Citation: | CHEN Li-jia, ZHOU Xin-wei, YANG Pei-yi, WANG Kai, LI Sheng-wei. Modeling and prediction method of ship maneuvering motion facing environmental uncertainty[J]. Journal of Traffic and Transportation Engineering, 2024, 24(3): 279-295. doi: 10.19818/j.cnki.1671-1637.2024.03.020 |
[1] |
YAN Xin-ping, HE Ya-peng, HE Yi, et al. Development trends of waterway transportation technology[J]. Journal of Traffic and Transportation Engineering, 2022, 22(4): 1-9. (in Chinese) doi: 10.19818/j.cnki.1671-1637.2022.04.001
|
[2] |
ZHAO Bai-gang, ZHANG Xian-ku, LI Zheng, et al. Research on ship motion identification modeling[J]. Ship Science and Technology, 2021, 43(23): 21-24. (in Chinese) doi: 10.3404/j.issn.1672-7649.2021.12.004
|
[3] |
ZHANG Yan-yun, WANG Zi-hao, ZOU Zao-jian. Black-box modeling of ship maneuvering motion based on multi-output nu-support vector regression with random excitation signal[J]. Ocean Engineering, 2022, 257: 111279. doi: 10.1016/j.oceaneng.2022.111279
|
[4] |
ZHANG Xiu-feng, WANG Xiao-xue, MENG Yao, et al. Research progress and future development trend of ship motion modeling and simulation[J]. Journal of Dalian Maritime University, 2021, 47(1): 1-8. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-DLHS202101001.htm
|
[5] |
LU Guan-yu, YAO Jian-xi. Black-box modeling of ship maneuvering by means of SVR[J]. Navigation of China, 2021, 44(4): 13-19, 31. (in Chinese) doi: 10.3969/j.issn.1000-4653.2021.04.003
|
[6] |
MEI Bin, SUN Li-cheng, SHI Guo-you, et al. Grey box identification modeling for ship maneuverability based on single parameter self-adjustable RM-GO-LSVR[J]. Journal of Traffic and Transportation Engineering, 2020, 20(2): 88-99. (in Chinese) doi: 10.19818/j.cnki.1671-1637.2020.02.008
|
[7] |
LUO Wei-lin, ZOU Zao-jian. Identification of response models of ship maneuvering motion using support vector machines[J]. Journal of Ship Mechanics, 2007, 11(6): 832-838. doi: 10.3969/j.issn.1007-7294.2007.06.003
|
[8] |
WANG Xue-gang, ZOU Zao-jian, YU Long, et al. System identification modeling of ship manoeuvring motion in 4 degrees of freedom based on support vector machines[J]. China Ocean Engineering, 2015, 29(4): 519-534. doi: 10.1007/s13344-015-0036-9
|
[9] |
CHEN Li-jia, YANG Pei-yi, LI Sheng-wei, et al. Grey-box identification modeling of ship maneuvering motion based on LS-SVM[J]. Ocean Engineering, 2022, 266: 112957. doi: 10.1016/j.oceaneng.2022.112957
|
[10] |
XU Feng, LIU Zhi-ping, ZHENG Hai-bin, et al. On-line modeling of ship maneuvering motion by using LS-SVM[J]. Journal of Ship Mechanics, 2021, 25(6): 752-759. (in Chinese) doi: 10.3969/j.issn.1007-7294.2021.06.007
|
[11] |
XIE Shuo, CHU Xiu-min, LIU Chen-guang, et al. Online parameter identification method for ship maneuvering models based on improved LSSVM[J]. Shipbuilding of China, 2018, 59(2): 178-189. (in Chinese) doi: 10.3969/j.issn.1000-4882.2018.02.019
|
[12] |
QIU Wen-qin, TANG Cun-bao, TANG Qiang-rong. Navigation environment risk assessment of uncertain inland waterway[J]. Navigation of China, 2019, 42(1): 52-55, 67. (in Chinese) doi: 10.3969/j.issn.1000-4653.2019.01.011
|
[13] |
FAN Cun-long, ZHANG Di, YAO Hou-jie, et al. Navigational risk identification for maritime autonomous surface ship[J]. Navigation of China, 2019, 42(2): 75-82. (in Chinese) doi: 10.3969/j.issn.1000-4653.2019.02.015
|
[14] |
HUANG Bai-gang, ZOU Zao-jian. Online prediction of ship roll motion in irregular waves using a fixed grid wavelet network[J]. Journal of Ship Mechanics, 2020, 24(6): 693-705. (in Chinese) doi: 10.3969/j.issn.1007-7294.2020.06.001
|
[15] |
WANG Tong-tong, LI Guo-yuan, WU Bai-hang, et al. Parameter identification of ship manoeuvrling model under disturbance using support vector machine method[J]. Ships and Offshore Structures, 2021, 16(S1): 13-21.
