基于FTIR的污染气体预处理与神经网络模式识别研究.doc

傅里叶变换红外光谱技术论文：基于FTIR的污染气体预处理与神经网络模式识别研究【中文摘要】随着大气污染问题的加剧，环境问题越来越受到人们的关注，快速、准确地预测出大气污染气体，对环境保护有着巨大的作用。傅里叶变换红外光谱技术以测量迅速，分辨率高，测量精度高等优点，在气体检测领域得到越来越广泛的应用。本文从傅里叶变换红外光谱技术的应用和傅里叶变换红外光谱仪的优点出发，分析了光谱数据预处理和模式识别的国内外研究现状，搭建了红外光谱数据采集系统，并对CO、NO2和NO三组分混合气体进行建模分析。通过质量流量控制器精密控制三种气体进行不同比例的混合，并通过傅里叶变换红外光谱仪采集大量的数据样本。针对红外光谱数据易受噪声的影响和呈现复杂非线性关系，选择小波去噪和神经网络进行建模，并对两种方法进行改进，能够有效的预测出光谱的成分组成。首先对采集到的红外光谱数据进行预处理，用改进小波去噪方法消除高频噪声，之后选择主成分分析对样本的波段进行降维，降维之后只用三个主成份就可以完全覆盖原样本数据的99%的信息量，减少了运算时间和计算量。使用四种典型神经网络和优化后的神经网络对样本数据进行模式识别建模，并用预测样本数据对所建立的模型进行验证分析，分析结果表明优化后的神经网络的分类精度达到97.78%，均方误差为0.0222，相比四种典型神经网络模型，优化后的神经网络模型分类精度更好。【英文摘要】With the aggravation of air pollution, environmental problems are increasinglyconcerned. It plays a enormous role in the environmental protection to predict pollutant gasquickly and accurately. Fourier transform infrared （FTIR） spectroscopy technology has beenmore widely used in the field of gas detection because of its rapid measurement , highresolution and high measuring accuracy.The paper starts from the application and the advantage of FTIR spectrometer to analyzethe research status of the pretreatment of spectral data and pattern recognition at home andabroad. On these bases, the infrared spectral acquisition system is designed. And then threetypes of mixed gas CO, NO2and NO are chosen to be analyzed in the system. Three gases aremixed at a different ratio by the mass flow controller （MFC）. The spectral data of themixtures is collected by FTIR spectrometer. Because of noise influence and the complexnonlinear relationship of IR data, wavelet de-noising and neural network are chosen andimproved, and both methods can effectively predict the spectral components.At first, IR spectral data collected is pretreated using a modified wavelet de-noising toeliminate high frequency noise. Then, principal component analysis （PCA） is selected todescend dimension for sample band. After descending dimension, the 99% information of theoriginal sample data could be completely covered by only three principal components, thustime and computation could be reduced. Four typical neural network and optimized neuralnetwork are used to build model for pattern recognition and sample data with the prediction isused to validate and analysis the model. The results show that the classification accuracy ofoptimized neural network can reach 97.78%, and mean square error （MSE） can reach 0.0222.The classification accuracy of optimized neural network model is better than the four typicalneural network model.【关键词】傅里叶变换红外光谱技术 小波去噪 模式识别 人工神经网络【英文关键词】Fourier transform infrared spectroscopy technology wavelet de-noising pattern recognition artificial neural network加好友索全文：一三九九三八八四八（同时提供论文写作指导和论文发表委托）【目录】基于FTIR的污染气体预处理与神经网络模式识别研究摘要4-5Abstract51 引言9-161.1 选题的背景、目的及意义91.2 大气环境污染9-111.2.1 大气污染的来源101.2.2 大气污染的危害10-111.3 气体检测技术概述11-131.3.1 常规检测技术11-121.3.2 FTIR 检测技术12-131.4 国内外研究现状13-141.5 本课题主要内容14-162 FTIR 光谱技术16-242.1 FTIR 基本原理16-182.2 FTIR 技术发展18-192.3 WQF-520 简介19-212.4 红外光谱模式识别步骤21-243 红外光谱数据预处理24-363.1 化学计量学方法24-263.1.1 窗口移动均值滤波243.1.2 窗口移动中位数平滑法24-253.1.3 Savitzky-Golay 滤波25-263.2 小波阈值滤波26-293.2.1 小波阈值分类26-283.2.2 小波阈值的选取方法28-293.3 改进的阈值去噪方法29-303.4 实验方法对比30-353.5 结论35-364 神经网络模式识别36-464.1 典型神经网络37-434.1.1 BP 神经网络37-414.1.2 RBF 神经网络41-424.1.3 GRNN 神经网络424.1.4 PNN 神经网络42-434.2 改进神经网络43-444.2.1 神经网络权值修正43-444.2.2 系数的修正444.3 神经网络模式识别过程44-465 实验系统与结果分析4