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Modified grey wolf optimizer-based support vector regression for ship maneuvering identification with full-scale trial

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Abstract

This study explores a nonparametric identification scheme for a ship maneuvering mathematical model. To overcome the difficulty in setting support vector regression (SVR) hyperparameters, a modified grey wolf optimizer algorithm is proposed. The algorithm introduces a nonlinear convergence factor and an adaptive position update strategy to enhance the search ability, which contributes toward identifying optimal hyperparameters. Using these optimal hyperparameters, SVR can predict the state variables pertaining to ship motion with high precision. The prediction of the motion state variables of the vessel YUKUN is considered as an illustrative example to verify the algorithm’s generalization ability and robustness. The prediction results indicate that, compared with the SVR based on the firefly algorithm and the particle swarm optimization, the proposed scheme offers the advantages of robustness, fewer iterations, and smaller prediction errors.

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Abbreviations

MGWO:

Modified grey wolf optimizer

SVR:

Support vector regression

FA:

Firefly algorithm

SI:

System identification

LS:

Least squares

RPE:

Recursive prediction error

EKF:

Extended Kalman filter

ML:

Maximum likelihood

NN:

Neural network

SVM:

Support vector machine

GA:

Genetic algorithm

RBF:

Radial basis function

RNN:

Recursive neural network

LWL:

Locally weighted learning

PSO:

Particle swarm optimization

ABC:

Artificial bee colony

DOF:

Degree of freedom

LWR:

Local weighted regression

\(u\) :

Surge velocity

\(v\) :

Sway velocity

\(r\) :

Yaw rate

\(\dot{u}\) :

Surge acceleration

\(\dot{v}\) :

Sway acceleration

\(\dot{r}\) :

Yaw acceleration

\(\delta\) :

Rudder angle

\(\psi\) :

Heading angle

\(\mathop{X}\limits^{\rightharpoonup}\) :

Position vector of grey wolf

\(\mathop{D}\limits^{\rightharpoonup}\) :

Distance vector of grey wolf

\(K\left({x}_{i},{x}_{j}\right)\) :

Kernel functions

MaxAE:

Maximum absolute error

MAE:

Mean absolute error

EV:

Error variance

MSE:

Mean square error

CC:

Correlation coefficient

OI:

Optimization iterations

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (51779029) and Fundamental Research Funds for the Central Universities (3132019306). The authors sincerely thank all the crew members who completed the full-scale sea trial test. The authors gratefully acknowledge this support.

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Authors and Affiliations

  1. Navigation Dynamic Simulation and Control Laboratory, Navigation College, Dalian Maritime University, Dalian, 116026, Liaoning, People’s Republic of China

    Xiufeng Zhang, Yao Meng, Zhaochun Liu & Jinxin Zhu

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  1. Xiufeng Zhang
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  2. Yao Meng
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  3. Zhaochun Liu
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  4. Jinxin Zhu
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Correspondence to Xiufeng Zhang.

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Zhang, X., Meng, Y., Liu, Z. et al. Modified grey wolf optimizer-based support vector regression for ship maneuvering identification with full-scale trial. J Mar Sci Technol 27, 576–588 (2022). https://doi.org/10.1007/s00773-021-00858-2

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  • DOI: https://doi.org/10.1007/s00773-021-00858-2

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