|
[16] |
XIA Zhao-dan, MA Xiang, LIU Yi, et al. Machine learning for ship maneuvering in wave[J]. Chinese Journal of Hydrodynamics, 2022, 37(6): 763-768. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-SDLJ202206003.htm
|
[17] |
JIANG Yan, HOU Xian-rui, WANG Xue-gang, et al. Identification modeling and prediction of ship maneuvering motion based on LSTM deep neural network[J]. Journal of Marine Science and Technology, 2022, 27(1): 125-137. doi: 10.1007/s00773-021-00819-9
|
[18] |
XUE Yi-fan, CHEN Gang, LI Zhi-tong, et al. Online identification of a ship maneuvering model using a fast noisy input Gaussian process[J]. Ocean Engineering, 2022, 250: 110704. doi: 10.1016/j.oceaneng.2022.110704
|
[19] |
JIANG Yan, WANG Xue-gang, HOU Xian-rui, et al. Comparative study on identification of ship maneuvering motion based on deep recurrent neural network[J]. Journal of Marine Science and Technology, 2023, 38(2): 187-194. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-SDLJ202302004.htm
|
[20] |
ZHOU Xiao, ZOU Lu, OUYANG Zi-lu, et al. Nonparametric modeling of ship maneuvering motions in calm water and regular waves based on R-LSTM hybrid method[J]. Ocean Engineering, 2023, 285: 115259. doi: 10.1016/j.oceaneng.2023.115259
|
[21] |
ZENG Dao-hui, CAI Cheng-tao. Identification of the ship maneuvering response model based on recursive refined instrumental variable least-squares[J]. Journal of Harbin Engineering University, 2023, 44(2): 161-171. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HEBG202302001.htm
|
[22] |
ZENG Ke, GU Min, LU Jiang, et al. Numerical prediction of parametric rolling of ships in cross waves[J]. Shipbuilding of China, 2021, 62(4): 65-74. (in Chinese) doi: 10.3969/j.issn.1000-4882.2021.04.005
|
[23] |
SON K, NOMOTO K. On the coupled motion of steering and rolling of a high speed container ship[J]. Journal of the Society of Naval Architects of Japan, 1981, 1981(150): 232-244. doi: 10.2534/jjasnaoe1968.1981.150_232
|
[24] |
HU Yi, SONG Li-fei, LIU Zu-yuan, et al. Identification of ship hydrodynamic derivatives based on LS-SVM with wavelet threshold denoising[J]. Journal of Marine Science and Engineering, 2021, 9(12): 1356-1356. doi: 10.3390/jmse9121356
|
[25] |
CHEN Li-jia, YANG Pei-yi, LI Shi-gang, et al. Online modeling and prediction of maritime autonomous surface ship maneuvering motion under ocean waves[J]. Ocean Engineering, 2023, 276: 114183. doi: 10.1016/j.oceaneng.2023.114183
|
[26] |
NIE Zhi-hong, SHEN Feng, XU Ding-jie, 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. doi: 10.1016/j.oceaneng.2020.107927
|
[27] |
MENG Yao, ZHANG Xiu-feng, CHEN Yu-nong. Parameter identification of a ship mathematical model based on the modified grey wolf algorithm[J]. Journal of Harbin Engineering University, 2023, 44(8): 1304-1312. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-HEBG202308009.htm
|
[28] |
HOU Xian-rui. Identification modeling of ship motions in waves based on support vector regression[D]. Shanghai: Shanghai Jiao Tong University, 2017. (in Chinese)
