Prediction for TCN-BiLSTM Ship Trajectory Based on Attention Mechanism
To address the problem of low prediction accuracy in existing ship trajectory prediction model,a ship trajectory predic-tion model based on attention mechanism time-domain convolutional network and bidirectional long-short memory network is proposed Firstly,the temporal convolutional network(TCN)network is constructed to extract the sequence features of ship trajectories.Then,attention mechanism is introduced into the network to adjust the weights of different attribute features,highlighting greater in-fluence on the trajectory prediction.Finally,the bi-directional long short-term memory(Bi-LSTM)network is constructed to learn the pre and post situation of trajectory sequences to extract more information from the sequences,achieving the prediction of future ship trajectories;Training and testing experiments are conducted on the network by using actual ship automatic identification system(AIS)data.The experimental results show that compared to the LSTM,Bi-LSTM and BiLSTM-Attention models,the TCN-ABiL-STM model has higher accuracy and better fit in predicting ship trajectories.which verifyes the effectiveness and practicality of the proposed TCN-ABiLSTM model in predicting ship trajectories.
万方数据
维普
