基于动态贝叶斯网络和模糊灰度理论的飞行训练评估

doi:10.7527/S1000-6893.2021.25838

论文

本期目录 | 过刊浏览 | 高级检索

前一篇 | 后一篇

基于动态贝叶斯网络和模糊灰度理论的飞行训练评估

刘浩¹, 王昊¹, 孟光磊², 吴昊², 周铭哲²

1. 航空工业沈阳飞机设计研究所, 沈阳 110035;
2. 沈阳航空航天大学自动化学院, 沈阳 110136

收稿日期:2021-04-15 修回日期:2021-05-08 发布日期:2021-05-31
通讯作者: 刘浩 E-mail:liuhao@vista.aero
基金资助:
航空科学基金（2016ZD54015）

Flight training evaluation based on dynamic Bayesian network and fuzzy gray theory

LIU Hao¹, WANG Hao¹, MENG Guanglei², WU Hao², ZHOU Mingzhe²

1. AVIC Shenyang Aircraft Design and Research Institute, Shenyang 110035, China;
2. School of Automation, Shenyang Aerospace University, Shenyang 110136, China

Received:2021-04-15 Revised:2021-05-08 Published:2021-05-31
Supported by:
Aeronautical Science Foundation of China (2016ZD54015)

摘要/Abstract

摘要： 针对战斗机飞行训练中的评估问题，提出了一种基于动态贝叶斯网络和模糊灰度理论的评估方法。首先，分析了训练过程中典型飞行参数与机动动作的因果关系，根据专家经验与先验知识构建基于动态贝叶斯网络的机动动作识别模型，推理得到战斗机机动动作识别结果。然后，建立战斗机飞行训练评估指标体系，根据战斗机机动识别结果选择飞行训练评估指标，并采用综合赋权法确定了指标权重。最后，建立灰度模糊评估矩阵，结合飞行训练过程中各评估指标的飞行数据得到评估结果。实验结果表明该评估方法能够根据飞行过程中的参数信息进行机动动作识别及飞行训练评估，提高了飞行训练评估的效率。

关键词: 动态贝叶斯网络, 机动动作识别, 灰度模糊评估矩阵, 飞行训练评估, 评估指标体系

Abstract: An evaluation method for flight training of warplanes is proposed based on dynamic Bayesian network and fuzzy gray theory. Firstly, the causal relationship between typical flight parameters and maneuver in the training process is analyzed. The maneuver recognition model based on dynamic Bayesian network is constructed according to expert experience and prior knowledge, and the maneuver recognition results are obtained by reasoning. Then, an evaluation index system of fighter flight training is established. The evaluation index of flight training is selected according to the results of fighter maneuver identification, and the index weight is determined by the comprehensive weighting method. Finally, the gray fuzzy evaluation matrix is established, and the evaluation results are obtained by calculating the flight data of each evaluation index in the flight training process. The experimental results show that the evaluation method proposed can recognize maneuver and evaluate flight training according to the parameters in the flight process, improving the efficiency of flight training evaluation.

Key words: dynamic Bayesian network, maneuver recognition, gray fuzzy evaluation matrix, flight training evaluation, evaluation index system

中图分类号:

V249.1

刘浩, 王昊, 孟光磊, 吴昊, 周铭哲. 基于动态贝叶斯网络和模糊灰度理论的飞行训练评估[J]. 航空学报, 2021, 42(8): 525838-525838.

LIU Hao, WANG Hao, MENG Guanglei, WU Hao, ZHOU Mingzhe. Flight training evaluation based on dynamic Bayesian network and fuzzy gray theory[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2021, 42(8): 525838-525838.