|
[29] |
ZHANG Xin-guang. Online identification modeling of ship manoeuvring motion using support vector regression[J]. Ship Engineering, 2019, 41(3): 98-101. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-CANB201903022.htm
|
[30] |
WANG Zi-hao, XU Hai-tong, XIA Li, et al. Kernel-based support vector regression for nonparametric modeling of ship maneuvering motion[J]. Ocean Engineering, 2020, 216: 107994. doi: 10.1016/j.oceaneng.2020.107994
|
[31] |
SHEN Wen-he, YAO Jiang-xi, HU Xin-jue, et al. Ship dynamics model identification based on semblance least square support vector machine[J]. Ocean Engineering, 2023, 287: 115908. doi: 10.1016/j.oceaneng.2023.115908
|
[32] |
DONG Lei, MA Xiang, FENG Jia-xiang, et al. Online prediction method of ship maneuvering motion based on improved long-short term memory neural network[J]. Shipbuilding of China, 2023, 64(2): 184-198. (in Chinese) doi: 10.3969/j.issn.1000-4882.2023.02.017
|
[33] |
LUO Wei-lin, SOARES C G, ZOU Zao-jian. Parameter identification of ship maneuvering model based on support vector machines and particle swarm optimization[J]. Journal of Offshore Mechanics and Arctic Engineering, 2016, 138(3): 031101. doi: 10.1115/1.4032892
|
[34] |
LIU Chang-de, GU Yu-xiang, ZHANG Jin-feng. Extreme short-term prediction of ship motions based on wavelet filter and LSTM neural network[J]. Journal of Ship Mechanics, 2021, 25(3): 299-310. (in Chinese) doi: 10.3969/j.issn.1007-7294.2021.03.005
|
[35] |
PARAND K, AGHAEI A A, JANI M, et al. Parallel LS-SVM for the numerical simulation of fractional Volterra's population model[J]. Alexandria Engineering Journal, 2021, 60(6): 5637-5647. doi: 10.1016/j.aej.2021.04.034
|
[36] |
SHEN Wen-jun, ZHAO Zhi-juan, LIU Li-qin, et al. Research of wave period effect on the dynamic response characteristics of a small ship[J]. Journal of Ship Mechanics, 2022, 26(3): 342-352. (in Chinese) doi: 10.3969/j.issn.1007-7294.2022.03.004
|
[37] |
ZHANG Teng, REN Jun-sheng, MEI Tian-long. Mathematical model of ship motions in regular waves based on Froude-Krylov force nonlinear method[J]. Journal of Traffic and Transportation Engineering, 2020, 20(2): 77-87. (in Chinese) doi: 10.19818/j.cnki.1671-1637.2020.02.007
|
[38] |
QIAN Xiao-bin, YIN Yong, ZHANG Xiu-feng, et al. Influence of irregular disturbance of sea wave on ship motion[J]. Journal of Traffic and Transportation Engineering, 2016, 16(3): 116-124. (in Chinese) doi: 10.3969/j.issn.1671-1637.2016.03.014
|
[39] |
JIANG Hong, SUN Shi-wei, WEI Chang-jin, et al. Design of intelligent shipping ship-shore communication system and optimization of network data transmission[J]. Ship Science and Technology, 2020, 42(20): 178-180. (in Chinese) https://www.cnki.com.cn/Article/CJFDTOTAL-JCKX202020061.htm
|
[40] |
SUTULO S, GUEDES SOARES C. An algorithm for offline identification of ship manoeuvring mathematical models from free-running tests[J]. Ocean Engineering, 2014, 79: 10-25. doi: 10.1016/j.oceaneng.2014.01.007
|