發(fā)布時(shí)間：2018-03-26 08:48

  本文選題：水松紙　切入點(diǎn)：透氣度　出處：《昆明理工大學(xué)》2016年碩士論文

【摘要】：打孔水松紙透氣度的大小嚴(yán)重制約卷煙中焦油的含量,而焦油又是香煙對(duì)人體造成傷害的主要成分,隨著健康問(wèn)題日益成為人們關(guān)注的焦點(diǎn),水松紙透氣度檢測(cè)也正在逐步成為煙草行業(yè)的技術(shù)重點(diǎn),關(guān)系著是否能在煙草行業(yè)立足,是人們健康問(wèn)題的根本保障。因此,水松紙透氣度檢測(cè)分析是當(dāng)前一直被關(guān)注的研究領(lǐng)域,構(gòu)建有效的水松紙透氣度軟測(cè)量模型對(duì)水松紙透氣度實(shí)現(xiàn)高效檢測(cè)具有重大意義。本文根據(jù)已設(shè)計(jì)的基于圖像的新型透氣度檢測(cè)設(shè)備,結(jié)合傳統(tǒng)打孔水松紙透氣度檢測(cè)原理和水松紙圖像信息,分析了影響打孔水松紙透氣度的主要因素為打孔水松紙打孔面積和水松紙灰度值,進(jìn)而采用單輸入(打孔水松紙打孔面積)、雙輸入(打孔水松紙打孔面積、水松紙灰度值)兩種方式對(duì)水松紙透氣度進(jìn)行建模并實(shí)現(xiàn)檢測(cè)。由于支持向量機(jī)(Support Vector Machine,SVM)具有能夠較好地解決小樣本、非線性、高維數(shù)問(wèn)題的特性,故本文采用SVM建立軟測(cè)量模型并對(duì)水松紙透氣度進(jìn)行檢測(cè)。首先,基于對(duì)支持向量機(jī)(Support Vector Machine,SVM)的研究,通過(guò)利用訓(xùn)練誤差的平方代替松弛變量,將不等式約束改為等式約束,從而提出基于最小二乘支持向量機(jī)(Least Squares Support Vector Machine,LSSVM)的水松紙透氣度軟測(cè)量模型。這可避免求解二次規(guī)劃問(wèn)題,提高了檢測(cè)模型的訓(xùn)練速度。其次,考慮到LSSVM水松紙透氣度軟測(cè)量模型的參數(shù)對(duì)檢測(cè)結(jié)果精度有著至關(guān)重要的影響,為避免模型參數(shù)選擇的盲目性,提高模型的泛化能力,本文利用粒子群優(yōu)化算法(Particle Swarm Optimization,PSO)對(duì)LSSVM中的參數(shù)進(jìn)行確定,得到基于PSO-LSSVM的水松紙透氣度軟測(cè)量模型。基于實(shí)際數(shù)據(jù)的仿真實(shí)驗(yàn)表明,所提的單輸入和雙輸入模型均能都得到更好的檢測(cè)效果。最后,為了進(jìn)一步提高單一模型的檢測(cè)精度和泛化能力,在PSO-LSSVM的基礎(chǔ)上,結(jié)合集成學(xué)習(xí)方法,提出了一種改進(jìn)的AdaBoost-PSO-LSSVM水松紙透氣度軟測(cè)量模型�；趯�(shí)際數(shù)據(jù)的仿真測(cè)試表明,所提出的基于改進(jìn)的AdaBoost-PSO-LSSVM比PSO-LSSVM具有更高的檢測(cè)精度。采用論文所建立軟測(cè)量模型的新型透氣度檢測(cè)設(shè)備已在國(guó)內(nèi)某水松紙廠得到了實(shí)際運(yùn)用,效果良好。
[Abstract]:The degree of air permeability of perforated pine paper seriously restricts the content of tar in cigarettes, and tar is the main component of cigarette harm to human body, with the health issues increasingly becoming the focus of attention. The measurement of air permeability of pine paper is gradually becoming the technical focus of tobacco industry, which is related to whether it can be established in the tobacco industry, and is the fundamental guarantee of people's health problems. Air permeability detection and analysis of water pine paper is a research field that has been paid close attention to at present. It is of great significance to construct an effective soft sensing model for air permeability measurement of water pine paper. Based on the principle of air permeability detection and image information, the paper analyzes that the main factors that affect the air permeability of the paper are the perforating area and the gray value of the paper. Then the single input (perforating water paper perforation area), double input (perforating water paper perforation area, Because the support vector machine (SVM) can solve the problem of small sample, nonlinear and high dimension, it can solve the problem of small sample, nonlinear and high dimension, because the support vector machine (SVM) can solve the problem of small sample, nonlinear and high dimension. In this paper, SVM is used to establish a soft sensing model and to detect the air permeability of water pine paper. Firstly, based on the research of support Vector Machine (SVM), the inequality constraint is changed into an equality constraint by using the square of training error instead of the relaxation variable. Thus, a soft sensing model for air permeability of water pine paper based on least squares support vector machine (Least Squares Support Vector Machine) is proposed, which can avoid solving quadratic programming problem and improve the training speed of the detection model. Considering that the parameters of the soft sensing model for the air permeability of LSSVM water pine paper have an important influence on the accuracy of the test results, in order to avoid the blindness of model parameter selection and improve the generalization ability of the model, In this paper, particle swarm optimization algorithm (PSO) is used to determine the parameters of LSSVM, and a soft sensing model of air permeability of water pine paper based on PSO-LSSVM is obtained. In order to further improve the detection accuracy and generalization ability of the single model, the proposed single input and double input models can both get better detection results. Finally, based on PSO-LSSVM, an integrated learning method is used to improve the detection accuracy and generalization ability of the single model. An improved soft sensing model for air permeability of AdaBoost-PSO-LSSVM water pine paper is proposed. The simulation results based on the actual data show that, The improved AdaBoost-PSO-LSSVM has higher detection accuracy than PSO-LSSVM. A new type of air permeability testing equipment based on soft sensing model has been applied in a domestic pines mill with good results.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2016
【分類號(hào)】：TS77;TP18

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