Simplifying neural network based model for ship motion prediction: a comparative study of sensitivity analysis

• Published in: Volume 1: Offshore Technology
• Main Authors: Cheng, Xu, Chen, Shengyong, Diao, Chen, Liu, Mengna, Li, Guoyuan, Zhang, Houxiang
• Format: Book Part
• Language: English
• Published: American Society of Mechanical Engineers (ASME) 2017
• Subjects: Arctic
• Online Access: http://hdl.handle.net/11250/2495409; https://doi.org/10.1115/OMAE2017-61474

This paper presents a comparative study of sensitivity analysis (SA) and simplification on artificial neural network (ANN) based model used for ship motion prediction. Considering traditional structural complexity of ANN usually results in slow convergence, SA, as an efficient tool for correlation analysis, can help to reconstruct the ANN model for ship motion prediction. An ANN-Garson method and an ANN-EFAST method are proposed, both of which utilize the ANN for modeling but select the input parameters in a local and a global fashion, respectively. Through the benchmark tests, ANN-EFAST exhibits superior performance in both linear and nonlinear systems. Further test on ANN-EFAST via a case study of ship heading prediction shows its cost-effective and timely in compacting the ANN based prediction model. publishedVersion Copyright © 2017 by ASME