摘要
提出了确定影响矿井通风风流稳定性主要风路的新方法。首先构造影响矿井风流稳定性的主要风路的RBF神经网络模型,然后用生产实际数据对神经网络模型进行训练,从而确定矿井通风系统巷道的风阻与风量之间的非线性映射关系,最后用神经网络计算巷道风阻值的变化对巷道风量的影响,通过风量变化分析确定影响矿井风流稳定性的主要风路。
An approach to determine main ari flow affecting the stability of tunnels ventilation based on Radial Basis Function (RBF)neural network is put forward. The model of the main air flow affecting stability of tunnels ventilation based on RBF neural network is constructed. The nonlinear relationship between the resistance of ventilation system and the quantity of tunnel air flow by training RBF neural network is determined. The main air flow affecting the stability of tunnels ventilation are calculated by calculating the RBF neural network with data.
出处
《化工矿物与加工》
CAS
北大核心
2004年第7期21-23,共3页
Industrial Minerals & Processing
基金
陕西省教育厅自然科学专项基金资助项目(编号03JK148
01JK124)
关键词
通风稳定性
神经网络
主要风路
stability of mine ventilation, Neural Network, main air flow
