Open Access
Issue
JNWPU
Volume 42, Number 3, June 2024
Page(s) 377 - 385
DOI https://doi.org/10.1051/jnwpu/20244230377
Published online 01 October 2024
  1. ZHU Qidan, ZHANG Zhi, ZHANG Wen. Aircraft carrier aircraft safe takeoff and landing technology[M]. Harbin: Harbin Engineering University Press, 2016 (in Chinese) [Google Scholar]
  2. SUN Q, TANG Z, GAO J P, ZHANG G C. Short-term ship motion attitude prediction based on LSTM and GPR[J]. Applied Ocean Research, 2022, 118: 102927 [Article] [CrossRef] [Google Scholar]
  3. KAPLAN P. A study of prediction techniques for aircraft carrier motions at sea[C]//AIAA 6th Aerospace Sciences Meeting, 1968: 68–123 [Google Scholar]
  4. YUMORI I. Real time prediction of ship response to ocean waves using time series analysis[C]//Proceedings of OCEANS 81, 1981: 1082–1089 [Google Scholar]
  5. JIANG H, DUAN S L, HUANG L, et al. Scale effects in AR model real-time ship motion prediction[J]. Ocean Engineering, 2020, 203: 107202 [Article] [CrossRef] [Google Scholar]
  6. KHAN A, BIL C, MARION K E. Ship motion prediction for launch and recovery of air vehicles[C]//Proceedings of OCEANS 2005 MTS/IEEE, 2005: 2795–2801 [Google Scholar]
  7. LUO W, REN J. On the identification of coupled pitch and heave motions using support vector machine[C]//2016 Chinese Control and Decision Conference, 2016: 3316–3321 [Google Scholar]
  8. YIN J, ZOU Z, XU F. On-line prediction of ship roll motion during maneuvering using sequential learning RBF neural networks[J]. Ocean Engineering, 2013, 61, 139–147 [Article] [CrossRef] [Google Scholar]
  9. SILVA K M, MAKI K J. Data-driven system identification of 6-DoF ship motion in waves with neural networks[J]. Applied Ocean Research, 2022, 125: 103222 [Article] [CrossRef] [Google Scholar]
  10. XU C Z, ZOU Z J. Online prediction of ship roll motion in waves based on auto-moving gird search-least square support vector machine[J]. Mathematical Problems in Engineering, 2021, 2021, 2760517 [Google Scholar]
  11. SKULSTAD R, LI G, FOSSEN T, et al. A cooperative hybrid model for ship motion prediction[J]. Modeling Identification and Control, 2021, 42(1): 17–26 [Article] [CrossRef] [Google Scholar]
  12. ZHANG Biao, PENG Xiuyan, GAO Jie. Ship motion attitude prediction based on ELM-EMD-LSTM integrated model[J]. Journal of Ship Mechanics, 2020, 24(11): 1413–1421 [Article] (in Chinese) [Google Scholar]
  13. ZHANG G, TAN F, WU Y. Ship motion attitude prediction based on an adaptive dynamic particle swarm optimization algorithm and bidirectional LSTM neural network[J]. IEEE Access, 2020, 8: 90087–90098 [Article] [NASA ADS] [CrossRef] [Google Scholar]
  14. NIE Z, SHEN F, XU D, et al. An EMD-SVR model for short-term prediction of ship motion using mirror symmetry and SVR algorithms to eliminate EMD boundary effect[J]. Ocean Engineering, 2020, 217: 107927 [Article] [CrossRef] [Google Scholar]
  15. HE Zhikun, LIU Guangbin, ZHAO Xijing, et al. Overview of Gaussian process regression[J]. Control and Decision, 2013, 28(8): 1121–1129 [Article] (in Chinese) [Google Scholar]
  16. YU Min, LUO Jianjun, WANG Mingming. Real-time motion prediction of space tumbling targets based on machine learning[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42(2): 195–205 [Article] (in Chinese) [Google Scholar]
  17. DUVENAUD D, LLOYD J, GROSSE R, et al. Structure discovery in nonparametric regression through compositional kernel search[C]//Proceedings of the 30th International Conference on Machine Learning, PMLR, 2013: 1166–1174 [Google Scholar]
  18. RICHARDSON R R, OSBORNE M A, HOWEY D A. Gaussian process regression for forecasting battery state of health[J]. Journal of Power Sources, 2017, 357: 209–219 [Article] [Google Scholar]
  19. HU Weijie, HUANG Zenghui, LIU Xuejun, et al. Missile aerodynamic performance prediction of Gaussian process through automatic kernel construction[J]. Acta Aeronautica et Astronautica Sinica, 2021, 42(4): 289–302 [Article] (in Chinese) [Google Scholar]
  20. SUN S, ZHANG G, WANG C, et al. Differentiable compositional kernel learning for Gaussian processes[C]//Proceedings of the 35th International Conference on Machine Learning, PMLR, 2018: 4828–4837 [Google Scholar]
  21. WANG Ke, XU Mingliang, LI Yafei, et al. A robust learning model for deck motion prediction of aircraft carrier[J]. Acta Automatica Sinica, 2021, 48(1): 1–9 (in Chinese) [Google Scholar]
  22. SCHULZ E, SPEEKENBRINK M, KRAUSE A. A tutorial on Gaussian process regression: modelling, exploring, and exploiting functions[J]. Journal of Mathematical Psychology, 2018, 85: 1–16 [Article] [CrossRef] [Google Scholar]
  23. WILSON A, ADAMS R. Gaussian process kernels for pattern discovery and extrapolation[C]//Proceedings of the 30th International Conference on Machine Learning, PMLR, 2013: 1067–1075 [Google Scholar]
  24. YANG Yidong, YU Junya. Shipboard aircraft landing guidance and control[M]. Beijing: Defense Industry Press, 2007 (in Chinese) [Google Scholar]
  25. HODGES L H, SCHON D A. An analysis of teminal flight path control in carrier landing[R]. AD606040, 1970 [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.