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Ship Motion Prediction Method Based on Long Short-Term Memory Network and Gaussian Process Regression
Ship Motion Prediction Method Based on Long Short-Term Memory Network and Gaussian Process Regression
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机译：基于长短期内存网络和高斯过程回归的船舶运动预测方法
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摘要
The disclosure discloses a ship motion prediction method based on long short-term memory network and Gaussian process regression. The method includes: normalizing acquired ship motion historical data under a certain degree of freedom to form a ship motion original time series; dividing the original time series into a training set and a test set; reconstructing a data set according to the training set and the test set, and establishing a long short-term memory (LSTM) network model for prediction to obtain prediction results of the first ship motion; reconstructing a data set, and establishing a Gaussian process regression (GPR) model for prediction to obtain prediction results of the second ship motion; and denormalizing the prediction results obtained by the Gaussian process regression model to obtain final ship motion prediction results. Aiming at highly non-linear ship motion, the disclosure can obtain ship motion interval prediction results with probability distribution significance while obtaining high-accuracy point prediction results.
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