Designing Back Propagation Neural Network for Ship Seakeeping Investigations

Mohsen Khosravi Babadi¹ and Hassan Ghassemi^1,

¹Department of Ocean Engineering, AmirKabir University of Technology, Tehran, Iran

Cite this paper:
Mohsen Khosravi Babadi and Hassan Ghassemi. Designing Back Propagation Neural Network for Ship Seakeeping Investigations. American Journal of Mechanical Engineering. 2014; 2(1):21-27. doi: 10.12691/ajme-2-1-4

Abstract

In recent years, there has been more attention to predict the behavior of vessel in the sea (sea keeping). The more the speed of vessel increases in the high speed and light vessels, the more calculations are necessary. In this paper, a BP (back propagation) neural network is presented that keeps sea keeping indexes under the categories of input and output of the network. Evaluation is based on a corvette model, and the stability parameter of wave has been evaluated by using MATLAB software. Comparison between the network output values and the expected values represent the amount of error, which is negligible, indicating that assessing the wave’s stability values is possible via using (BP) back propagation neural network.

Keywords:
sea-keeping analysis back-propagation neural networks

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