP network

更新时间：2025-09-11

- Prediction model of gas volume fraction based on golden section and BP network
- Vol. 31, Issue 1, Pages: 4-6(2012)
- 作者机构：
  
  1. 山西省光电信息与仪器工程技术研究中心
  2. 中北大学仪器科学与动态测试教育部重点实验室
- 作者简介：
- 基金信息：
- DOI：10.13873/j.1000-97872012.01.003
  CLC： X831;TP183
- Published：2012
- 稿件说明：
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[1]闫丹,张记龙,王志斌,陈媛媛.基于黄金分割和BP网络的气体体积分数预测模型[J].传感器与微系统,2012,31(01):4-6.

YAN Dan2, ZHANG Ji-long1, WANG Zhi-bin1, et al. Prediction model of gas volume fraction based on golden section and BP network[J]. 2012, 31(1): 4-6.
[1]闫丹,张记龙,王志斌,陈媛媛.基于黄金分割和BP网络的气体体积分数预测模型[J].传感器与微系统,2012,31(01):4-6. DOI： 10.13873/j.1000-97872012.01.003.

YAN Dan2, ZHANG Ji-long1, WANG Zhi-bin1, et al. Prediction model of gas volume fraction based on golden section and BP network[J]. 2012, 31(1): 4-6. DOI： 10.13873/j.1000-97872012.01.003.

摘要

研究基于黄金分割法的神经网络在红外光谱定量中的应用。通过黄金分割法对神经网络隐含层节点数进行优化

再将4组分混合气体预测集样本通过LM学习算法建立模型

用所得到的神经网络模型对10组混合气体光谱数据进行检验

平均相对误差为0.63%。使用该方法建立多组分污染气体定量分析模型的时间短

而且能够较精确地预测各组分的体积分数。

Abstract

The application of the quantitative analysis of the infrared spectra based on neural network with golden section method is studied.Golden section method optimizes the number of nodes in the hidden layer of neural networks.Using the LM learning algorithm models of the sample of the four-component mixture gas are set up.Using the model of the neural network

tests on spectral data of 10 groups of mixture gas and the average relative error is 0.63 %.By this method

the time to build multi-component model for quantitative analysis on pollutional gas is short

and this model can accurately predict the volume fraction of each component.

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references

基于人工神经网络的荧光光谱识别 [J]. 范怀玉,姚青,申金媛. 济宁医学院学报 . 2010(04)

基于BP神经网络的煤矿瓦斯浓度预测模型构建及仿真 [J]. 姜雷. 矿业安全与环保 . 2010(04)

SOF-FTIR在化工厂区氨气排放通量测量中的应用 [J]. 金岭,高闽光,刘文清,陆亦怀,张玉钧,王亚萍,张天舒,徐亮,刘志明,陈军. 光谱学与光谱分析 . 2010(06)

BP神经网络算法的改进及应用 [J]. 王美玲,王念平,李晓. 计算机工程与应用 . 2009(35)

运用GA-BP神经网络研究时间序列的预测 [J]. 黄建国,罗航,王厚军,龙兵. 电子科技大学学报 . 2009(05)

BP神经网络在废气测量中的应用 [J]. 黄建清,朱伟兴,李丽. 农机化研究 . 2009(04)

无背景遥感FT-IR干涉图特征信息提取方法 [J]. 兰天鸽,方勇华,熊伟. 红外与激光工程 . 2007(06)

前向神经网络隐含层节点数的一种优化算法 [J]. 夏克文,李昌彪,沈钧毅. 计算机科学 . 2005(10)

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