摘要
针对横截面数据的复杂装备研制费用预测问题,研究了GM(0,N)模型的稳定性,证明了当样本较少时,GM(0,N)模型相对稳定。依据所提出的相似度对原始数据排序,在此基础上建立GM(0,N)模型,与待预测对象越相似的样本数据对GM(0,N)模型解的影响越敏感,从敏感性角度说明与待预测对象越相似的样本数据,其影响权重越大。由于相关因素相似的装备往往产生趋同的研制费用,充分利用与待预测对象相似的样本数据,有助于提高预测精度。通过实例说明了本模型的实用性与有效性。
Complicated equipment development cost forecasting of the cross section data is studied,the GM(0,N)model is relatively stable when the sample is small.The data is sorted according to the similarity degree proposed,then GM(0,N)model is established,the more similar to the prediction the object sample data,the more sensitive to GM(0,N)model,the greater weight GM(0,N)model determine from the angle of sensitivity.Due to the similar equipment often produce similar development costs,making full use of the similar sample data to prediction object is helpful to improve the forecasting accuracy.The practical example is given to illustrate the practicability and validity of this model.
作者
吴利丰
于亮
文朝霞
WU Li-feng;YU Liang;WEN Zhao-xia(College of Management Engineering and Business, Hebei University of Engineering, Handan 056038, China;China Academay of Launch Vehicle Technology, Beijing 100076, China)
出处
《中国管理科学》
CSSCI
CSCD
北大核心
2019年第7期203-207,共5页
Chinese Journal of Management Science
基金
国家自然科学基金资助项目(71871084,71401051)
河北省青年拔尖人才项目
河北省高校百名优秀创新人才支持计划项目(SLRC2019001)
关键词
导弹研制费用
灰色模型
横截面数据
相似度
missile development cost
grey model
cross-sectional data
similarity degree
