任务驱动下航材需求量的GA-GM-BP预测

摘要

机务保障影响着航空装备战斗力的生成,机务保障资源是任务成功率的物质支撑.基于航材需求信息的灰色性,通过采用DEMATEL方法提取影响航空备件需求量的关键影响因素,采用遗传算法优化的灰色神经网络对需求量进行了仿真预测,其预测精度较BP神经网络和灰色神经网络都高.该方法对于其他航空机务保障资源的需求预测有借鉴意义.%Flying mission support influence the building of aviation equipment battle effectiveness,flying mission support resource is the substance support for the success of mission.Based on the gray feature of the aviation spare part requirement,DEMATEL method is used to distill the key factor which influence the requirement of aviation spare part,using the GA-GMBP to simulate forecast the spare part quantity,the forecast precision is higher than BP network and gray-BP network.The said method can be used to forecast other kinds of aviation flying mission support resource.