[1]JIANG Lichao,SHANG Xiaobing,WANG Wei,et al.Nonparametric modeling method of ship maneuvering motion based on the ν-SVR with mixed kernel function[J].CAAI Transactions on Intelligent Systems,2024,19(6):1376-1384.[doi:10.11992/tis.202310001]
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Nonparametric modeling method of ship maneuvering motion based on the ν-SVR with mixed kernel function

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 6 Page number: 1376-1384 Column: 学术论文—机器学习 Public date: 2024-12-05
Title:
Nonparametric modeling method of ship maneuvering motion based on the ν-SVR with mixed kernel function
Author(s):
JIANG Lichao;  SHANG Xiaobing;  WANG Wei;  ZHANG Zhi;  LI Jiaqi
College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China
Keywords:
nonparametric modeling; mixed kernel function; system identification; ν-SVR; ship maneuvering motion; genetic algorithm; SIMMAN2008; KVLCC2
CLC:
TP18; U661.33
DOI:
10.11992/tis.202310001
Abstract:
Nonparametric modeling methods are widely used in modeling ship maneuvering motions. This study introduces a v-support vector regression (v-SVR) nonparametric modeling method based on a mixed kernel function (MK), optimized through a genetic algorithm (GA) called GA-MK-v-SVR. The MK aims to improve the performance of v-SVR by combining radial basis functions with polynomial kernel functions, thus capturing both global and local characteristics. A GA is employed to fine-tune the hyper-parameters. The performance of GA-MK-v-SVR was evaluated by using KVLCC2 free-running test data provided by SIMMAN 2008, and the results were compared with several maneuvering models. Experimental results show that the proposed GA-MK-v-SVR model delivers impressive prediction accuracy and robust generalization capabilities for ship maneuvering motion modeling.
References:
[1] 刘胜, 宋佳, 李高云. PSO并行优化LSSVR非线性黑箱模型辨识[J]. 智能系统学报, 2010, 5(1): 51-56.
LIU Sheng, SONG Jia, LI Gaoyun. Modeling a complex nonlinear system with particle swarm optimization and parallel-optimized least squares support vector regression[J]. CAAI transactions on intelligent systems, 2010, 5(1): 51-56.
[2] WANG Zihao, XU Haitong, XIA Li, et al. Kernel-based support vector regression for nonparametric modeling of ship maneuvering motion[J]. Ocean engineering, 2020, 216: 107994.
[3] HE Hongwei, WANG Zihao, ZOU Zaojian, et al. Nonparametric modeling of ship maneuvering motion based on self-designed fully connected neural network[J]. Ocean engineering, 2022, 251: 111113.
[4] YUE Wancheng, REN Junsheng, BAI Weiwei. Online non-parametric modeling for ship maneuvering motion using local weighted projection regression and extended Kalman filter[C]//2023 IEEE 12th Data Driven Control and Learning Systems Conference. Xiangtan: IEEE, 2023: 793-798.
[5] ZHU Man, HAHN A, WEN Yuanqiao, et al. Parameter identification of ship maneuvering models using recursive least square method based on support vector machines[J]. International journal on marine navigation and safety of sea transportation, 2017, 11(1): 23-29.
[6] ZHANG Zhao, REN Junsheng, WANG Guangxing. Multi-dimensional local weighted regression ship motion identification modeling based on particle swarm optimization[C]//2019 Chinese Control Conference. Guangzhou: IEEE, 2019: 1520-1525.
[7] EMMANUEL I. Simulation of manoeuvring safety of ships in adverse weather conditions when subject to limited power imposed by EEDI improvement by engine derating[D]. Newcastle: University of Newcastle upon Tyne, 2019.
[8] OUYANG Zilu, ZOU Zaojian, ZOU Lu. Adaptive hybrid-kernel function based Gaussian process regression for nonparametric modeling of ship maneuvering motion[J]. Ocean engineering, 2023, 268: 113373.
[9] HAN Yang, HAO Lizhu, SHI Chao, et al. Prediction of ship maneuvering motion with grey-box modelling incorporating mechanism and data[J]. Ships and offshore structures, 2023: 1-14.
[10] WANG Xin, GUEDES SOARES C. Direct adaptive neural network control for ship manoeuvring modelling group model-based uncertain nonlinear systems in non-affine pure-feedback form[J]. IEEE access, 2020, 8: 3272-3284.
