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基于PLSR-CER模型的大飞机成本风险控制
引用本文:姜鹏. 基于PLSR-CER模型的大飞机成本风险控制[J]. 南京航空航天大学学报, 2012, 44(3): 425-430
作者姓名:姜鹏
作者单位:1. 北京航空航天大学经济管理学院 北京 100191
2. 北京航空航天大学经济管理学院 北京 100191;中航发动机有限责任公司 北京 100028
摘    要:大型飞机的研制往往存在多种难以预见的不确定性因素,而且目前国内大型客机研制经验缺乏,相关的性能参数和研制成本的历史数据稀少,并存在多重相关性等问题,因此,文中引入了偏最小二乘回归方法,以客机的机体特征及性能特征作为多因变量,建立了一种多因变量对单一自变量的成本预测回归模型,并将部分客机性能参数和成本数据导入成本预测回归模型进行验证。实例分析表明,基于偏最小二乘回归法(Partial least-squares reqression,PLSR)构建的费用估算关系(Cost estimating relationship,CER)模型相对传统预测模型有很大的改善,预测的精度更高,更能体现客机研制成本与前期的性能要求之间的关系。

关 键 词:费用关系估算式  偏最小二乘  多元回归  成本风险

PLSR-CER Cost Risk Control Model of Large-Size Airplane
Jiang Peng , Guo Tongxiu , Meng Deyun , Hao Yi. PLSR-CER Cost Risk Control Model of Large-Size Airplane[J]. Journal of Nanjing University of Aeronautics & Astronautics, 2012, 44(3): 425-430
Authors:Jiang Peng    Guo Tongxiu    Meng Deyun    Hao Yi
Affiliation:1(1.School of Economics and Management,Beijing University of Aeronautics & Astronautics,Beijing,100191,China;2.Engine Co.Ltd.,Aviation Industry Corporation of China,Beijing,100028,China)
Abstract:The development of large-size airplane often bring a variety of unforeseen uncertainties.Partial least-squares regression(PLSR) method is introduced to aircrafts in third generation for the lack of parameters of the performance and the cost data in relevant.The existence of multiple correlation between the values are observed.A single independent variable regression model for prediction of cost is established with aircraft characteristics and performance characteristics as the multi-dependent variables.And then the relevant data published by the Boeing and Airbus is introduced into the prediction model for the empirical study.Case analysis shows that,compared with the traditional prediction model,the cost estimating relationship(CER) model constructed based on PLSR improves the forecast accuracy,and reflects relationship between the research and development(R&D) costs and the previous requirements.
Keywords:cost estimating relationship(CER)  partial least-squares regression(PLSR)  multiple regression  cost risk
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