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  本文選題：數據挖掘 + 支持向量回歸機��； 參考：《內蒙古科技大學》2013年碩士論文

【摘要】：瓦斯災害是煤礦的主要災害之一，不僅會給工作人員的生命安全造成很大的威脅而且還會帶來大量的財產損失。做好瓦斯防治工作至關重要，而瓦斯涌出量預測是瓦斯防治中的重要環(huán)節(jié)，在很大程度上影響著煤礦的安全生產。因此，根據不同的礦井的情況，選擇與其適應的瓦斯涌出量預測方法對指導礦井安全作業(yè)和制定行之有效的瓦斯災害治理措施具有十分重要的意義。 本文采用數據挖掘的相關技術與方法，對某礦瓦斯涌出影響因素及涌出量進行分析與預測研究，并利用Poly Analyst與MATLAB軟件進行建模與計算。 （1）運用Poly Analyst與MATLAB分別對瓦斯涌出量及影響因素進行相關分析與灰色關聯(lián)分析，并得出以下結果：①某礦的瓦斯涌出量與煤層瓦斯含量、煤層埋深、煤層厚度、開采強度、鄰近層瓦斯含量成正相關，與推進速度和工作面采出率成負相關；②以上各影響因素與瓦斯涌出量的相關系數的絕對值都在0.71以上，呈顯著相關或高度相關。③經兩種分析方法共同確定的影響因素為煤層瓦斯含量、煤層厚度、開采強度、煤層埋深、鄰近層瓦斯含量。 （2）利用Poly Analyst軟件平臺的支持向量回歸機模型對瓦斯涌出量進行預測。通過對訓練數據的預測，選取了支持向量回歸機兩種核函數的參數。由預測結果可知：①當多項式核函數參數及多項式的次數為5時，平均相對誤差最小為0.91%。②當高斯核函數參數及標準偏差為2.1時，，平均相對誤差最小為8.59%。 （3）通過已選的兩種核函數參數對測試數據進行預測，由預測結果可知：多項式核函數預測的平均相對誤差為3.04%，高斯核函數的平均相對誤差為5.39%，前者的預測精度優(yōu)于后者。運用Poly Analyst支持向量回歸機模型進行瓦斯涌出量預測，簡單易行，便于掌握，能夠充分運用瓦斯涌出量影響因素的各項數據，實現速度快、預測精度高，省去了大量繁瑣的計算工作，并且能夠取得良好的預測效果，為瓦斯涌出量的預測的實現提供了一條新途徑。但是，在模型應用的過程中，要注意根據所研究對象的性質，選用合適的核函數及其參數。 （4）利用MATLAB軟件創(chuàng)建了一個滿足網絡設計要求的BP神經網絡。通過對訓練數據與測試數據的預測，可得出如下結論：①隱含層節(jié)點數的增加，雖可以提高網絡的映射能力，但預測的精度不一定提高②對測試數據預測的精度較高，其最大相對誤差為8.14%，平均相對誤差為3.68%，誤差小于10%滿足精度要求。③為了使網絡預測精度進一步提高，收集的樣本數據應盡可能的多而準確，還要確定
[Abstract]:Gas disaster is one of the main disasters in coal mine, which will not only cause a great threat to the safety of workers, but also bring a lot of property losses. It is very important to do gas prevention and control work well, and the prediction of gas emission is an important link in gas prevention and control, which affects the safety production of coal mine to a great extent. Therefore, according to the situation of different mines, it is very important to choose the method of gas emission prediction to guide mine safety operation and to formulate effective measures for gas disaster control. In this paper, the related techniques and methods of data mining are used to analyze and predict the influencing factors and quantity of gas emission in a certain mine. Using Poly Analyst and MATLAB software to model and calculate. (1) Poly Analyst and MATLAB are used to analyze the quantity of gas emission and the influencing factors respectively. The following results are obtained: the amount of gas emission is positively correlated with the gas content of coal seam, the depth of coal seam, the thickness of coal seam, the intensity of mining, and the gas content of adjacent strata, but negatively with the speed of advancing and the mining rate of working face; (2) the absolute value of the correlation coefficient between the above factors and the amount of gas emission is above 0.71. The influencing factors determined by the two analysis methods are the gas content of coal seam, the thickness of coal seam, the intensity of mining. The gas emission is predicted by using the support vector regression model of Poly Analyst software platform. Based on the prediction of training data, the parameters of two kernel functions of support vector regression machine are selected. The prediction results show that when the parameters of polynomial kernel function and the degree of polynomial are 5, the average relative error is 0.91and 2.2. when the parameter and standard deviation of Gao Si kernel function is 2.1, The average relative error is the minimum of 8.59.) the test data is predicted by two selected kernel function parameters. The prediction results show that the average relative error of polynomial kernel function is 3.04 and the average relative error of Gao Si kernel function is 5.39.The former is better than the latter. The prediction of gas emission by using Poly Analyst support vector regression model is simple and easy to grasp. It can make full use of the data of influencing factors of gas emission and achieve fast speed and high prediction accuracy. It saves a lot of tedious calculation work, and can achieve good prediction results, which provides a new way to realize the prediction of gas emission. However, in the application of the model, we should pay attention to the selection of appropriate kernel function and its parameters according to the properties of the object studied. (4) A BP neural network that meets the requirements of network design is created by using MATLAB software. Through the prediction of training data and test data, we can draw the following conclusion: the increase of the number of nodes in the hidden layer of 1 can improve the mapping ability of the network, but the accuracy of prediction does not necessarily improve the accuracy of 2 pairs of test data prediction. The maximum relative error is 8.14, the average relative error is 3.68, the error is less than 10% to meet the precision requirement .3 in order to further improve the accuracy of network prediction, the sample data collected should be as much and accurate as possible, and must be determined.
【學位授予單位】：內蒙古科技大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：TD712.5

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本文編號：2039239