發(fā)布時間：2018-05-18 04:18

  本文選題：混沌理論 + 相空間重構(gòu)　； 參考：《山西大學》2015年碩士論文

【摘要】：煤炭為我國國民經(jīng)濟的發(fā)展提供了有力的能源支持。但是煤炭生產(chǎn)一直被安全問題所困擾,其中瓦斯事故是威脅我國煤礦井下安全生產(chǎn)的主要災害之一。因此對瓦斯?jié)舛冗M行科學準確的預測具有重要意義。煤礦井下系統(tǒng)受到多種因素的影響,各種因素相互作用形成了具有混沌性質(zhì)的復雜煤巖瓦斯動力系統(tǒng)。因此,可以基于混沌理論建立煤礦井下瓦斯?jié)舛阮A測模型,進而達到瓦斯預警和事故防控的目的。本文首先針對基于混沌理論的最大Lyapunov指數(shù)預測模型存在的符號選擇問題,引入加權(quán)一階局域的思想推導出新的預測公式,并在單瓦斯傳感器數(shù)據(jù)預測中進行模型驗證及分析。然后,針對多傳感器數(shù)據(jù)預測模型存在的多變量選擇問題,引入相關(guān)性分析的方法分析變量之間的相關(guān)性強弱。接著,采用聯(lián)合考慮歐式距離和夾角余弦的方法對基于最大Lyapunov指數(shù)預測模型的鄰近點選擇問題進行改進。最后建立基于多傳感器數(shù)據(jù)的改進最大Lyapunov指數(shù)瓦斯?jié)舛阮A測模型并通過實驗分析和驗證。本文研究內(nèi)容主要有：(1)探討了混沌理論的相空間重構(gòu)技術(shù),并通過參數(shù)相關(guān)法和參數(shù)不相關(guān)法分別對重構(gòu)參數(shù)進行了求取分析；研究了混沌性的識別,特別是通過最大Lyapunov指數(shù)是否大于零來驗證系統(tǒng)的混沌性。(2)通過引入加權(quán)一階局域法的思想,對基于最大Lyapunov旨數(shù)的單瓦斯傳感器數(shù)據(jù)預測模型進行推導,消除預測時符號選擇的問題,并用鹿臺山煤礦的實時瓦斯傳感器數(shù)據(jù)驗證模型。通過與傳統(tǒng)預測模型的對比分析得出,改進模型符號確定且均方根誤差為2.61%較傳統(tǒng)模型4.27%低,改進模型在瓦斯?jié)舛阮A測上較優(yōu)。(3)首先引入相關(guān)性分析法得出對瓦斯影響大的因素作為多變量預測模型的輸入。然后提出考慮歐式距離和夾角余弦的思路對基于最大Lyapunov指數(shù)的多變量預測模型進行改進。接著用采集自霍爾辛赫煤礦的多傳感器數(shù)據(jù)驗證模型,并分別與傳統(tǒng)預測模型和BP神經(jīng)網(wǎng)絡(luò)預測模型進行實驗對比。結(jié)果得出改進模型的預測精度較后兩者模型都有提高,說明改進模型在多變量瓦斯?jié)舛阮A測上是有效的。
[Abstract]:Coal provides powerful energy support for the development of our national economy. However, coal production has always been troubled by safety problems, among which gas accident is one of the main disasters that threaten the safety of underground coal production in China. Therefore, it is of great significance to predict gas concentration scientifically and accurately. The underground coal mine system is influenced by many factors, and various factors interact to form a complex coal-rock gas power system with chaotic properties. Therefore, the gas concentration prediction model can be established based on chaos theory, and the purpose of gas early warning and accident prevention and control can be achieved. In order to solve the problem of symbol selection in the maximum Lyapunov exponent prediction model based on chaos theory, a new prediction formula is derived by introducing the idea of weighted first order local area, and the model verification and analysis are carried out in the prediction of single gas sensor data. Then, aiming at the problem of multivariable selection in multisensor data prediction model, the correlation analysis method is introduced to analyze the correlation between variables. Then, the method of combining Euclidean distance and angle cosine is used to improve the selection of adjacent points based on the maximum Lyapunov exponent prediction model. Finally, an improved maximum Lyapunov exponent gas concentration prediction model based on multi-sensor data is established and verified by experiments. In this paper, the phase space reconstruction technology of chaos theory is discussed, and the parameter correlation method and parameter independent method are used to obtain and analyze the reconstruction parameters, and the identification of chaos is studied. In particular, the chaos of the system is verified by whether the maximum Lyapunov exponent is greater than zero.) by introducing the idea of weighted first order local method, the prediction model of single gas sensor data based on the maximum Lyapunov number is derived. The problem of symbol selection in prediction is eliminated and the real time gas sensor data of Lutaishan Coal Mine is used to verify the model. By comparing with the traditional prediction model, the results show that the root-mean-square error of the improved model is 2.61% lower than that of the traditional model 4.27%. The improved model is superior in predicting gas concentration. Firstly, the correlation analysis method is introduced to obtain the factors that have a great influence on gas concentration as the input of multivariate prediction model. Then the idea of considering the Euclidean distance and the angle cosine is proposed to improve the multivariable prediction model based on the maximum Lyapunov exponent. Then the multi-sensor data collected from Holchingham coal mine are verified and compared with the traditional prediction model and BP neural network model. The results show that the prediction accuracy of the improved model is higher than that of the latter two models, which shows that the improved model is effective in predicting multivariable gas concentration.
【學位授予單位】：山西大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TD712

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本文編號：1904334