Abstract
In order to improve the accuracy of ship motion identification modeling, the grey wolf optimizer (GWO) algorithm is used to optimize the hyperparameters of support vector regression(SVR). GWO-SVR identification algorithm is proposed and applied to black box identification modeling of ship motion. All the 15° / 15° zigzag test and part of 5°, 20° turning tests data are used as training data to train hyperparameters of SVR. The trained prediction model is applied to predict the whole 20° turning test and 20° / 20° zigzag test data. Compared with the prediction results of SVR based on particle swarm optimization (PSO-SVR), the prediction results of GWO-SVR algorithm show that the proposed algorithm has the advantages of smaller prediction errors, faster convergence speed and better generalization performance.
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