本文選題：瓦斯預(yù)測　切入點：支持向量機　出處：《西安科技大學(xué)》2017年碩士論文

【摘要】：煤炭占我國一次能源消費比例最重,是我國的主要能源,在我國有著重要的戰(zhàn)略地位,因此煤礦安全生產(chǎn)是煤礦的一個重大問題。但是我國煤炭地質(zhì)構(gòu)造復(fù)雜,煤層瓦斯含量大,煤礦安全事故發(fā)生率遠(yuǎn)遠(yuǎn)高于世界主要產(chǎn)煤國家,其中瓦斯災(zāi)害事故發(fā)生頻率最高,傷害最大。因此,瓦斯?jié)舛鹊念A(yù)測對于煤礦生產(chǎn)安全和職工人身安全意義重大。本文以煤礦安全生產(chǎn)為目的,以現(xiàn)有瓦斯監(jiān)測技術(shù)為基礎(chǔ),結(jié)合煤礦井下實際,提出基于支持向量機的瓦斯數(shù)據(jù)去噪算法和基于免疫遺傳BP神經(jīng)網(wǎng)絡(luò)的瓦斯?jié)舛葦?shù)據(jù)預(yù)測算法,對井下采集瓦斯?jié)舛葦?shù)據(jù)進(jìn)行去噪和預(yù)測研究。本文主要研究工作如下:首先,分析煤礦實際生產(chǎn)中井下瓦斯數(shù)據(jù)的特點,得出其受井下復(fù)雜環(huán)境影響普遍含有噪聲,因此提出基于最小二乘支持向量機的瓦斯數(shù)據(jù)去噪算法,對采集到的瓦斯數(shù)據(jù)進(jìn)行處理,通過對瓦斯數(shù)據(jù)的仿真實驗,驗證所提算法的有效性。其次,針對實際井下瓦斯?jié)舛阮A(yù)測不足的問題,提出基于免疫遺傳BP神經(jīng)網(wǎng)絡(luò)對瓦斯?jié)舛阮A(yù)測的算法。針對BP神經(jīng)網(wǎng)絡(luò)結(jié)構(gòu)難以確定的問題,結(jié)合井下瓦斯?jié)舛葦?shù)據(jù)的特點,提出以相空間重構(gòu)理論為依據(jù)的解決方法,通過求取最佳嵌入維數(shù)m,確定了BP神經(jīng)網(wǎng)絡(luò)的結(jié)構(gòu);針對BP神經(jīng)網(wǎng)絡(luò)存在收斂速度慢及易困入局部極值的缺陷,提出基于免疫遺傳理論的優(yōu)化算法,將BP神經(jīng)網(wǎng)絡(luò)權(quán)值和閾值作為待求解問題(抗原),產(chǎn)生初始抗體種群,通過引入免疫遺傳機制,提高算法運行效率,來克服BP神經(jīng)網(wǎng)絡(luò)易困入局部極值的缺陷。通過對煤礦井下瓦斯?jié)舛葦?shù)據(jù)的仿真實驗,驗證所提算法的有效性。最后,將本文算法分別應(yīng)用于搭建的采煤工作面瓦斯采集系統(tǒng)和本校的煤礦瓦斯監(jiān)測系統(tǒng),對算法的實用性進(jìn)行驗證。此外,還研究井下人員定位和無線通訊系統(tǒng),以滿足煤炭生產(chǎn)的需要。
[Abstract]:Coal accounts for the heaviest proportion of primary energy consumption in China and is the main energy in our country, and has an important strategic position in our country. Therefore, the safety of coal production is a major problem in coal mine. However, the geological structure of coal in China is complex. The coal seam gas content is large, the coal mine safety accident rate is far higher than the world main coal producing country, the gas disaster accident occurrence frequency is the highest, the harm is biggest. The prediction of gas concentration is of great significance to the safety of coal mine production and the personal safety of workers. This paper proposes a gas data denoising algorithm based on support vector machine and a gas concentration prediction algorithm based on immune genetic BP neural network. Based on the analysis of the characteristics of underground gas data in coal mine production, it is found that there is generally noise in the underground gas data under the influence of complex underground environment. Therefore, a gas data de-noising algorithm based on least square support vector machine is proposed to deal with the collected gas data. Through the simulation experiment of gas data, the validity of the proposed algorithm is verified. Secondly, aiming at the problem of insufficient gas concentration prediction in actual underground, This paper presents an algorithm for predicting gas concentration based on immune genetic BP neural network. In view of the problem that BP neural network structure is difficult to determine and considering the characteristics of underground gas concentration data, a solution based on phase space reconstruction theory is proposed. The structure of BP neural network is determined by obtaining the best embedding dimension m, and the optimization algorithm based on immune genetic theory is proposed to solve the problems of slow convergence speed and easy entrapment in local extremum of BP neural network. The weight and threshold of BP neural network are taken as the problem to be solved (antigen), and the immune genetic mechanism is introduced to improve the efficiency of the algorithm. To overcome the defect that BP neural network is easily trapped into the local extremum. Through the simulation experiment of gas concentration data in coal mine, the validity of the proposed algorithm is verified. Finally, The algorithm is applied to the coal face gas collection system and the coal mine gas monitoring system, and the practicability of the algorithm is verified. In addition, the underground personnel positioning and wireless communication system are also studied. To meet the needs of coal production.
【學(xué)位授予單位】：西安科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TP18;TD712

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本文編號：1691465