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PCA-BP模型在判别基于LIF技术煤矿突水水源的应用
引用本文:王亚,周孟然,闫鹏程,何晨阳,刘栋. PCA-BP模型在判别基于LIF技术煤矿突水水源的应用[J]. 光谱学与光谱分析, 2017, 37(3): 978-983. DOI: 10.3964/j.issn.1000-0593(2017)03-0978-06
作者姓名:王亚  周孟然  闫鹏程  何晨阳  刘栋
作者单位:1. 安徽理工大学电气与信息工程学院,安徽 淮南 232001
2. 阜阳师范学院计算机与信息工程学院,安徽 阜阳 236037
基金项目:the "Twelfth Five-year" National Science and Technology Support Projects,the National Science Foundatio n Project of China,the National Safe Production Critical Incident to Key Technologies Science and Technology Project,the Prov incial Natural Science Research Project,the College Young Talent Support Plan
摘    要:防治煤矿突水时需迅速精准地判别突水水源,激光诱导荧光(LIF)光谱技术具有灵敏度高、快速准确监测特点,为检测突水水源提供了一种新的方法。该研究引入该技术以获取突水荧光光谱数据。采用卷积(SG)平滑和多元散射校正(MSC)方法对光谱图进行预处理,以消除光谱采集过程中噪声干扰。采用主成分分析(PCA)方法提取特征信息,针对SG预处理后的数据,当主成分个数为3时,累积贡献率可达到99.76%,已基本保留原数据的全信息。选择3层结构BP神经网络建立分类判别模型,通过不同方式构造训练集和测试集,SG预处理数据构建的分类模型可以达到精准判别,而对于MSC预处理和原始数据出现很少的误判。实验结果表明SG预处理结果要优于MSC预处理。研究结果表明,将PCA和BP神经网络结合建立分类模型,能有效判别煤矿突水水源,且具有较强的自组织、自学习能力。

关 键 词:煤矿突水  水源判别  激光诱导荧光光谱  人工神经网络  主成分分析   
收稿时间:2016-04-13

Identification of Coalmine Water Inrush Source with PCA-BP Model Based on Laser-Induced Fluorescence Technology
WANG Ya,ZHOU Meng-ran,YAN Peng-cheng,HE Chen-yang,LIU Dong. Identification of Coalmine Water Inrush Source with PCA-BP Model Based on Laser-Induced Fluorescence Technology[J]. Spectroscopy and Spectral Analysis, 2017, 37(3): 978-983. DOI: 10.3964/j.issn.1000-0593(2017)03-0978-06
Authors:WANG Ya  ZHOU Meng-ran  YAN Peng-cheng  HE Chen-yang  LIU Dong
Affiliation:1. College of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China2. School of Computer and Information, Fuyang Teachers College, Fuyang 236037, China
Abstract:The water inrush should been rapidly and accurately identified during preventing coalmine water inrush .The laser induced fluorescent (LIF) spectrum technology provides a new method to identify water inrush with the characteristics of high sensitivity,quick and accurate monitoring .In order to identify water inrush,t his paper introduces the spectrum technology of LIF to obtain water inrush fluor escence spectra data .The spectral preprocessing methods of Savitzky-Golay(SG) and Multiplicative Scatter Correction (MSC) have been used to eliminate noise spectra in collecting process .Principal component analysis (PCA) extracts feature information,for SG reprocessing data,when the number of principal component is 3,the cumulative contribution rate can reach 9976 percent .This method has largely retained the information of original data .This paper chooses the classification model with 3 layers BP neural network,constructing by different training and testing sets .The classification model with SG preprocessing has achieved accurate identification,however,appeared few false identification for MSC and original data .The result shows that SG preprocessing is better than MSC .Research results show that the classification model with PCA and BP neural network ca n effectively identify coalmine water inrush,and have the strong self-organizi ng,self-learning ability .
Keywords:Coalmine water inrush  Identification model  Laser-induced fluorescence  Principal component analysis  Artificial neural network
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