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.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

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


    Contributors:
    Li, Guoyuan (author) / Kawan, Bikram (author) / Wang, Hao (author) / Zhang, Houxiang (author)

    Published in:

    Publication date :

    2017-01-02


    Size :

    10 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Research on Ship Swaying Motion Prediction Based on Multi-Variable Chaotic Time Series Analysis

    Yang, Xin Dong ;Wang, Zuo Chao ;Shi, Ai Guo | Trans Tech Publications | 2013


    Motion Perception of Ship-Ship Unmanned System Based on LSTM Neural Network

    Tang, Haoyun / Meng, Jiayi / Ren, Deyuan et al. | IEEE | 2024



    Robust Time Series Prediction of Neural Network

    He, P.-l. / Hou, Y.-x. / Chang, H. et al. | British Library Online Contents | 2001