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

【摘要】：煤與瓦斯突出是礦井瓦斯災害中最危險的災害之一，也是最常見的災害，對礦井災害的預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)生機理的基礎上，實現(xiàn)瓦斯突出危險性的準確預測成為防治瓦斯災害的主要技術手段。為此，本文針對礦井安全生產(chǎn)的需要，以影響礦井瓦斯突出主控因素為研究對象，以礦井瓦斯突出預警為目的，系統(tǒng)地研究了支持向量機和數(shù)據(jù)融合在瓦斯突出預測中的應用。 本文通過研究數(shù)據(jù)融合與支持向量機的理論與方法，構建了基于支持向量機的礦井瓦斯數(shù)據(jù)融合技術框架，提出了瓦斯突出預測的分層融合模型，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的煤與瓦斯突出預測模型；首先對預測系統(tǒng)的數(shù)據(jù)來源進行分析，本文通過灰色關聯(lián)分析法計算出灰色關聯(lián)度來選擇礦井瓦斯突出風險特征指標集，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測系統(tǒng)的特征指標，即輸入數(shù)據(jù)；利用支持向量機對這些特征指標進行模擬訓練，并選擇合適的支持向量機核函數(shù)，利用調步長網(wǎng)格搜索與十折交叉驗證組合的方法對支持向量機參數(shù)進行優(yōu)化，實驗結果表明，在經(jīng)過特征層融合后能夠得到很好的預測結果，但基于SVM固有的缺點，進一步提出用D-S證據(jù)理論作為決策層融合方法，利用SVM的預測結果和幾個典型的指標的預測結果共同作為D-S證據(jù)理論的決策層融合證據(jù)體，，從而構成特征層和決策層的兩層融合結構模型，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)，對本文提出的分層融合預測模型進行了驗證，結果表明通過決策層融合后，所得的預測結果更準確，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.
【學位授予單位】：遼寧工程技術大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：TD713.3

【參考文獻】

相關期刊論文 前10條

1 盧平;煤礦瓦斯災害事故頻發(fā)的原因及其對策分析[J];安全;2005年03期

2 丁世飛;齊丙娟;譚紅艷;;支持向量機理論與算法研究綜述[J];電子科技大學學報;2011年01期

3 李劍峰,樂光新,尚勇;基于改進型D—S證據(jù)理論的決策層融合濾波算法[J];電子學報;2004年07期

4 文光才,徐三民;煤礦瓦斯防治技術的新進展[J];礦業(yè)安全與環(huán)保;2000年01期

5 樊栓保;國內外煤與瓦斯突出預測的新方法[J];礦業(yè)安全與環(huán)保;2000年05期

6 徐耀松;郭磊;王丹丹;;多傳感器模糊信息融合算法在煤礦瓦斯監(jiān)測中的應用[J];礦業(yè)安全與環(huán)保;2008年03期

7 簡小剛;賈鴻盛;石來德;;多傳感器信息融合技術的研究進展[J];中國工程機械學報;2009年02期

8 齊永鋒,火元蓮;支持向量機研究[J];甘肅聯(lián)合大學學報(自然科學版);2005年02期

9 廖志恒;;貴州某煤礦煤與瓦斯突出規(guī)律分析[J];貴州工業(yè)大學學報(自然科學版);2008年03期

10 劉換;趙剛;;人工智能在信息安全風險評估中的應用[J];北京信息科技大學學報(自然科學版);2012年04期

相關博士學位論文 前1條

1 黃為勇;基于支持向量機數(shù)據(jù)融合的礦井瓦斯預警技術研究[D];中國礦業(yè)大學;2009年

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