基于机器学习的染液光谱分类算法研究

发布时间：2018-01-04 15:09

本文关键词：基于机器学习的染液光谱分类算法研究　出处：《浙江理工大学》2017年硕士论文　论文类型：学位论文

【摘要】：我国作为全球印染行业的中心,传统粗放的人工发展模式已经不能满足人们日益增长的需求,同时印染行业产生的工业废水也给环境带来了巨大压力,因此实现自动控制印染生产并实时监测染液各个参数是印染发展的必然趋势。染液浓度是参数监测中最重要的一部分,目前国内外最常用的浓度检测方法是分光光度法,此方法以朗伯比尔定律为基础,利用吸光度与浓度之间的线性关系实现染液浓度的测量。但实际生产中大部分混合染液存在吸收光谱重叠或干扰等问题,现有常用的化学计量法并不能达到印染行业实际要求的检测精度。为满足企业生产和发展的要求,混合染液浓度检测必须借助于更智能的化学计量法或其改进算法来提高检测精度。本文在学习现有机器学习理论的基础上,提出了基于机器学习的染液光谱分类算法研究。机器学习能从观测数据出发寻找出尚不能通过理论分析得到的规律,通过构造具有低结构风险和高泛化能力的分类模型实现对数据的预测。其中,支持向量机(SVM)是一种基于结构风险最小化(SRM)原则构建最大间隔分类面的机器学习方法,对于非线性回归问题SVM可通过核函数转化为线性可分问题;神经网络可通过调节感知器的权值和阈值得到数据的规律,具有很强的分类能力。SVM算法和神经网络在多分类领域都已有较成熟的成果,深度学习作为神经网络的衍生,具有学习更深层次特征的能力,但目前SVM和深度学习在混合光谱分类方面的研究还不够完善。基于混合光谱分类的研究现状,本文提出了基于支持向量机和深度学习的多组分混合染液光谱分类算法。本文的研究内容主要包括以下四个部分:(1)设计搭建光谱采集系统,卤钨灯作为光源;芯径400μm的光纤作为光源传输通道,可减少能量损失;SMA Z-流通池作为样品池,盛放循环的混合染液;采用USB2000+光纤光谱仪采集混合染液吸光度数据;(2)提出一种将归一化、Savitzky-Golay(SG)卷积平滑法和SPXY法进行组合的数据处理方法,归一化能减少噪声和漂移对吸光度数据的影响;SG卷积平滑法可滤除杂质对吸光度数据的影响;SPXY法通过综合考虑吸光度数据之间和吸光度与浓度之间的关系将样本集划分为75个训练集和5个测试集;(3)提出一种基于连续投影算法(SPA)和支持向量机的三组分混合弱酸性染液光谱分类模型。利用SPA算法提取吸光度数据的22个光谱特征;通过在原目标函数上增加L2正则项得到改进的nu-SVR模型,nu-SVR分类模型采用RBF核函数,其正则参数C和核参数σ通过交叉验证得到最优值。实验证明,该算法在提高混合染液光谱分类精度的同时,模型分类时间也得到了进一步缩短,可为后期实现多组分染液光谱在线分类提供参考;(4)提出一种基于遗传算法(GA)、深度学习和反向传播(BP)算法的三组分混合活性染液光谱分类模型。利用GA算法得到深度网络的最佳隐层数为2,每层单元数为60,通过最佳深度网络模型提取吸光度数据的深度特征,BP算法两个隐含层的单元数为14和8。实验表明,该算法能有效提高多组分混合染液的光谱分类精度。
[Abstract]:China as the world's printing and dyeing industry center, artificial development mode of the traditional extensive has been unable to meet the growing needs of people at the same time, the industrial wastewater of printing and dyeing industry has brought tremendous pressure to the environment, thus to realize the automatic control and real-time monitoring of the dye printing and dyeing production parameters is the inevitable trend of the development of printing and dyeing is a part of the dye concentration. The important parameters in the monitoring of the concentration detection method most commonly used at home and abroad are spectrophotometry, this method is based on Longbow Bill's law, the linear relationship between the absorbance and the concentration of the dye concentration measurement. But in the actual production of most existing mixed dye absorption spectra overlap or interference, the existing chemical measurement method the printing and dyeing industry and can not achieve the detection accuracy. In order to meet the actual requirements of the enterprise production and the requirements of the development of mixed dye concentration detection. Test must rely on the stoichiometry of more intelligent or its improved algorithm to improve the detection precision. Based on the study of existing machine learning theories, put forward the research of dye spectral classification algorithm based on machine learning. From the observation data of machine learning to find out is not obtained by law theory to forecast data the classification model has low risk structure and high generalization ability by constructing. Among them, the support vector machine (SVM) is a kind of based on structural risk minimization (SRM) principle to build the largest interval classification surface machine learning method for nonlinear regression problem by SVM kernel function into a linear separable problem; neural network can be obtained the rules by adjusting the perceptron weights and thresholds, with the ability of classification.SVM algorithm and neural network is very strong in many areas are already mature classification results Deep learning, the neural network is derived, with the ability to learn more profound features, but the current SVM and deep learning research in the classification of spectral mixture is still not perfect. The research status of mixed spectral classification based on support vector machine is proposed in this paper and deep learning of multicomponent mixed dye spectral classification algorithm based on research. The main contents of this paper include the following four parts: (1) design of spectrum acquisition system, tungsten halogen lamp as the light source; the fiber core diameter of 400 mu m as a light source transmission channel, can reduce energy loss; SMA Z- circulation pool as the sample pool, a mixed dyeing cycle; using USB2000+ fiber optic spectrometer to collect mixed absorbance data; (2) propose a normalized Savitzky-Golay (SG) method combined data smoothing method and SPXY method can reduce the noise and drift of normalized absorbance data The influence of impurities on the filter; can affect the absorbance data of SG convolution smoothing method; the relationship between the SPXY method by considering the absorbance data and the absorbance and the concentration of the sample set will be divided into 75 training sets and 5 test sets; (3) put forward an algorithm based on successive projection (SPA) and support vector machine the three component mixed weak acid dye spectral classification model. The extraction of 22 spectral absorbance data using the SPA algorithm; by increasing the L2 regularization in the original objective function to get the improved nu-SVR model, the nu-SVR classification model using RBF kernel function and the regularization parameter C and kernel parameter to get the optimal value through cross validation experiments., this algorithm can improve the classification accuracy of mixed dye spectra at the same time, the classification model of time has been further shortened, multicomponent dye spectrum online classification and provide reference for later implementation; (4) a proposed In the genetic algorithm (GA), deep learning and back propagation (BP) algorithm three component mixed spectral classification model. The best reactive dye hidden layer uses GA algorithm to get the depth of the network is 2, the number of units of each layer is 60, the best depth network model to extract depth characteristic absorbance data, BP algorithm two the hidden layer units showed that the 14 and 8. experiments, the algorithm can effectively improve the multicomponent dye spectral classification accuracy.

【学位授予单位】：浙江理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TS190;TP181

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