本文選題：煤與瓦斯突出 + 支持向量機(jī)　； 參考：《中國計(jì)量學(xué)院》2013年碩士論文

【摘要】：煤與瓦斯突出是一個(gè)受多種因素綜合影響的、復(fù)雜的非線性問題，用傳統(tǒng)方法對其進(jìn)行預(yù)測有很大缺陷。隨著計(jì)算機(jī)技術(shù)和信息處理技術(shù)的快速發(fā)展，很多智能方法和技術(shù)也逐漸滲透到了類似于突出預(yù)測的一些問題中，，其中支持向量機(jī)是一種基于統(tǒng)計(jì)學(xué)習(xí)理論的機(jī)器學(xué)習(xí)方法，主要用于解決小樣本、非線性、高維數(shù)、局部極小值等實(shí)際問題，并且具有良好的分類識別效果，已被廣泛應(yīng)用到眾多領(lǐng)域的模式識別和預(yù)測預(yù)報(bào)中。為此，本文提出以現(xiàn)場和實(shí)驗(yàn)室檢測數(shù)據(jù)為基礎(chǔ)，通過引入支持向量機(jī)建立學(xué)習(xí)模型，實(shí)現(xiàn)突出危險(xiǎn)性的分類預(yù)測。 由于突出影響因素眾多，不易區(qū)分突出發(fā)生的必要條件。因此，必須對原始數(shù)據(jù)進(jìn)行預(yù)處理，以便獲得影響突出的關(guān)鍵因素。為了有效解決該問題，本文采用灰色關(guān)聯(lián)分析與熵權(quán)法結(jié)合的方法從原始樣本中提取關(guān)鍵的特征指標(biāo)。 通過關(guān)鍵指標(biāo)選取預(yù)測模型的訓(xùn)練和測試樣本，并在此基礎(chǔ)上建立支持向量機(jī)預(yù)測模型，其中整個(gè)模型的訓(xùn)練及測試過程在MATLAB平臺下完成，并調(diào)用了LIBSVM軟件包中的部分函數(shù)進(jìn)行仿真程序的設(shè)計(jì)。另外，本文從支持向量機(jī)自身核函數(shù)選型以及參數(shù)優(yōu)化的角度，對模型分類準(zhǔn)確性的影響進(jìn)行進(jìn)一步研究，驗(yàn)證基于徑向基（RBF）核函數(shù)更適合用于煤礦的突出分類預(yù)測。在此基礎(chǔ)上分別通過交叉驗(yàn)證法和遺傳算法對支持向量機(jī)的懲罰參數(shù)C和核參數(shù)g進(jìn)行尋優(yōu)，證明遺傳算法能夠在兩個(gè)參數(shù)優(yōu)選的前提下取得更好的測試效果。 最后利用支持向量機(jī)的分類預(yù)測方法建立五陽煤礦南豐擴(kuò)區(qū)76、78采區(qū)的區(qū)域危險(xiǎn)性預(yù)測模型，測試結(jié)果與實(shí)際突出危險(xiǎn)性情況相符。因此，該支持向量機(jī)模型可被用于采區(qū)未知區(qū)域的突出危險(xiǎn)性預(yù)測。另外，本文結(jié)合“四位一體”綜合防突措施，針對五陽煤礦提出以瓦斯預(yù)抽為主的防突措施。最后，通過超前鉆孔進(jìn)行防突措施有效性分析和檢驗(yàn)。為實(shí)現(xiàn)該礦今后的煤與瓦斯突出綜合防治提供方向。
[Abstract]:Coal and gas outburst is a complex nonlinear problem influenced by many factors. It has great defects to predict coal and gas outburst by traditional methods. With the rapid development of computer technology and information processing technology, many intelligent methods and techniques have gradually penetrated into some problems similar to prominent prediction, among which support vector machine is a machine learning method based on statistical learning theory. It is mainly used to solve the practical problems such as small sample, nonlinear, high dimension, local minimum, and has good classification and recognition effect. It has been widely used in pattern recognition and prediction in many fields. Therefore, based on the field and laboratory data, a learning model based on support vector machine (SVM) is proposed to realize the classification and prediction of outburst hazards. It is difficult to distinguish the necessary conditions of protruding because of its numerous influence factors. Therefore, the raw data must be preprocessed in order to obtain the key factors that have a prominent impact. In order to solve the problem effectively, this paper uses the method of combining grey correlation analysis and entropy weight method to extract the key feature index from the original sample. The training and test samples of the prediction model are selected through the key indicators, and the prediction model of support vector machine is established on this basis, in which the training and testing process of the model is completed under the MATLAB platform. Some functions in LIBSVM software package are called to design the simulation program. In addition, from the point of view of kernel function selection and parameter optimization of support vector machine, this paper further studies the effect of model classification accuracy, and verifies that RBF-based kernel function is more suitable for coal mine outburst classification prediction. On this basis, the penalty parameter C and kernel parameter g of support vector machine are optimized by cross-validation method and genetic algorithm, respectively. It is proved that the genetic algorithm can obtain better test results under the premise of optimal selection of two parameters. Finally, the regional hazard prediction model of 76YO78 mining area in Nanfeng expansion area of Wuyang coal mine is established by using the classification and prediction method of support vector machine. The test results are consistent with the actual outburst hazard situation. Therefore, the support vector machine model can be used to predict the outburst risk in unknown areas. In addition, combined with the comprehensive anti-outburst measures of "four in one", this paper puts forward some measures to prevent outburst in Wuyang coal mine. Finally, the effectiveness of anti-outburst measures is analyzed and tested through advance drilling. It provides the direction for the comprehensive prevention and control of coal and gas outburst in the future.
【學(xué)位授予單位】：中國計(jì)量學(xué)院
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2013
【分類號】：TD713

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