基于BP神经网络的煤与瓦斯突出预测方法的研究  

The Study on the Methods for Predicting Coal or Gas Outburst Based on BP Neural Network

作　　者：田云丽[1] 周利华[1] 

机构地区：[1]湖南科技大学能源与安全工程学院,湖南湘潭411201

出　　处：《系统工程理论与实践》2005年第12期102-106,共5页Systems Engineering-Theory & Practice

基　　金：湖南省自然科学基金(05JJ30081);国家安全生产科技计划(04-232);湖南科技大学博士启动资金(E50335)

摘　　要：为准确预测矿井煤与瓦斯突出的危险性,针对反向BP神经网络收敛差的缺点,分别采用基于MATLAB神经网络工具箱中的VLBP和LMBP算法的改进BP神经网络模型对煤与瓦斯突出的危险性进行了预测.根据煤与瓦斯突出的特点,选取开采深度、瓦斯压力、瓦斯放散初速度、煤的坚固性系数与地质破坏程度等五个关键影响因素作为煤与瓦斯突出的评判指标,建立了煤与瓦斯突出预测的神经网络模型.实际应用效果表明,采用基于MATLAB神经网络工具箱的BP网络模型,能克服一般BP网络收敛较慢的缺点,能加快收敛速度;运用LMBP算法比VLBP算法快,但需较大计算机内存;与常规预测方法相比较,该模型的预测准确性高,能有效地预测煤与瓦斯突出的危险性.For the purpose of predicting the danger of coal and gas outburst in mine coal layer correctly, a kind of modified BP neural network based on the VLBP and LMBP algorithm in MATLAB neural network toolbox was put forth to speed up the network convergence speed in this paper. According to the characteristics of coal and gas outburst, five key influencing factors such as excavation depth, pressure of gas, and geologic destroy degree are selected as the judging indexes of coal and gas outburst. Then the model for predicting coal and gas outburst is built. Practical application demonstrates that the modified BP prediction model based on the MATLAB neural network toolbox can overcome the disadvantages of constringency and has fast convergence speed and good prediction accuracy. The analysis and computing show that the computing speed by LMBP algorithm is faster than by VLBP algorithm but needs more memory. And the results show that the model is a very efficient prediction method for mine coal and gas outburst, compared with the popular predicting methods, and has an important practical meaning for the mine production safety.

关 键 词：BP方法 神经网络 煤与瓦斯突出 MATLAB 预测 

分 类 号：TD752[矿业工程—矿井通风与安全]