系统仿真学报 ›› 2016, Vol. 28 ›› Issue (2): 416-424.

• 仿真应用工程 • 上一篇    下一篇

基于灰色聚类和层次分析的模拟训练成绩评定

武兆斌1,2, 陈黎3, 赵春霞1   

  1. 1.南京理工大学计算机科学与技术,南京 210094;
    2.63961部队,北京,100012;
    3.北京电子工程总体研究所,北京 100854
  • 收稿日期:2014-10-21 修回日期:2014-11-29 出版日期:2016-02-08 发布日期:2020-08-17
  • 作者简介:武兆斌(1973-),男,山西灵丘,博士,高工,研究方向为地空导弹总体技术;陈黎(1981-),男,湖南长沙,博士,高工,研究方向指控系统总体设计;赵春霞(1964-),女,北京,博导,教授,研究方向计算机应用技术以及模式识别与智能系统。

Evaluation Method of Training Simulation Result Based on Grey Clustering and Analytic Hierarchy Process

Wu Zhaobin1,2, Chen Li3, Zhao Chunxia1   

  1. 1. Computer Department of Nanjing University of Science and Technology, Nanjing 210094, China;
    2. Unit 63961 PLA, Beijing 100012, China;
    3. Beijing Institute of Electronic System Engineering, Beijing 100854, China
  • Received:2014-10-21 Revised:2014-11-29 Online:2016-02-08 Published:2020-08-17

摘要: 为提高便携式防空导弹模拟训练成绩评定的合理性,给出一种基于灰色聚类和层次分析的综合评定方法。从准确性、快速性和平稳性3个方面建立了便携式防空导弹模拟训练成绩的评价指标体系,给出各指标的定义和计算公式;利用多专家层次分析法,计算了各指标的评价权重,进一步通过0-1法和灰色聚类评估法得到了各指标的评分,并综合各指标的评价权重对射手的训练成绩进行了综合评定;最后以某型便携式防空导弹模拟训练系统训练成绩评定为例说明了成绩评定方法的合理性。

关键词: 模拟训练, 评价指标, 灰色聚类, 层次分析

Abstract: An evaluation method based on grey relational analytic hierarchy process was proposed in order to improve rationality of evaluation training result of portable anti-air missile training simulation system. The evaluating indicators including definitions and calculation equations were proposed from three aspects including accuracy, rapidity and stationarity. The weights of the evaluating indicators were calculated by analytic hierarchy process of multiple experts, and the scores of the evaluating indicators were given by 0-1 process and grey clustering evaluation, and using the weights and scores of the evaluating indicators the training score could be obtained. An example of evaluation training result of a certain type portable anti-air missile training simulation system was given, which shows the rationality and the superiority of the proposed evaluation method.

Key words: training simulation, evaluating indicator, grey clustering, analytic hierarchy process

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