系统仿真学报 ›› 2017, Vol. 29 ›› Issue (10): 2373-2383.doi: 10.16182/j.issn1004731x.joss.201710019

• 仿真系统与技术 • 上一篇    下一篇

基于融合流形学习的体系多维效能指标可视化

丁剑飞1, 司光亚1, 石亚峰2, 刘懿3   

  1. 1.国防大学信息作战与指挥训练教研部,北京 100091;
    2.喀什大学数学与统计学院,喀什 844000;
    3.31002部队,北京 100091
  • 收稿日期:2017-05-20 发布日期:2020-06-04
  • 作者简介:丁剑飞(1980-),男,山东济南,博士生,讲师,研究方向为武器装备体系效能评估;司光亚(1967-),男,河南柘城,博士,教授,博导,研究方向为战争模拟。
  • 基金资助:
    国家自然科学军民共用重大研究计划联合基金(U1435218)

Visualization of Multi-dimensional Effectiveness Indicator of Weapon System of SystemsBased on Fusion Manifold Learning

Ding Jianfei1, Si Guangya1, Shi Yafeng2, Liu Yi3   

  1. 1. Department of Information Operation & Command Training, NDU of PLA, Beijing 100091, China;
    2. College of mathematics and statistics, University of Kashi, Kashi 844000, China;
    3. Troops 31002 of PLA, Beijing 100091, China
  • Received:2017-05-20 Published:2020-06-04

摘要: 依托可视化方法展示武器装备体系仿真试验的各类效能指标信息,是武器装备体系建设规划方案综合特征对比的重要手段。提出了一种基于融合流形学习的体系多维效能指标可视化方法,将指标项和方案项由高维空间融合投影到同一二维空间,利用密度计算求得指标对多方案样本的影响程度,通过辐射计算实现最终的多维效能指标可视化效果,运用两组实验,验证了该方法融合映射的正确性,并展现了其在体系效能评估中的应用效果,证明了其可行性。

关键词: 可视化分析, 多维指标, 流形学习, 融合映射

Abstract: It is an important way to compare the comprehensive characteristics of weapon system of systems (WSoS) by means of visualization method to display the various effectiveness indicators information of WSoS construction planning scheme in simulation test. A visualization method for multi-dimensional effectiveness indicator was proposed based on fusion manifold learning. The indicator items and scheme items were converged from high-dimensional space to the same two-dimensional space. The density calculation was used to calculate the effect degree between the multidimensional indicator and scheme. The final multidimensional indicator visualization effect was achieved by radiological calculation. Two sets of experiments were used to verify the correctness of the proposed method, its application effect in the WSoS effectiveness evaluation was shown and feasibility was proved.

Key words: visual analysis, multi-dimensional indicators, manifold learning, fusion mapping

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