Online Prediction of Ship Roll Motion in Waves Based on Auto-Moving Gird Search-Least Square Support Vector Machine

Chang-Zhou Xu,Zao-Jian Zou,Yang Li

doi:10.1155/2021/2760517

Chang-Zhou Xu, Zao-Jian Zou + Show 1 more

Open Access

https://doi.org/10.1155/2021/2760517

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Abstract

A novel method based on auto-moving grid search-least square support vector machine (AGS-LSSVM) is proposed for online predicting ship roll motion in waves. To verify the method, simulation data are used, which are obtained by solving the second-order nonlinear differential equation of ship roll motion using the fourth-order Runge–Kutta method, while the Pierson–Moskowitz spectrum (P–M spectrum) is used to simulate the irregular waves. Combining the sliding time window with the least square support vector machine (LS-SVM), the samples in the time window are used to train the LS-SVM model, and the model hyperparameters are optimized online by the auto-moving grid search (AGS) method. The trained model is used to predict the roll motion in the next 30 seconds, and the prediction results are compared with the simulation data. It is shown that the AGS-LSSVM is an effective method for online predicting ship roll motion in waves.

Highlights

Prediction of ship motion at sea is important for ship’s safety and efficient operation
In order to meet the requirements of online prediction, this paper proposes an effective auto-moving grid search (AGS) method to innovate in the online hyperparameter optimization for least square support vector machine (LS-support vector machine (SVM))
E rest of the paper is organized as follows: Section 2 describes the principles of the algorithm, including LS-SVM, time windows, and AGS; Section 3 presents the data preparation, that is, the acquisition of ship roll motion data by simulation; in Section 4, the prediction results are presented and compared; and Section 5 provides a summary of the innovations and shortcomings of the methodology presented in this paper

Summary

Introduction

Prediction of ship motion at sea is important for ship’s safety and efficient operation. E modeling methods of AR, ARMA, and ARIMA models are simple, and the computation burden is small, but they are mainly suitable for modeling and prediction of linear systems; for the problem of strongly nonlinear ship roll motion, these methods still have certain limitations. As an excellent machine learning method, SVM has rigorous derivation based on mathematical theory and can obtain the optimal solution in the case of limited samples. It has an excellent performance in nonlinear function fitting, as well as parameter identification and prediction of ship nonlinear motion. E problem of offline training the model and selecting the hyperparameters is that it does not consider the complex and changeable sea conditions in actual navigation. E rest of the paper is organized as follows: Section 2 describes the principles of the algorithm, including LS-SVM, time windows, and AGS; Section 3 presents the data preparation, that is, the acquisition of ship roll motion data by simulation; in Section 4, the prediction results are presented and compared; and Section 5 provides a summary of the innovations and shortcomings of the methodology presented in this paper

Online Predictions

Numerical Simulation of Ship Roll Motion in Irregular Waves

Online Prediction of Roll Motion and Analysis of the Results

Methods