参考文献

[1] 张建业, 李学仁, 倪世宏. 飞行成绩评定及管理系统[J]. 空军工程大学学报(自然科学版), 2001, 2(01):70-73. ZHANG J Y, LI X R, NI S H. Flight performance evaluation and management system[J]. Journal of Air Force Engineering University (Natural Science Edition), 2001, 2(1):70-73(in Chinese).
[2] 张福转, 孔庆波. 层次分析法在飞行模拟训练评价体系设计中的应用[J]. 电脑知识与技术, 2016, 12(11):270-271. ZHANG F Z, KONG Q B. Application of analytic hierarchy process in the design of flight simulation training evaluation system[J]. Computer Knowledge and Technology, 2016, 12(11):270-271(in Chinese).
[3] 高文琦, 张复春, 王立波, 等. 飞行训练成绩评估模型的建立与实现[J]. 电子设计工程, 2011, 19(24):50-52. GAO W Q, ZHANG F C, WANG L B, et al. Establishment and implementation of flight training performance evaluation model[J]. Electronic Design Engineering, 2011, 19(24):50-52(in Chinese).
[4] ABDUSSAMIE N, DABOOS M, ELFERJANI I, et al. Risk assessment of LNG and FLNG vessels during manoeuvring in open sea[J]. Journal of Ocean Engineering and Science, 2018, 3(1):56-66.
[5] 黄江中, 陈秀清, 许威, 等. 基于模糊灰度变换的水下图像增强技术研究[J]. 应用科技, 2018, 45(3):1-6. HUANG J Z, CHEN X Q, XU W, et al. Research on underwater image enhancement technology based on fuzzy gray transformation[J]. Applied Science and Technology, 2018, 45(3):1-6(in Chinese).
[6] 乔晋崴, 肖光春, 衣明东, 等. 基于模糊灰度理论的重卡消声器冲压生产线可靠性分配[J]. 齐鲁工业大学学报, 2019, 33(5):52-58. QIAO J X, XIAO G C, YI M D, et al. Reliability allocation of heavy truck muffler stamping production line based on fuzzy gray theory[J]. Journal of Qilu University of Technology, 2019, 33(5):52-58(in Chinese).
[7] 朱玉琴, 王旭, 叶琴, 等. 潮流发电装置综合评价方法[J]. 现代电力系统与清洁能源杂志, 2015, 4(4):702-708. ZHU Y Q, WANG X, YE Q, et al. Comprehensive evaluation method for tidal current power generation device[J]. Journal of Modern Power Systems & Clean Energy, 2015, 4(4):702-708(in Chinese).
[8] HAN W, YANG Z, HUANG L, et al. Fuzzy comprehensive evaluation of the effects of relative air humidity on the morpho-physiological traits of Pakchoi (Brassica chinensis L.) under high temperature[J]. Scientia Horticulturae, 2019, 246:971-978.
[9] LEE E W, WONG L T, MUI K W. Development of a hybrid artificial neural network model and its application to data regression[J]. Intelligent Automation & Soft Computing, 2012, 18(4):319-332.
[10] SREEKANTH T G, SENTHILKUMAR M, REDDY S M. Fatigue life evaluation of delaminated GFRP laminates using artificial neural networks[J]. Transactions of the Indian Institute of Metals, 2021(in press).
[11] 柳忠起, 袁修干, 樊瑜波. 基于BP神经网络的飞行绩效评价模型[J]. 北京航空航天大学学报, 2010, 36(4):403-406. LIU Z Q, YUAN X G, FAN Y B. Flight performance evaluation model based on BP neural network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2010, 36(4):403-406(in Chinese).
[12] SU Z, YU S, CHU J, et al. A novel architecture:Using convolutional neural networks for Kansei attributes automatic evaluation and labeling[J]. Advanced Engineering Informatics, 2020, 44:101055.
[13] 孔祥芬, 王杰, 张兆民. 基于贝叶斯网络和共因失效的飞机电源系统可靠性分析[J]. 航空学报, 2020, 41(5):323632. KONG X F, WANG J, ZHANG Z M. Reliability analysis of aircraft power system based on Bayesian network and common cause failure[J]. Acta Aeronautica et Astronautica Sinica, 2020, 41(5):323632(in Chinese).
[14] 于劲松, 沈琳, 唐荻音, 等. 基于贝叶斯网络的故障诊断系统性能评价[J]. 北京航空航天大学学报, 2016, 42(1):35-40. YU J S, SHEN L, TANG D Y, et al. Performance evaluation of fault diagnosis system based on Bayesian network[J]. Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(01):35-40(in Chinese).
