Ship track prediction based on Bayesian optimization in temporal convolutional networks
[Objective]As the traditional ship trajectory prediction method is prone to gradient explosion and long calculation time,this paper seeks to improve its accuracy and calculation efficiency by proposing a ship trajectory prediction model based on an improved Bayesian optimization algorithm(IBOA)and temporal convolution network(TCN).[Method]A temporal pattern attention(TPA)mechanism is introduced to ex-tract the weights of each input feature and ensure the timing of the historical flight track data.At the same time,a reversible residual network(RevNet)is introduced to reduce the memory occupied by TCN model training.The IBOA is then used to find the optimality of the hyperparameters in the TCN(size of kernel K,ex-pansion coefficient d).The model is finally validated using a five-fold cross-validation method,and trajectory prediction is carried out after obtaining the optimal model.[Result]The trajectory data is collected by auto-matic identification system(AIS)and verified.The root mean square error(RMSE)is found to be increased by 5.5×10-5,3.5×10-4 and 6×10-4 in weak coupling,medium coupling and strong coupling track prediction respec-tively.[Conclusion]The proposed network has good adaptability to complex trajectories and higher accur-acy than the traditional model and long short-term memory(LSTM)model,while maintaining high prediction accuracy for trajectories with strong coupling.
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