Open Access
Issue
JNWPU
Volume 42, Number 2, April 2024
Page(s) 295 - 302
DOI https://doi.org/10.1051/jnwpu/20244220295
Published online 30 May 2024
  1. ZHOU Zhihua. Machine learning[M]. Beijing: Tsinghua University Press, 2016: 73–88 (in Chinese) [Google Scholar]
  2. GOODFELLOW Ian, BENGIO Yoshua, COURVILLE Aaron. Deep learning[M]. ZHAO Shenjian, LI Yujun, FU Tianfan, et al. Translated. Beijing: People's Posts and Telecommunications Publishing House, 2021: 105–137 (in Chinese) [Google Scholar]
  3. NGUYEN D H, WIDROW B. Neural networks for self-learning control systems[J]. IEEE Control Systems Magazine, 1990, 10(3): 18–23. [Article] [Google Scholar]
  4. CHEN F C. Back-propagation neural networks for nonlinear self-tuning adaptive control[J]. IEEE Control Systems Magazine, 1990, 10(3): 44–48. [Article] [Google Scholar]
  5. CHEN T, GUESTRIN C. XGBoost: a scalable tree boosting system[C]//Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, 2016: 785–794 [Google Scholar]
  6. Flight Standards Department of the Civil Aviation Administration of China. Aircraft Flight Simulator Qualification Performance Standards[S]. AC-60-FS-2019-006, 2019 (in Chinese) [Google Scholar]
  7. WEN Yajun. Simulation analysis of a certain civil aircraft's negative acceleration flight[C]//Proceedings of the International Conference on Aerospace System Science and Engineering, Singapore, 2021: 163–173 [Google Scholar]
  8. WANG Zhao, TIAN Xiaotao, HUANG Meng, et al. Design of thrust estimator for solid rocket engine based on PSO-BP neural network[J]. Journal of Aerodynamics, 2022, 37(7): 1487–1494. [Article] (in Chinese) [Google Scholar]
  9. WANG Wenzhong, ZHANG Shusheng, YU Suihuai. Image resteoration by BP neural based on PSO[J]. Journal of Northwestern Polytechnical University, 2018, 36(4): 709–714. [Article] (in Chinese) [Google Scholar]
  10. LIU Fandong. Research on the application of forward neural networks in aircraft longitudinal control systems[D]. Xi'an: Northwestern Polytechnical University, 2004 (in Chinese) [Google Scholar]
  11. ZHONG R, JOHNSON JR R, CHEN Z. Generating pseudo density log from drilling and logging-while-drilling data using extreme gradient boosting(XGBoost)[J]. International Journal of Coal Geology, 2020, 97(3): 605–623 [Google Scholar]
  12. LI Qingkuo, ZHANG Ziqing, ZHANG Yingjie, et al. Optimization design for the diffuser vanes of a transonic centrifugal compressor based on the XGBoost algorithm[J]. Journal of Engineering Thermophysics, 2021, 42(8): 1970–1978. [Article] (in Chinese) [Google Scholar]
  13. LI Changlin, Tong Xin, YU Peixiang, et al. New method for predicting the transition position of airfoil surface based on xgboost model[J/OL]. [2022-12-30](2023-02-12). [Article] (in Chinese) [Google Scholar]
  14. EUGENE Mangortey, DYLAN Monteiro, JAMEY Ackley, et al. Application of machine learning techniques to parameter selection for flight risk identification[C]//AIAA Scitech 2020 Forum, Orlando, 2020: 1850 [Google Scholar]
  15. XU Maojun, WANG Jian, LIU Jinxin, et al. An improved hybrid modeling method based on extreme learning machine for gas turbine engine[J]. Aerospace Science and Technology, 2020, 107: 106333. [Article] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.