基于偏最小二乘回归的装备备件消耗预测  

Spare Parts Consumption Prediction Based on Partial Least-Squares Regression

作　　者：刘浏[1] 罗广旭[1] 魏东涛[1] 刘妍[2] 赵徐成[1] 王保成[1] LIU Liu LUO Guang-xu WEI Dong-tao LIU Yan ZHAO Xu-cheng WANG Bao-cheng(Department of Aviation Four Stations, Air Force Logistics College, Xuzhou 221000, China Department of Basic, Air Force Logistics College, Xuzhou 221000, China)

机构地区：[1]空军勤务学院航空四站系,江苏徐州221000 [2]空军勤务学院基础部,江苏徐州221000

出　　处：《数学的实践与认识》2017年第10期122-126,共5页Mathematics in Practice and Theory

摘　　要：考虑了装备使用时间、行驶里程和配备时间等影响备件消耗的多种因素,依据装备备件的消耗特点,在分析偏最小二乘回归方法原理的基础上,运用方法对小样本数据条件下装备备件的消耗数量进行预测.应用示例表明,偏最小二乘回归方法比传统多元回归分析法、逐步多元回归分析法和删除多元回归分析法具有更高的预测精度.Through making a analysis of the factors which include operation time, actual service life and the age of the equipment and the character of spare parts consumption, the partial least-squares regression is applied to solve the problem of spare parts consumption prediction when the sample is small. The example indicates that partial least-squares regression is much more accurate than traditional multiple linear regression, stepwise multiple linear regression and erasing multiple linear regression.

关 键 词：偏最小二乘回归 小样本 备件消耗 

分 类 号：E911[军事] O212.4[理学—概率论与数理统计]