基于鲁棒偏最小二乘法的涡扇发动机成本预测  

Cost Prediction of Turbofan Engine Using a Robust Partial Least Squares Method

作　　者：张蕾[1] 周洲[1] 

机构地区：[1]西北工业大学无人机特种技术国防科技重点实验室,陕西西安710072

出　　处：《飞机设计》2012年第1期19-22,共4页Aircraft Design

摘　　要：采用的偏最小二乘法目的是解决数据量较小的问题,特别针对小样本数据缺失和多重共线性的问题。作者提出适用标准PLS算法,以鲁棒的协方差矩阵,用于涡扇发动机成本预测中的应用。结果表明,误差较小,能够满足实际涡扇发动机成本费用的估算。Partial Least squares(PLS) regression is a linear regression technique developed to relate many regressors to one or several response variables.Classical PLS is based on the empirical covariance matrix of the data and hence it is highly sensitive to outlying observations.Robust methods are introduced to reduce or remove the effect of outlying data points.In this paper,several simulation results and analysis of real turbofan engine data set show the effectiveness and robustness of the robust PLS method.

关 键 词：成本预测 鲁棒 偏最小二乘 涡扇发动机 

分 类 号：V23[航空宇航科学与技术—航空宇航推进理论与工程]