【作者】 黄光球； 陆秋琴；

【Author】 Huang Guangqiu Lu Qiuqin (Xian University of Architecture & Technology)

【机构】 西安建筑科技大学；

【摘要】 提出基于粗神经网络确定影响矿内通风风流稳定性主要风路的方法。该方法首先应用粗糙集理论来选择影响风流稳定性的重要因素。然后构造确定影响矿内通风风流稳定性主要风路BP神经网络模型,根据重要因素采集生产实际数据,对神经网络模型进行训练,从而确定影响矿内通风风流稳定性的重要因素与巷道的风量之间的非线性映射关系。最后用神经网络计算主要因素的变化对巷道风量的影响,通过风量变化分析确定影响矿内通风风流稳定性主要风路。更多还原

【Abstract】 A rough neural network - based approach to determining the main airways affecting the stability of airflow in mine ventilation is put forward. It selects the main factors that affect the airflow stability by rough set theory. Then model based on BP neural network for determining the main airways affecting the stability of airflow in mine ventilation is constructed. The nonlinear mapping relation between the important factors affecting the stability of airflow and air volume in mine ventilation is determined by training BP neural network model with data practically collected in production. The main airways affecting the airflow stability in mine ventilation are determined by analyzing the effect of the change in the main factors on the air volume of tunnels and the change in air volume. An approach to determine main affecting tunnels on the stability of mine ventilation based on rough neural network is put forward. The main affecting factors on the stability of mine ventilation are gained by rough set theory. The model of determination main affecting tunnels on the stability of mine ventilation based on BP neural network is constructed. The nonlinear relationship between the main affecting factors and the quantity of tunnel ventilation is determined by training BP neural network with data. The main affecting tunnels on the stability of mine ventilation are calculated by analyzing the changing of the main affecting factors on the quantity of tunnel ventilation by BP neural network.更多还原