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袁伟良,卢朝阳,卢卫,等. 基于机器学习的飞行品质评估[J]. 科学技术与工程, 2021, 21(19): 8262-8269.
Yuan Weiliang,Lu Chaoyang,Lu Wei,et al.Research on Flight Quality Evaluation Based on Machine Learning[J].Science Technology and Engineering,2021,21(19):8262-8269.
基于机器学习的飞行品质评估
Research on Flight Quality Evaluation Based on Machine Learning
投稿时间:2020-11-13  修订日期:2021-04-15
DOI:
中文关键词:  飞行品质  QAR  主成分分析  支持向量机  随机权重粒子群
英文关键词:flight quality  QAR  principal component analysis  support vector machine  random weighted particle swarm
基金项目:空中交通管理系统与技术国家重点实验室开放基金
           
作者单位
袁伟良 南京航空航天大学
卢朝阳 南京航空航天大学
卢卫 中国东方航空江苏有限公司飞行部
何庶 南京航空航天大学
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中文摘要:
      民航事业的快速发展,航空事故的频繁发生,使得航空运输安全成为人们关注的焦点。本文提出了一种基于PCA-PSO-SVM的飞行品质评估方法,能够有效对飞行品质做出客观评价。首先,根据QAR数据提取起飞爬升和进近着陆阶段飞行品质评价指标,采用PCA综合评价对飞行品质进行评估;然后,将评估结果作为SVM的输入,通过PSO算法优化SVM参数;最后,通过训练PSO-SVM模型实现机器学习算法对飞行品质进行评估。测试结果表明,飞行品质分类评估准确率达90%。因此,该方法能够客观有效对飞行品质进行评估,有利于提升飞行品质,提高飞行安全。
英文摘要:
      Due to the rapid development of civil aviation and the frequent occurrence of aviation accidents, now the world’s attention being fixed on air transportation safety. In this paper, a flight quality evaluation method based on PCA-PSO-SVM was proposed. It can effectively make an objective evaluation of flight quality. First, it extracted the flight quality evaluation indicators for the take-off, climb and approach and landing stages based on the QAR data, and used the PCA comprehensive evaluation to evaluate the flight quality. Then, the evaluation result was used as the input of the SVM, and the parameters of the SVM were optimized through the PSO algorithm. Finally, by training the PSO-SVM model, a machine learning algorithm was implemented to evaluate flight quality. The test results showed that the accuracy of flight quality classification assessment is 90%. Therefore, this method can objectively and effectively evaluate flight quality, and benefits to improve flight quality and flight safety.
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