Due to the random nature of ship motion in an open sea environment, the ship related maritime operations such as landing on an aircraft carrier, ship-borne helicopter recovery, cargo transfer between ships and so on, are usually very difficult. An accurate prediction of the motion will improve the operation safety and efficiency on board ships. This paper presents a research on the application of artificial neural network methods in the short-time prediction of ship pitching motion. The radial basis function (RBF) neural network is applied to develop a model for short-time prediction of ship pitching motion, and the other two kinds of artificial neural networks, i.e., back propagation (BP) neural network, Elman neural network are also applied for the same purpose. A comparative analysis among them is presented. It is shown that RBF neural network provides a more effective and accurate tool for predicting the ship pitching motion.
Short-Term Prediction of Ship Pitching Motion Based on Artificial Neural Networks
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Huang, B, & Zou, Z. "Short-Term Prediction of Ship Pitching Motion Based on Artificial Neural Networks." Proceedings of the ASME 2016 35th International Conference on Ocean, Offshore and Arctic Engineering. Volume 7: Ocean Engineering. Busan, South Korea. June 19–24, 2016. V007T06A007. ASME. https://doi.org/10.1115/OMAE2016-54317
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