Construction mechanism of whitenization weight function and its application in grey clustering evaluation

作     者：XIE Naiming SU Bentao CHEN Nanlei 

作者机构：College of Economics and Management Nanjing University of Aeronautics and Astronautics 

出 版 物：《Journal of Systems Engineering and Electronics》 (系统工程与电子技术（英文版）)

年 卷 期：2019年第30卷第1期

页      面：121-131页

核心收录：

学科分类：07[理学] 08[工学] 

基　　金：supported by the National Natural Science Foundation of China(71671090) the Aeronautical Science Foundation of China(2016ZG52068) the Liberal Arts and Social Sciences Foundation of the Ministry of Education(MOE)in China(15YJCZH189) the Qinglan Project for Excellent Youth or Middle-aged Academic Leaders in Jiangsu Province 

主　　题：whitenization weight function grey system theory grey clustering evaluation. 

摘      要：The clustering evaluation can be used to scientifically classify the objects to be evaluated according to the information aggregation of various evaluation rules. In grey weighted clustering evaluation, the index clustering rule relies on the construction of the whitenization weight function, while the existing construction method of the linear function lacks the construction mechanism analysis and validity explanation. A normative construction principle is put forward by analyzing the construction mechanism of the function. Through proving the normative principle of the function,the basic modal function(BMF) is proposed and characterized by different function forms. Then, a new type of the whitenization weight function and its grey clustering evaluation model algorithm are given by studying the mechanism and nature of the construction of different forms of the function. Finally, the comparative study for self-innovation capability of defense science and technology industry(DSTI) is taken as an example. The results show that the different construction ways of the function have an effect on the clustering result. The proposed construction mechanism can better explain the index clustering rules and evaluation effectiveness,which will perfect the theoretical system of grey clustering evaluation and be applied to practice effectively.