本文選題：底板突水　切入點(diǎn)：支持向量機(jī)　出處：《山東科技大學(xué)》2017年碩士論文　論文類(lèi)型：學(xué)位論文

【摘要】：我國(guó)華北型煤田底板突水問(wèn)題普遍嚴(yán)重,事故的發(fā)生會(huì)造成重大的人員傷亡和財(cái)產(chǎn)損失,而風(fēng)險(xiǎn)預(yù)測(cè)和評(píng)價(jià)是礦井水害防治的一個(gè)重要環(huán)節(jié),也是實(shí)現(xiàn)安全開(kāi)采的基本前提和重要基礎(chǔ)。本文通過(guò)對(duì)煤礦突水預(yù)測(cè)現(xiàn)狀的研究,結(jié)合煤層底板突水的非線性特征,選用支持向量機(jī)進(jìn)行煤礦突水危險(xiǎn)性預(yù)測(cè)。支持向量機(jī)是基于統(tǒng)計(jì)學(xué)習(xí)理論發(fā)展起來(lái)的一種新型機(jī)器學(xué)習(xí)算法,具有較強(qiáng)的泛化能力,適用于解決突水預(yù)測(cè)這樣的非線性、小樣本問(wèn)題。但是,支持向量機(jī)的泛化能力和預(yù)測(cè)精度受到懲罰參數(shù)和核函數(shù)參數(shù)等相關(guān)參數(shù)的影響,針對(duì)支持向量機(jī)預(yù)測(cè)模型參數(shù)難以確定的問(wèn)題,通過(guò)對(duì)種群的初始化設(shè)置、適應(yīng)度函數(shù)和終止條件的設(shè)置不斷更新粒子速度和位置,從而對(duì)影響支持向量機(jī)性能的相關(guān)參數(shù)進(jìn)行尋優(yōu),得到基于粒子群算法的改進(jìn)支持向量機(jī)預(yù)測(cè)模型。在PSO-SVM突水預(yù)測(cè)模型的應(yīng)用過(guò)程中,首先分析研究區(qū)域礦井的地質(zhì)、水文地質(zhì)條件,選取影響煤層底板突水的主控因素(即隔水層厚度、水壓、底板破壞深度、含水層和斷層落差),然后搜集典型突水工作面的歷史數(shù)據(jù)資料,并將這些數(shù)據(jù)分為訓(xùn)練集和測(cè)試集兩部分;以MATLAB2014a為實(shí)驗(yàn)平臺(tái),結(jié)合Microsoft Visual C++編譯器在MATLAB軟件中添加Libsvm工具箱,通過(guò)代碼編程對(duì)訓(xùn)練集數(shù)據(jù)進(jìn)行仿真訓(xùn)練與測(cè)試,得出支持向量機(jī)的最優(yōu)懲罰因子C和核函數(shù)參數(shù)σ分別為694.8591和317.1063;將測(cè)試集數(shù)據(jù)代入訓(xùn)練好的支持向量機(jī)模型,對(duì)工作面突水危險(xiǎn)性進(jìn)行預(yù)測(cè),并將PSO-SVM模型的預(yù)測(cè)結(jié)果與突水系數(shù)法的預(yù)測(cè)結(jié)果、實(shí)際情況進(jìn)行對(duì)比分析。實(shí)驗(yàn)結(jié)果表明,PSO-SVM模型在突水預(yù)測(cè)中具有較高的精度與工程應(yīng)用推廣價(jià)值,對(duì)保證煤礦安全生產(chǎn)具有重要的意義。
[Abstract]:North China coal mine water inrush accident occurred serious problems, will cause serious casualties and property losses, and prediction and risk evaluation is an important link of water disasters, but also an important basis for safe mining. This paper studies the status quo of water inrush prediction, nonlinear characteristics combined with the water inrush from coal seam floor, support vector machine is used to predict the risk of water inrush in mine. The support vector machine is based on statistical learning theory developed a kind of new machine learning algorithm, has strong generalization ability, suitable for solving such nonlinear prediction of water inrush, the small sample problem. But the effect of support vector machine the generalization ability and prediction accuracy by the penalty parameter and kernel function parameters, model parameters for support vector machine is difficult to determine, through to the population Initialization, fitness function and termination condition set update the particle velocity and position, so as to optimize the relevant parameters affecting the performance of support vector machine, improved particle swarm optimization algorithm based on support vector machine prediction model. In the process of PSO-SVM application of water inrush prediction model, the first analysis of the regional geology research mine, hydrogeological conditions, main control effect of selection of coal seam floor water inrush factors (i.e. aquifuge thickness, pressure, depth of destroyed floor, aquifer and fault throw), and then collect the typical water inrush in working face of historical data, and these data are divided into training set and test set of two parts based on MATLAB2014a experimental platform; Visual C++, with Microsoft compiler to add the Libsvm toolbox in MATLAB software, the programming code of the training set data for training and testing of simulation, the support vector machine is the most Optimal penalty factor C and kernel function parameters were 694.8591 and 317.1063; the test set data into the trained support vector machine model to predict the risk of water inrush in working face, and the prediction results the prediction results of PSO-SVM model and water inrush coefficient method, comparatively analyzed the actual situation. The experimental results show that the precision, and engineering application value of PSO-SVM model is higher in water inrush prediction, which is important to ensure the safety production of coal mine.

【學(xué)位授予單位】：山東科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類(lèi)號(hào)】：TD745.2

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 王愷;關(guān)少卿;汪令祥;王鼎奕;崔W，

本文編號(hào)：1585632