Real-time Prediction of Parametric Roll Motion via Power-activation Feed-forward Neural Network with Model Experiment Data

Xin Li, Ning Ma, QiQi Shi, X. Gu
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Abstract

The Power-activation Feed-forward Neural Network (PFN) is used to achieve real-time prediction of the ship’s parametric roll motion. The theoretical rationality of real-time prediction based on the ship’s rolling motion time series data is verified. Sequence-to-Sequence models are proposed and used to compare the PFN model, Long Short-Term Memory model, and Convolutional Neural Network. Three different groups of model experiment data are used for comparison. Results show that PFN has advantages in real-time prediction of parametric roll motion due to its time-varying weight adjustment methods, with a more effective mapping mode, higher accuracy, and shorter computing time.
利用模型试验数据,通过功率激活前馈神经网络对参数滚动运动进行实时预测
利用功率激活前馈神经网络(PFN)实现了对船舶参数滚动运动的实时预测。验证了基于船舶滚动运动时间序列数据的实时预测的理论合理性。提出了序列到序列模型,并用于比较 PFN 模型、长短期记忆模型和卷积神经网络。比较使用了三组不同的模型实验数据。结果表明,PFN 因其时变权重调整方法而在参数滚动运动的实时预测方面具有优势,其映射模式更有效、精度更高、计算时间更短。
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