航空工程进展2025，Vol.16Issue(4)：74-81,8.DOI:10.16615/j.cnki.1674-8190.2025.04.07
基于灰色关联分析和XGBoost的飞机飞行品质评价
Aircraft flight quality evaluation based on grey correlation analysis and XGBoost
摘要
Abstract
Aircraft flight quality assessment is a critical process for evaluating the training effects and improving the training standards of pilots.Traditional evaluation methods rely heavily on the subjective scoring by flight instruc-tors,which suffer from subjectivity and limited accuracy.To enhance the objectivity and precision of flight quality assessment,this paper introduces a novel evaluation method that integrates grey correlation analysis(GCA)with the XGBoost algorithm.GCA is utilized to identify flight parameters closely related to flight quality,while the XG-Boost algorithm is employed to construct a flight quality assessment model.The high accuracy of the proposed method is verified through the evaluation of actual flight training data.The study demonstrates that the method can effectively enhance the scientific and precise nature of flight quality assessment,providing robust technical support for pilot training.关键词
灰色关联分析方法/品质评估模型/XGBoost算法/机器学习Key words
grey correlation analysis method/quality assessment model/XGBoost(eXtreme Gradient Boosting)algorithm/machine learning分类
航空航天引用本文复制引用
孙宝嵩,石治国,潘新龙,颜廷龙,王非凡..基于灰色关联分析和XGBoost的飞机飞行品质评价[J].航空工程进展,2025,16(4):74-81,8.基金项目
国家自然科学基金(62076249) （62076249）
山东省自然科学基金(ZR2020MF154) （ZR2020MF154）
