Nonparametric Modelling of Ship Dynamics Using Puma Optimizer Algorithm-Optimized Twin Support Vector Regression

Ship dynamic models serve as the foundation for designing ship controllers, trajectory planning, and obstacle avoidance. Support vector regression (SVR) is a commonly used nonparametric modelling method for ship dynamics. Achieving high accuracy SVR models requires a substantial amount of training s...

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Vydané v:Journal of marine science and engineering Ročník 12; číslo 5; s. 754
Hlavní autori: Jiang, Lichao, Zhang, Zhi, Lu, Lingyun, Shang, Xiaobing, Wang, Wei
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.05.2024
Predmet:
Accuracy
Algorithms
Computational efficiency
Computer applications
Computing time
Container ships
Controllers
Design
Dynamic models
Efficiency
Kalman filters
Machine learning
Methods
Modelling
nonparametric modelling
Obstacle avoidance
Parameter estimation
Parameter identification
puma optimizer algorithm
Quadratic programming
Regression analysis
ship dynamics
ship manoeuvring motion
Support vector machines
Training
Trajectory control
Trajectory planning
twin support vector regression
Variables
White noise
United Kingdom
ISSN:2077-1312, 2077-1312
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Popis
Shrnutí:Ship dynamic models serve as the foundation for designing ship controllers, trajectory planning, and obstacle avoidance. Support vector regression (SVR) is a commonly used nonparametric modelling method for ship dynamics. Achieving high accuracy SVR models requires a substantial amount of training samples. Additionally, as the number of training samples increases, the computational efficiency for solving the quadratic programming problem (QPP) of SVR decreases. Ship controllers demand dynamic models with both high accuracy and computational efficiency. Therefore, to enhance the prediction accuracy and computational efficiency of SVR, this paper proposes a nonparametric modelling method based on twin SVR (TSVR). TSVR replaces a large QPP with a set of smaller QPPs, significantly enhancing generalizability and computational efficiency. To further improve the predictive accuracy of TSVR, the puma optimizer algorithm is employed to determine the optimal hyperparameters. The performance of the proposed method is validated using a Mariner class vessel. Gaussian white noise is introduced into the modelling data to simulate measurement error. The TSVR model accurately predicts various zigzag and turning circle manoeuvring motions under disturbance conditions, demonstrating its robustness and generalizability. Compared to the SVR model, the TSVR model achieves lower root mean square error and computational time, confirming its superior predictive accuracy and computational efficiency.
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ISSN:2077-1312
2077-1312
DOI:10.3390/jmse12050754