公开(公告)号：US20210117796A1

公开(公告)日：2021-04-22

申请号：US17136409

申请日：2020-12-29

Applicant: Harbin Engineering University

Inventor： Qian SUN ,  Zhong Tang ,  Qianhui Dong ,  Yibing Li ,  Fang Ye ,  Yuan Tian ,  Fei Yu

IPC: G06N3/08 ,  G06N3/04

Abstract: 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.