Key indexes identifying approach of weapon equipment system-of-systems effectiveness integrating Bayes method and dynamic grey incidence analysis model  

作　　者：ZHANG Jingru FANG Zhigeng YE Feng CHEN Ding 

机构地区：[1]College of Economics and Management,Nanjing University of Aeronautics and Astronautics,Nanjing 210006,China [2]National Key Laboratory for Complex Systems Simulation,Institute of System Engineering,Academy of Military Science,Beijing 100020,China [3]Shanghai Electro-Mechanical Engineering Institute,Shanghai 201109,China

出　　处：《Journal of Systems Engineering and Electronics》2024年第6期1482-1490,共9页系统工程与电子技术(英文版)

基　　金：supported by the National Natural Science Foundation of China(72271124,72071111).

摘　　要：Aiming at the characteristics of multi-stage and(extremely)small samples of the identification problem of key effectiveness indexes of weapon equipment system-of-systems(WESoS),a Bayesian intelligent identification and inference model for system effectiveness assessment indexes based on dynamic grey incidence is proposed.The method uses multi-layer Bayesian techniques,makes full use of historical statistics and empirical information,and determines the Bayesian estima-tion of the incidence degree of indexes,which effectively solves the difficulties of small sample size of effectiveness indexes and difficulty in obtaining incidence rules between indexes.Sec-ondly,The method quantifies the incidence relationship between evaluation indexes and combat effectiveness based on Bayesian posterior grey incidence,and then identifies key system effec-tiveness evaluation indexes.Finally,the proposed method is applied to a case of screening key effectiveness indexes of a missile defensive system,and the analysis results show that the proposed method can fuse multi-moment information and extract multi-stage key indexes,and has good data extraction capability in the case of small samples.

关 键 词：weapon equipment system-of-systems(WESoS) effectiveness index system effectiveness key index Bayes theo-rem grey incidence analysis (extremely)small samples 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]