Intelligent Prediction for Oscillation Motion of Ships in Waves Based on Convolutional Neural Network
It is very important to accurately predict the ship's oscillating motion in waves to ensure the ship's navigation safety and normal operation.A convolutional neural network(CNN)is applied to predict the heave-pitch coupled motion of ships in waves.The ship heave-pitch coupled motion under different regular wave excitations are analyzed,and the prediction results of the CNN and long short term memory(LSTM)neural networks are compared to verify the prediction performance of the CNN model.Secondly,the ship's irregular heave-pitch coupled motion response under the excitation of the white noise spectrum and the JONSWAP spectrum is obtained by numerical simulation.The CNN model is used to learn the constructed training set,and the test set is predicted.The prediction results of CNN and LSTM are compared to verify the prediction performance of CNN in a ship's rocking motion in irregular waves.The results show that CNN and LSTM neural networks have the same level of prediction accuracy,which can accurately predict the ship's heave and pitch coupling motion in waves.
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