[11] XUE Yifan, LIU Yanjun, JI Chen, et al. System identification of ship dynamic model based on Gaussian process regression with input noise[J]. Ocean engineering, 2020, 216: 107862.
[12] ARIZA R W, LEONG Z Q, NGUYEN H, et al. Non-parametric dynamic system identification of ships using multi-output Gaussian processes[J]. Ocean engineering, 2018, 166: 26-36.
[13] ZHANG Xiufeng, MENG Yao, LIU Zhaochun, et al. Modified grey wolf optimizer-based support vector regression for ship maneuvering identification with full-scale trial[J]. Journal of marine science and technology, 2022, 27(1): 576-588.
[14] LIU Xixiang, WANG Qiming, HUANG Rong, et al. A prediction method for deck-motion based on online least square support vector machine and genetic algorithm[J]. Journal of marine science and technology, 2019, 24(2): 382-397.
[15] ZHANG Yanyun, WANG Zihao, ZOU Zaojian. 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.
[16] LIU Jiao, SHI Guoyou, ZHU Kaige. Vessel trajectory prediction model based on AIS sensor data and adaptive chaos differential evolution support vector regression (ACDE-SVR)[J]. Applied sciences, 2019, 9(15): 2983.
[17] FANG Y D, ZHAN Z F, YANG J Q, et al. A mixed-kernel-based support vector regression model for automotive body design optimization[C]//Proceedings of ASME 2016 International Mechanical Engineering Congress and Exposition. Phoenix:ASME, 2016: 67669.
[18] CHENG Kai, LU Zhenzhou, WEI Yuhao, et al. Mixed kernel function support vector regression for global sensitivity analysis[J]. Mechanical systems and signal processing, 2017, 96: 201-214.
[19] 杨帅, 郭茂祖, 赵玲玲, 等. 融合遗传算法与XGBoost的玉米百粒重相关基因挖掘[J]. 智能系统学报, 2022, 17(1): 170-180.
YANG Shuai, GUO Maozu, ZHAO Lingling, et al. The method of 100-kernel weight related genes mining in maize mixed with genetic algorithm and XGboost[J]. CAAI transactions on intelligent systems, 2022, 17(1): 170-180.
[20] 裴小兵, 孙志卫. 改进区块遗传算法解决分布式车间调度问题[J]. 智能系统学报, 2021, 16(2): 303-312.
PEI Xiaobing, SUN Zhiwei. Solving distributed-shop scheduling problems based on modified genetic algorithm[J]. CAAI transactions on intelligent systems, 2021, 16(2): 303-312.
[21] 赵新超, 郭赛. 遗传算法求解多旅行商问题的相对解空间分析[J]. 智能系统学报, 2018, 13(5): 760-768.
ZHAO Xinchao, GUO Sai. Analysis on the relative solution space for MTSP with genetic algorithm[J]. CAAI transactions on intelligent systems, 2018, 13(5): 760-768.
[22] STERN F, AGDRUP K, KIM S Y, et al. Experience from SIMMAN 2008-the first workshop on verification and validation of ship maneuvering simulation methods[J]. Journal of ship research, 2011, 55(2): 135-147.
[23] FOSSEN T I. Handbook of marine craft hydrodynamics and motion control[M]. United Kingdom: Wiley, 2021.
[24] 刘明华, 张强. 支持向量机与神经网络相结合的板带凸度预测[J]. 智能系统学报, 2022, 17(3): 506-514.
LIU Minghua, ZHANG Qiang. Prediction of strip crown based on support vector machine and neural network[J]. CAAI transactions on intelligent systems, 2022, 17(3): 506-514.
[25] 汪鸿翔, 柳培忠, 骆炎民, 等. 高斯核函数卷积神经网络跟踪算法[J]. 智能系统学报, 2018, 13(3): 388-394.
WANG Hongxiang, LIU Peizhong, LUO Yanmin, et al. Convolutional neutral network tracking algorithm accelerated by Gaussian kernel function[J]. CAAI transactions on intelligent systems, 2018, 13(3): 388-394.
[26] 张艳云, 欧阳子路, 邹早建. 基于ν-支持向量机的波浪中船舶操纵运动辨识建模[J]. 船舶力学, 2023, 27(5): 637-645.
ZHANG Yanyun, OUYANG Zilu, ZOU Zaojian. Identification modeling of ship maneuvering motion in waves based on ν-support vector machine[J]. Journal of ship mechanics, 2023, 27(5): 637-645.
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Last Update: 2024-11-05

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