Abstract
When ships sail on the sea, the changes of ship motion attitude presents the characteristics of nonlinearity and high randomness. Aiming at the problem of low accuracy of ship roll angle prediction by traditional prediction algorithms and single neural network model, a ship roll angle prediction method based on bidirectional long short-term memory network (Bi-LSTM) and temporal pattern attention mechanism (TPA) combined deep learning model is proposed. Bidirectional long short-term memory network extracts time features from the forward and reverse of the ship roll angle time series, and temporal pattern attention mechanism extracts the time patterns from the deep features of a bidirectional long short-term memory network output state that are beneficial to ship roll angle prediction, ignore other features that contribute less to the prediction. The experimental results of real ship data show that the proposed Bi-LSTM-TPA combined model has a significant reduction in MAPE, MAE, and MSE compared with the LSTM model and the SVM model, which verifies the effectiveness of the proposed algorithm.
Highlights
College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China; Abstract: When ships sail on the sea, the changes of ship motion attitude presents the characteristics of nonlinearity and high randomness
Zhang et al proposed a neural network prediction model based on adaptive dynamic particle swarm optimization algorithm and bidirectional long short-term memory network (Bi-long shortterm memory network (LSTM)), the experimental results show that it has good prediction performance in the field of ship motion attitude prediction, but the model only used single input, ignoring the wind speed, wind direction, ship roll angle acceleration, and other data that can affect the prediction results, and the proposed model can not extract the interdependence between multiple input variables [40]
temporal pattern attention mechanism (TPA) is located in the back of the Bi-LSTM, which takes the output features of Bi-LSTM network as the input, extracts the deep information contained in the state of single feature at all sampling times, and focus on temporal patterns that are good for forecasting
Summary
When ships sail on the sea, due to the influence of the complex marine environment such as strong wind and sea waves, they sway irregularly. The artificial neural network is one of the most commonly used methods for time series prediction, which can learn network weights from dataset and has stronger complex nonlinear expression ability It is widely used in the fields of rainfall prediction, wind speed prediction, photovoltaic power generation prediction, and has achieved good results. G. Zhang et al proposed a neural network prediction model based on adaptive dynamic particle swarm optimization algorithm and Bi-LSTM, the experimental results show that it has good prediction performance in the field of ship motion attitude prediction, but the model only used single input, ignoring the wind speed, wind direction, ship roll angle acceleration, and other data that can affect the prediction results, and the proposed model can not extract the interdependence between multiple input variables [40]. The validity of the proposed algorithm is verified by comparing with the single LSTM network and the traditional machine learning prediction model with the real ship data
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