A new method to evaluate whether the data are suitable to GM model or not
A new method to evaluate whether the data are suitable to GM model or not
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DOI：
10.1108/03684920910976952
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发表时间：
2009-08
期刊：
影响因子：
2.5
通讯作者：
Yong Wei;Dahong Hu
中科院分区：
文献类型：
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作者：
Yong Wei;Dahong Hu
目的 – 本文的目的是介绍新的类比离散度、新的平滑度序列和新平滑度的比较准则，并提出灰色建模的新的先验检验，以满足具有白指数重合定律的优化灰色模型的建模需求。设计/方法/途径 – 类比传统的类比离散度、平滑度序列和比较准则，引入相应的新概念和新的比较准则，可以反映原始数据的接近程度和正常的几何级数。 – 对于优化后的灰色模型，新概念和新比较准则可以看作是灰色建模的先验检验。 原创性/价值 – 首先，新概念和新比较准则能够反映原始数据与正常几何级数的接近程度，本文提出了灰色建模对优化灰色模型的先验检验。
Purpose – The purpose of this paper is to introduce the new class ratio dispersion, the new smooth degree sequence and the comparison criterion of the new smooth degree and to propose the new prior check of grey modeling in order to meet the modeling demand of the optimized grey models which have the white exponential law of coincidence.Design/methodology/approach – This paper introduces the corresponding new concepts and new comparison criterion which can reflect the approach degree of the raw data and the normal geometric progression by analogy with the traditional class ratio dispersion, smooth degree sequence and comparison criterion.Findings – To the optimized grey models, the new concepts and the new comparison criterion can be regarded as the prior check of grey modeling.Originality/value – First, the new concepts and the new comparison criterion can reflect the approach degree of the raw data and the normal geometric progression, and this paper proposes the prior check of grey modeling to the opti...