Neural-network-based modelling and analysis for time series prediction of ship motion

Abstract

This paper presents a data-driven model for time series prediction of ship motion. Prediction based on past time series of data is a powerful function in modern ship support systems. For a large amount of ship sensor data, neural network (NN) is considered as a proper tool in modelling the prediction system. Efforts are made to compact the NN structure through sensitivity analysis, in which the importance of each input to the output is quantified and lower ranked inputs are eliminated. Further analysis about the impact of three different learning strategies, i.e. offline, online and hybrid learning on the NN, is conducted. The hybrid learning combining the advantages of both the offline learning and the online learning exhibits superior prediction performance. According to the long-term prediction ability of recurrent NN, multi-step-ahead prediction under the hybrid learning strategy is realised in a multi-stage prediction form. Experiments are carried out using collected ship sensor data on a vessel. The results show the feasibility of generating a data-driven model through modelling and analysis of the NN for ship motion prediction.