發(fā)布時(shí)間：2018-05-16 05:57

  本文選題：煤礦火災(zāi)預(yù)測(cè) + 模糊C均值聚類　； 參考：《遼寧工程技術(shù)大學(xué)》2017年碩士論文

【摘要】：煤礦火災(zāi)是煤炭行業(yè)發(fā)展的攔路石,對(duì)煤礦火災(zāi)預(yù)測(cè)的研究有重大的意義。依照傳統(tǒng)預(yù)測(cè)方法對(duì)單一的火災(zāi)因素的檢測(cè)已經(jīng)不能滿足復(fù)雜的井下狀況,本文對(duì)各種因素與火災(zāi)的聯(lián)系進(jìn)行分析,并分析了火災(zāi)的幾種燃燒階段的特點(diǎn),提出了火災(zāi)預(yù)測(cè)的特征。文中分析了井下火災(zāi)預(yù)測(cè)的控制技術(shù),通過使用井下救災(zāi)自動(dòng)風(fēng)門技術(shù),在礦井火災(zāi)災(zāi)變時(shí)期可以實(shí)現(xiàn)對(duì)風(fēng)門遠(yuǎn)程控制從而杜絕大量有毒有害氣體向其他工作地點(diǎn)侵入和蔓延。文中首先采用模糊C均值聚類算法(FCM)對(duì)井下火災(zāi)數(shù)據(jù)進(jìn)行處理,并將結(jié)果與樣本進(jìn)行對(duì)比,得出了FCM聚類的正確率,并且對(duì)FCM聚類算法應(yīng)用于井下火災(zāi)預(yù)測(cè)的優(yōu)缺點(diǎn)進(jìn)行分析。針對(duì)模糊C均值算法的缺點(diǎn),采用人工魚群算法(AFSA)對(duì)其進(jìn)行優(yōu)化,對(duì)火災(zāi)樣本進(jìn)行預(yù)測(cè),得出預(yù)測(cè)結(jié)果和目標(biāo)函數(shù)的收斂過程,并與FCM聚類算法和應(yīng)用較廣的bp神經(jīng)網(wǎng)絡(luò)分類算法進(jìn)行對(duì)比,預(yù)測(cè)準(zhǔn)確性較未優(yōu)化的FCM算法更好,而且不會(huì)像bp神經(jīng)網(wǎng)絡(luò)一樣在小樣本數(shù)量的情況下,準(zhǔn)確率受到過大的影響。從對(duì)比中可以看出,AFSA-FCM算法在火災(zāi)預(yù)測(cè)上更有優(yōu)勢(shì),
[Abstract]:Coal mine fire is the block stone for the development of coal industry, it is of great significance to the study of coal mine fire prediction. According to the traditional prediction method, the detection of a single fire factor can not meet the complicated underground conditions. This paper analyses the relationship between various factors and fire, and analyses the characteristics of several combustion stages of the fire, and puts forward the characteristics of the fire. This paper analyzes the characteristics of the fire prediction. In this paper, the control technology of the underground fire prediction is analyzed. By using the automatic ventilation door technology in the downhole disaster relief, the remote control of the air door can be realized in the time of the mine fire disaster to eliminate the intrusion and spread of a large number of poisonous and harmful gases to other working places. To deal with the downhole fire data and compare the results with the sample, the correct rate of FCM clustering is obtained, and the advantages and disadvantages of the FCM clustering algorithm applied to the downhole fire prediction are analyzed. In view of the shortcomings of the fuzzy C mean algorithm, the artificial fish swarm algorithm (AFSA) is used to optimize it, predict the fire samples, and get the prediction. The convergence process of the result and the target function is compared with the FCM clustering algorithm and the widely used BP neural network classification algorithm. The accuracy of the prediction is better than that of the FCM algorithm which is not optimized, and the accuracy rate is not greatly influenced by the small sample size of the BP neural network. The AFSA-FCM algorithm can be seen from the comparison. More advantageous in fire prediction.

【學(xué)位授予單位】：遼寧工程技術(shù)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TP311.13;TP18;TD752

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本文編號(hào)：1895727