發(fā)布時(shí)間：2018-04-22 04:06

  本文選題：支持向量機(jī) + 數(shù)據(jù)融合��； 參考：《遼寧工程技術(shù)大學(xué)》2013年碩士論文

【摘要】：煤與瓦斯突出是礦井瓦斯災(zāi)害中最危險(xiǎn)的災(zāi)害之一，也是最常見的災(zāi)害，對(duì)礦井災(zāi)害的預(yù)防和控制已成為礦井安全工作的重中之重。在深入了解瓦斯突出發(fā)生機(jī)理的基礎(chǔ)上，實(shí)現(xiàn)瓦斯突出危險(xiǎn)性的準(zhǔn)確預(yù)測成為防治瓦斯災(zāi)害的主要技術(shù)手段。為此，本文針對(duì)礦井安全生產(chǎn)的需要，以影響礦井瓦斯突出主控因素為研究對(duì)象，以礦井瓦斯突出預(yù)警為目的，系統(tǒng)地研究了支持向量機(jī)和數(shù)據(jù)融合在瓦斯突出預(yù)測中的應(yīng)用。 本文通過研究數(shù)據(jù)融合與支持向量機(jī)的理論與方法，構(gòu)建了基于支持向量機(jī)的礦井瓦斯數(shù)據(jù)融合技術(shù)框架，提出了瓦斯突出預(yù)測的分層融合模型，并確定了各層的融合算法及其完成的主要功能。在特征層融合上，選用SVM作為特征層融合算法，建立基于SVM的煤與瓦斯突出預(yù)測模型；首先對(duì)預(yù)測系統(tǒng)的數(shù)據(jù)來源進(jìn)行分析，本文通過灰色關(guān)聯(lián)分析法計(jì)算出灰色關(guān)聯(lián)度來選擇礦井瓦斯突出風(fēng)險(xiǎn)特征指標(biāo)集，即確定影響煤礦瓦斯突出的主控因素，并把主控因素作為預(yù)測系統(tǒng)的特征指標(biāo)，即輸入數(shù)據(jù)；利用支持向量機(jī)對(duì)這些特征指標(biāo)進(jìn)行模擬訓(xùn)練，并選擇合適的支持向量機(jī)核函數(shù)，利用調(diào)步長網(wǎng)格搜索與十折交叉驗(yàn)證組合的方法對(duì)支持向量機(jī)參數(shù)進(jìn)行優(yōu)化，實(shí)驗(yàn)結(jié)果表明，在經(jīng)過特征層融合后能夠得到很好的預(yù)測結(jié)果，但基于SVM固有的缺點(diǎn)，進(jìn)一步提出用D-S證據(jù)理論作為決策層融合方法，利用SVM的預(yù)測結(jié)果和幾個(gè)典型的指標(biāo)的預(yù)測結(jié)果共同作為D-S證據(jù)理論的決策層融合證據(jù)體，，從而構(gòu)成特征層和決策層的兩層融合結(jié)構(gòu)模型，增加了系統(tǒng)決策的可靠性。最后通過選取某礦區(qū)歷史突出數(shù)據(jù)，對(duì)本文提出的分層融合預(yù)測模型進(jìn)行了驗(yàn)證，結(jié)果表明通過決策層融合后，所得的預(yù)測結(jié)果更準(zhǔn)確，表明了該方案具有很好的可行性和有效性。
[Abstract]:Coal and gas outburst is one of the most dangerous disasters in the mine gas disaster, and it is also the most common disaster. The prevention and control of mine disaster has become the most important part of mine safety work. On the basis of deep understanding of the mechanism of gas outburst, accurate prediction of gas outburst risk has become the main technical means to prevent gas disaster. For this reason, aiming at the need of mine safety production, taking the main control factors of mine gas outburst as the research object, and aiming at the early warning of mine gas outburst, this paper systematically studies the application of support vector machine and data fusion in gas outburst prediction. By studying the theory and method of data fusion and support vector machine, this paper constructs the technical framework of mine gas data fusion based on support vector machine, and puts forward a stratified fusion model for gas outburst prediction. The fusion algorithm of each layer and its main functions are determined. In the feature layer fusion, SVM is selected as the feature layer fusion algorithm to establish the coal and gas outburst prediction model based on SVM. Firstly, the data source of the prediction system is analyzed. In this paper, the grey relational analysis method is used to calculate the grey correlation degree to select the risk index set of mine gas outburst, that is to say, to determine the main control factors affecting the coal mine gas outburst, and take the main control factor as the characteristic index of the prediction system, that is, the input data. Support vector machine (SVM) is used to simulate and train these features, and appropriate kernel function of SVM is selected. The parameters of SVM are optimized by the combination of step size mesh search and 10% cross-validation. The experimental results show that, After feature level fusion, good prediction results can be obtained. However, based on the inherent shortcomings of SVM, D-S evidence theory is further proposed as a decision level fusion method. The prediction results of SVM and several typical indexes are used together as the decision level fusion evidence body of D-S evidence theory, thus the two-layer fusion structure model of feature layer and decision layer is constructed, and the reliability of system decision is increased. Finally, by selecting the historical outburst data of a mining area, the hierarchical fusion prediction model proposed in this paper is verified. The results show that the prediction results are more accurate after the decision level fusion. The results show that the scheme is feasible and effective.
【學(xué)位授予單位】：遼寧工程技術(shù)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2013
【分類號(hào)】：TD713.3

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