[15] 汪泽辉, 方洋旺. 基于贝叶斯网络的空战效能评估方法研究[J]. 航空工程进展, 2018, 9(1):35-42. WANG Z H, FANG Y W. Effectiveness evaluation method of air-combat based on Bayesian network[J]. Advances in Aeronautical Science and Engineering, 2018, 9(1):35-42(in Chinese).
[16] 孟光磊, 张慧敏, 朴海音, 等. 自动化飞行训练评估中的战机机动动作识别[J]. 北京航空航天大学学报, 2020, 46(7):1267-1274. MENG G L, ZHANG H M, PIAO H Y, et al. Maneuver recognition in automatic flight training evaluation[J]. Journal of Beijing University of Aeronautics and Astronautics, 2020, 46(7):1267-1274(in Chinese).
[17] 王玉伟, 高永. 基于综合赋权法的无人机飞行质量综合评价方法[J]. 兵工自动化, 2019, 38(5):1-4, 10. WANG Y W, GAO Y. Comprehensive evaluation method of UAV flight quality based on comprehensive weighting method[J]. Ordnance Automation, 2019, 38(5):1-4, 10(in Chinese).
[18] DU M, GAO H, LI L, et al. Power grid security risk assessment based on the comprehensive element influence index[J]. IOP Conference Series:Earth and Environmental Science, 2019, 227(3):10.
[19] ZHAO Y, PAN L, XIN X, et al. Multiobjective evaluation of midblock crosswalks on urban streets based on TOPSIS and entropy methods[J]. Transportation Research Record Journal of the Transportation Research Board, 2016, 2586:59-71.
[20] WANG M, NIU D. Research on project post-evaluation of wind power based on improved ANP and fuzzy comprehensive evaluation model of trapezoid subordinate function improved by interval number[J]. Renewable Energy, 2019, 132(10):255-265.
[21] HOU B, HUANG Z, ZHOU H, et al. A hybrid hint-based and fuzzy comprehensive evaluation method for optimal parting curve generation in injection mold design[J]. The International Journal of Advanced Manufacturing Technology, 2021, 112(12):2133-2148.
[22] WEI J, LI G, XIE D, et al. Discrimination of mine water-inflow sources based on the multivariate mixed model and fuzzy comprehensive evaluation[J]. Arabian Journal of Geosciences, 2020, 13(17):873.
[23] YANG Y F, ZHANG M H, DAI Y W, et al. A fuzzy comprehensive CS-SVR model-based health status evaluation of radar[J]. PLoS One, 2019, 14(3):e0213833.
[24] SUN Y, WANG L, XU J, et al. An intelligent coupling 3-grade fuzzy comprehensive evaluation approach with AHP for selection of levitation controller of maglev trains[J]. IEEE Access, 2020, 8:99509-99518.
[25] 卜广志, 张宇文. 基于灰色模糊关系的灰色模糊综合评判[J]. 系统工程理论与实践, 2002, 22(4):141-144. BU G Z, ZHANG Y W. Grey fuzzy comprehensive evaluation based on the the theory of grey fuzzy relation[J]. Systems Engineering-Theory & Practice, 2002, 22(4):141-144. (in Chinese)
[26] 国连玉, 李可军, 梁永亮, 等. 基于灰色模糊综合评判的高压断路器状态评估[J]. 电力自动化设备, 2014, 34(11):161-167. GUO L Y, LI K J, LIANG Y L, et al. HV circuit breaker state assessment based on gray-fuzzy comprehensive evaluation[J]. Electric Power Automation Equipment, 2014, 34(11):161-167(in Chinese).
[27] SU L, LI H, LI Z, et al. Identification of Unbalanced bids based on grey-fuzzy evaluation method[J]. Canadian Journal of Civil Engineering, 2019, 47(3):272-278.
[28] YASUDA M, SEKIMOTO K. Spatial Monte Carlo integration with annealed importance sampling[J]. Physical Review E, 2021, 103(5):052118.
[29] SIFERD R, MAYBECK P. Monte Carlo approach for fatigue and damage calculation of nonlinear dynamical systems[J]. Chaos, 2020, 30(7):073135.
[30] BAREL A V, VANDEWALLE S. Robust optimization with a multilevel Monte Carlo method[J]. SIAM/ASA Journal on Uncertainty Quantification, 2019, 7(1):174-202.

