摘要
随着矿井开采深度的增加,矿井井底风流温度的预测分析对矿井生产具有重要意义。通过分析影响井底风温的主要因素:地面大气压力、入风温度、入风含湿量以及井筒深度,建立了一种新的T-S模糊神经网络模型,利用MATLAB模拟实现了对井筒的井底风温预测分析。通过实例验证了该方法的可行性,结果表明该方法相比BP神经网络收敛速度快,预测精度高,拟合能力强,符合现场工程应用的需要。
With the increasing depth of coal mining,airflow temperature prediction and analysis of mine bottom is important for mine production. Through the analysis of main factors affecting the bottom temperature： surface air pressure,enters air temperature,air humidity and borehole depth,A new model of T-S fuzzy neural network is established to predict the air temperature of mine bottom by using MATLAB. The feasibility of this method is verified,compared to BP neural networks,this method has the merits of rapid convergence,high precision and good fitting capability,which meets the needs of field applications.
出处
《世界科技研究与发展》
CSCD
2015年第3期226-229,共4页
World Sci-Tech R&D
基金
国家自然科学基金(51274115)资助
关键词
T-S模糊神经网络
淋水井筒
井底风温
预测分析
T-S fuzzy neural network wellbore with spay water air temperature of the bottom hole prediction analysis
