Real-time ship motion prediction based on adaptive wavelet transform and dynamic neural network

Abstract

Ship motion attitude is the time series data with highly nonlinear and non-stationary characteristics significantly affected by environmental factors. To accurately predict the ship motion attitude in real-time, a ship motion attitude prediction model based on the Adaptive Discrete Wavelet Transform Algorithm (ADWT) and the space-time Residual Recurrent Neural Network (RRNN) with a time-varying structure is proposed. The ADWT decomposes the original data into components easier to predict, each element in the optimal component combination is predicted using the RRNN, the structure and parameters of which are adjusted in real-time with the sliding of data window. The model performance tests are conducted based on the simulation data of the ship motion of DTMB5415. The results show that the subsequences decomposed by the ADWT are easier to predict. Compared with other prediction models, the prediction accuracy of the ADWT-RRNN is the highest under all working conditions, its prediction accuracy and stability of it do not fluctuate significantly over a long prediction period. Hence, the more severe the sea states, the more pronounced the performance advantage is over other models. Finally, a reliable and efficient tool is eventually provided for real-time and accurate prediction of ship motion.