Multi-step motion attitude prediction method of USV based on CNN-GRU model
Aiming at the problem of low accuracy of single model in predicting the ship motion of Unmanned Surface Vehicle(USV),a multi-step prediction model based on Convolutional Neural Network(CNN)and Gate Recurrent Unit(GRU)is proposed.Firstly,the sliding window method is used to construct the motion data set as the model input.Then,the CNN module is used to mine the local features of time series data.Finally,the GRU network is used for multi-step predic-tion.The experimental results show that the model has higher prediction accuracy than XGBoost model,single LSTM model and single GRU model,and its performance of each evaluation index is better,which has important application value.
万方数据
维普
