Journal of Applied Mathematics

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

Wenjun Zhang and Zhengjiang Liu

Full-text: Open access

Abstract

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.

Article information

Source
J. Appl. Math., Volume 2014 (2014), Article ID 176297, 7 pages.

Dates
First available in Project Euclid: 1 October 2014

Permanent link to this document
https://projecteuclid.org/euclid.jam/1412177811

Digital Object Identifier
doi:10.1155/2014/176297

Citation

Zhang, Wenjun; Liu, Zhengjiang. Real-Time Ship Motion Prediction Based on Time Delay Wavelet Neural Network. J. Appl. Math. 2014 (2014), Article ID 176297, 7 pages. doi:10.1155/2014/176297. https://projecteuclid.org/euclid.jam/1412177811


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