Paper Title:
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.

  Info
Periodical
Edited by
Zhixiang Hou
Pages
1044-1048
DOI
10.4028/www.scientific.net/AMM.48-49.1044
Citation
L. S. Liu, X. F. Peng, J. H. Zhou, "Ship Rolling Prediction Based on Gray RBF Neural Network", Applied Mechanics and Materials, Vols. 48-49, pp. 1044-1048, 2011
Online since
February 2011
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