Ship Rolling Prediction Based on Gray RBF Neural Network

Li Sang Liu; Xia Fu Peng; Jie Hua Zhou

doi:10.4028/www.scientific.net/AMM.48-49.1044

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 48-49Ship Rolling Prediction Based on Gray RBF Neural...

Ship Rolling Prediction Based on Gray RBF Neural Network

Abstract:

To enhance the ship’s seaworthiness and seakeeping capacity, a new prediction algorithm based on Gray RBF neural network is presented to forecast roll motion accurately. The second-order gray model GM(2,1) and RBF network are introduced firstly, then using AGO (accumulated generating operation) to weaken randomness and volatility of raw data, which would affect the accuracy of RBF network. On the other hand, the algorithm flow of GMRBF(2,1) is given. Further more, GMRBF(2,1) is applied in a sample of ship roll sequence and effectively improves large prediction error of second-order gray model. The simulation results prove that the new model is more accurate and stabilizer than traditional models.

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Periodical:

Applied Mechanics and Materials (Volumes 48-49)

Pages:

1044-1048

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.48-49.1044

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Online since:

February 2011

Authors:

Li Sang Liu, Xia Fu Peng, Jie Hua Zhou

Keywords:

GMRBF(2,1), Gray Model, Prediction, RBF, Ship Rolling Sequence

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References

[1] Ou Xusheng, Chen Wan-er, in: The application of fuzzy RBF neural network in time series' modeling [J]. Guangdong Automation & Information Engineering, 1999, (1): 25-28.

Google Scholar

[2] Wang Shujuan, in: The prediction of nonlinear ship motion based on multi-varble grey model MGM(1, n). Harbin Engineering University, (2007).

Google Scholar

[3] Shen Jihong, in: Functional transformation GM(1, 1 ) model constructed for ship pitching [J]. Journal of Harbin Institute of Technology. 2001, 33(3): 291-294.

Google Scholar

[4] Liu Sifeng, Dang Yaoguo, Zhang Qishan, in: Gray system theory and its application [M]. BEIJING: publishing House of Science, (1991).

Google Scholar

[5] Ge Zhexue, Sun Zhiqiang, in: Neural network theory and MATLAB application [M]. BEIJING: publishing House of Electronics Industry, (2007).

Google Scholar

[6] Fan Chunling, Zhang Jing, Jin Zhihua, Tian Weifeng, in: Novel modeling method for grey RBF neural network and its application [J]. Systems Engineering and Electronics, 2005, 27(2): 316-319.

DOI: 10.1109/icarcv.2002.1235006

Google Scholar

[7] Zhang Yunli，Yang Zhenshan, in: Grey RBF neural network based forecasting of outpatient capacity in modern hospital [J]. Computer Engineering and Applications, 2010, 46(29): 225-228.

Google Scholar