NDLI: The ship motion prediction approach based on BP neural network to identify Volterra series kernels

Content Provider	IEEE Xplore Digital Library
Author	Xiuyan Peng Zhiguo Men Xingmei Wang Shuli Jia
Copyright Year	2014
Description	Author affiliation: Econ. & Manage. Coll., Harbin Eng. Univ., Harbin, China (Zhiguo Men) \|\| Autom. Coll., Harbin Eng. Univ., Harbin, China (Xiuyan Peng; Shuli Jia) \|\| Comput. Sci. & Technol. Coll., Harbin Eng. Univ., Harbin, China (Xingmei Wang)
Abstract	Ship motion prediction plays a prominent role in the whole ship motion process. This paper presents a new approach for ship motion prediction. In order to obtain more effective prediction result, the paper studied the BP neural network and Volterra series model, and the chaos characteristics of ship motion time series. A novel method of single-output three-layer BP neural network to identify Volterra series kernels is proposed. Multi-step prediction for the roll motion time series of ship at 135° with the method is accomplished. The simulation analysis demonstrate that the ship motion prediction approach based on BP neural network to identify Volterra series kernels has higher precision, longer prediction time, effectiveness and adaptability, and it can predict the ship motion exactly.
Starting Page	2324
Ending Page	2328
File Size	207518
Page Count	5
File Format	PDF
ISBN	9781479937073
e-ISBN	9781479937080
DOI	10.1109/CCDC.2014.6852560
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-31
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Multi-step Prediction BP neural network Volterra series Ship motion Prediction Time series analysis Artificial neural networks Predictive models Educational institutions Marine vehicles Kernel
Content Type	Text
Resource Type	Article

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