神经网络测控露天煤矿220 t级卡车油耗研究  

Study on oil consumption of 220 t truck in open-pit coal mine by neural network

作　　者：冀盛全 王跃旭 JI Shengquan;WANG Yuexu(Electromechanical Information Department of Shenhua Beidian Shengli Energy Co.,Ltd.,Xilinhot 026000,China)

机构地区：[1]神华北电胜利能源有限公司机电信息部,内蒙古区锡林浩特026000

出　　处：《露天采矿技术》2018年第5期81-85,共5页Opencast Mining Technology

摘　　要：通过Particle Swarm Optimization,PSO全面优化BP神经网络的偏置与权值。同时利用优化过的BP神经网络创建胜利煤矿设备油耗的测控网络模型,将该矿影响设备油耗的复杂因素与繁琐的设备油耗数值进行完美的拟合。经过仿真后的结论说明:创建的模型具备较好的稳定性,测控准确度较高等性能,非常适合进行该煤矿设备油耗的测控工作,能够很好的指导该矿设备的科学管控,降低该矿生产成本投入。Through Particle Swarm optimization, PSO comprehensively optimizes the bias and weight of BP neural network. At the same time, the company uses the optimized BP neural network to create a measm＇enlent and control network model of the equipment oil consumption in Shengli Coal Mine. The complicated factors affecting the equipment oil consumption and the complicated equipment consumption can be perfectly matched. The simulation results show that the model has good stability, high accuracy and high accuracy, which is very suitable for the measurement and control of the oil consumption of the coal mine equipment, and can well guide the scientific control and management of the mine equipment and reduce the production cost input of the mine.

关 键 词：设备油耗 系统仿真 神经网络 测控 优化算法 

分 类 号：TD57[矿业工程—矿山机电]