In order to improve the accuracy of the ship ’s rolling motion prediction when sailing at sea, a combined prediction algorithm of ship rolling motion based on Complete Ensemble Empirical Mode Decomposition(CEEMD)-Long Short Term Memory Network(LSTM) is proposed. The traditional prediction method ignores the relationship between the data. LSTM has the memory of data, it does not need a lot of data processing or complex modeling process,. The CEEMD algorithm decomposes the original sequence of ship rolling into several modal functions with different characteristics, which makes the non-stationary time series stable and periodic. The LSTM prediction model is established for each component, and the prediction results of each component are superimposed to obtain short-term rolling prediction value. In order to verify the effectiveness of the algorithm proposed in this paper, the real ship data is used for verification. The first 85% of the data is used as the training set, and the last 15% is used for testing. Finally, the effectiveness of the proposed model is verified by experimental results.
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