基于 FCE-PCA-RF 的战时设备损坏测量方法

IF 1.9 3区 计算机科学 Q3 AUTOMATION & CONTROL SYSTEMS
Mingyu Li, Lu Gao, Hongwei Xu, Kai Li, Yisong Huang
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引用次数: 0

摘要

装备维修保障作为装备持续运行、反复作业的 "发动机",在对称作战体系对抗中的作用更加突出。作为计划和实施装备维修任务的依据和指南,装备损伤测量是有效实施维修保障的重要保障。本文首先从战时装备损伤测量的基本问题出发,从敌方属性、我方属性和战场环境三方面全面分析了损伤测量的影响因素。其次,本文在模糊综合评价(FCE)的基础上确定了关键因素,并对关键因素进行了主成分分析(PCA)。最后,将代表 85% 以上数据特征的主成分作为输入,将设备损坏量作为输出。通过人工神经网络(ANN)和随机森林(RF)对数据进行训练和测试。总之,FCE-PCA-RF 可作为战时装备损伤估算研究的参考。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Equipment Damage Measurement Method of Wartime Based on FCE-PCA-RF
As the “engine” of equipment continuous operation and repeated operation, equipment maintenance support plays a more prominent role in the confrontation of symmetrical combat systems. As the basis and guide for the planning and implementation of equipment maintenance tasks, the equipment damage measurement is an important guarantee for the effective implementation of maintenance support. Firstly,this article comprehensively analyses the influence factors to damage measurement from the enemy's attributes, our attributes and the battlefield environment starting from the basic problem of wartime equipment damage measurement. Secondly, this article determines the key factors based on fuzzy comprehensive evaluation (FCE) and performed principal component analysis (PCA) on the key factors. Finally, the principal components representing more than 85% of the data features are taken as the input and the equipment damage quantity is taken as the output. The data are trained and tested by artificial neural network (ANN) and random forest (RF). In a word, FCE-PCA-RF can be used as a reference for the research of equipment damage estimation in wartime.
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来源期刊
Journal of Systems Engineering and Electronics
Journal of Systems Engineering and Electronics 工程技术-工程:电子与电气
CiteScore
4.10
自引率
14.30%
发文量
131
审稿时长
7.5 months
期刊介绍: Information not localized
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