Real-Time Ship Motion Prediction Based on Time Delay Wavelet Neural Network | Zendy

Wenjun Zhang | Zendy; Zhengjiang Liu | Zendy

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Real-Time Ship Motion Prediction Based on Time Delay Wavelet Neural Network

Author(s) -

Wenjun Zhang,

Zhengjiang Liu

Publication year - 2014

Publication title -

journal of applied mathematics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.307

H-Index - 43

eISSN - 1687-0042

pISSN - 1110-757X

DOI - 10.1155/2014/176297

Subject(s) - artificial neural network , autoregressive model , computer science , generalization , dimension (graph theory) , nonlinear system , sensitivity (control systems) , autoregressive–moving average model , wavelet , control theory (sociology) , time series , algorithm , artificial intelligence , mathematics , machine learning , engineering , statistics , mathematical analysis , physics , control (management) , quantum mechanics , electronic engineering , pure mathematics

A wavelet neural network with time delay is proposed based on nonlinear autoregressive model with exogenous inputs (NARMAX) model, and the sensitivity method is applied in the selection of network inputs. The inclusion of delayed system information improves the network’s capability of representing the dynamic changes of time-varying systems. The implement of sensitivity analysis reduces the dimension of input as well as the dimension of networks, thus improving its generalization ability. The time delay wavelet neural network was implemented to real-time ship motion prediction, simulations are conducted based on the measured data of vessel “YUKUN,” and the results demonstrate that the feasibility of the proposed method

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