【摘要】：我國煤炭產量居世界首位,煤礦安全事故也頻頻發(fā)生,傷亡人數僅排在因交通事故傷亡之后。隨著開采深度地不斷加深,生產能力地提高,地質條件也更加復雜化,煤礦安全工作面臨著巨大的挑戰(zhàn)。瓦斯事故又是煤礦生產過程中的主要不安全因素,如何能準確快速地預測出瓦斯涌出量,對于瓦斯防治措施的制定有著積極意義。 本文從開采因素和自然因素兩個方面分別分析煤層的瓦斯含量、埋深、瓦斯壓力、大氣壓力、風量、產能等方面對瓦斯涌出量的影響,指出傳統(tǒng)預測瓦斯涌出量方法的局限性,不能將瓦斯涌出量與各個影響因素之間復雜的非線性關系清楚地表述。RBF神經網絡自身的容錯性和自適應性以及較強的非線性函數逼近能力,則能很好地克服這些缺點。 RBF神經網絡具有搜索全局最優(yōu)解和最佳逼近能力,其拓撲結構、隱節(jié)點數目、中心位置、寬度和權值是決定整個網絡性能的關鍵因素。遺傳算法作為一種全局優(yōu)化算法,具有強魯棒性,適用于解決訓練速度慢、易陷入局部極小值等缺點的網絡結構,自適應調整交叉概率和變異概率,能夠避免重復搜索,并提高搜索效率。本文提出采用遺傳算法優(yōu)化RBF神經網絡中的隱節(jié)點數目、中心位置、寬度和權值,有效地彌補RBF神經網絡的不足,最后利用Matlab軟件編程實現GA-RBF神經網絡模型,應用此模型分別對兩個采煤工作面進行瓦斯涌出量預測,得到了令人滿意結果。
[Abstract]:China's coal production ranks first in the world, coal mine safety accidents also occur frequently, the number of casualties only ranks behind traffic accidents. With the deepening of mining depth, the improvement of production capacity and the complication of geological conditions, the work of coal mine safety is facing great challenges. Gas accident is also the main unsafe factor in coal mine production. How to predict gas emission accurately and quickly is of positive significance to the formulation of gas prevention and control measures. This paper analyzes the influence of gas content, buried depth, gas pressure, atmospheric pressure, air volume and productivity on gas emission from two aspects of mining factors and natural factors, respectively, and points out the limitations of traditional methods for predicting gas emission. The complex nonlinear relationship between the amount of gas emission and the influence factors can not be clearly expressed. The fault tolerance and adaptability of RBF neural network and its strong nonlinear function approximation ability can not be clearly expressed. RBF neural network has the ability to search for global optimal solution and best approximation. Its topology, number of hidden nodes, center position, width and weight are the key factors to determine the network performance. As a global optimization algorithm, genetic algorithm (GA) has strong robustness, which is suitable for solving the problems of slow training speed, easy to fall into local minimum and other shortcomings of network structure, adaptively adjusts crossover probability and mutation probability, and can avoid repeated search. And improve the search efficiency. In this paper, genetic algorithm is proposed to optimize the number, center position, width and weight of hidden nodes in RBF neural network, which can effectively compensate for the deficiency of RBF neural network. Finally, the GA-RBF neural network model is realized by using Matlab software programming. The model is used to predict the gas emission in two coal mining faces, and satisfactory results are obtained.
【學位授予單位】：安徽理工大學
【學位級別】：碩士
【學位授予年份】：2013
【分類號】：TD712.5

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