E-mail：hkxb@buaa.edu.cn

关于我们

期刊社服务

专业学科

封面文章

友情链接

主管单位：中国科学技术协会主办单位：中国航空学会北京航空航天大学

基于动态贝叶斯网络和模糊灰度理论的飞行训练评估

Flight training evaluation based on dynamic Bayesian network and fuzzy gray theory

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	章胜周攀何扬黄江涛刘刚唐骥罡贾怀智杜昕. 基于深度强化学习的空战机动决策试验研究[J]. 航空学报, 0, (): 0-0.
[2]	张亮李丹钰崔乃刚李源. 垂直起降重复使用火箭全剖面飞行预设性能控制（可重复使用火箭技术专栏）[J]. 航空学报, 0, (): 0-0.
[3]	唐永兴, 朱战霞, 张红文, 罗建军, 袁建平. 机器人运动规划方法综述[J]. 航空学报, 2023, 44(2): 26495-026495.
[4]	梁益铭李广宁徐敏. 一种基于机器学习的智能控制数值虚拟飞行方法[J]. 航空学报, 0, (): 0-0.
[5]	郭华郭小和. 改进速度障碍法的无人机局部路径规划算法[J]. 航空学报, 0, (): 0-0.
[6]	刘武吴云燕刘玮田明明黄天鹏. 考虑未知扰动的RLV再入鲁棒容错姿态控制[J]. 航空学报, 0, (): 0-0.
[7]	胡润昌王子安陈永亮周大鹏杨大鹏龚正. 基于L1自适应推力矢量型V/STOL飞行器增稳控制(ICGNC2022推荐优秀论文)[J]. 航空学报, 0, (): 0-0.
[8]	王彬任鹏达张伟谢志刚. 航空双系统直驱伺服阀阀芯振荡机理及抑制方法[J]. 航空学报, 0, (): 0-0.
[9]	刘鹏宇朱雪耀. 基于深度学习的融合空域空管指令语义解析技术研究(ICGNC2022推荐优秀论文)[J]. 航空学报, 0, (): 0-0.
[10]	杨庶. 具有边界保护性能的直升机飞-发一体化控制律设计（ICGNC2022推荐优秀论文）[J]. 航空学报, 0, (): 0-0.
[11]	赵旭齐国元蔚昕晨胡建兵李霞. 补偿函数观测器及其在飞行器姿态控制中应用(稿号22-27108退稿重投)[J]. 航空学报, 0, (): 0-0.
[12]	宋鑫康, 赵尚弘, 王翔. 支持差异化服务的航空信息网络切片策略[J]. 航空学报, 2022, 43(3): 325123-325123.
[13]	石忠佼, 朱化杰, 赵良玉, 刘志杰. 考虑舵机动力学的旋转弹自适应解耦控制[J]. 航空学报, 2022, 43(3): 325068-325068.
[14]	胡嘉悦, 李广文, 章卫国, 张超, 贾乾磊. 面向有人/无人机协同远程作战的IVMS架构[J]. 航空学报, 2022, 43(3): 325199-325199.
[15]	周攀黄江涛章胜刘刚舒博文唐骥罡. 基于深度强化学习的智能空战决策与仿真研究[J]. 航空学报, 0, (): 0-0.