论文标题
预测神经网络半熟悉的涌现和激增运动
Predicting heave and surge motions of a semi-submersible with neural networks
论文作者
论文摘要
容器或浮动平台的实时运动预测可以帮助提高运动补偿系统的性能。它还可以为在运动中至关重要的离岸操作提供有用的早期训练信息。在这项研究中，开发了长期的短期记忆（LSTM）的机器学习模型，以预测半熟悉的涌现和激增运动。培训和测试数据来自在中国上海若昂大学的深水海洋盆地进行的模型测试。将运动和测得的波馈入LSTM细胞中，然后通过完全连接的服务（FC）层以获得预测。借助测得的波，预测将46.5 s扩展到未来，平均精度接近90％。使用噪声扩展的数据集，训练有素的模型可有效地工作，噪声级别高达0.8。作为另一个步骤，模型只能根据运动本身预测运动。基于对模型架构的敏感研究，提出了机器学习模型的构建指南。提出的LSTM模型显示出强大的预测血管波兴奋运动动作的能力。
Real-time motion prediction of a vessel or a floating platform can help to improve the performance of motion compensation systems. It can also provide useful early-warning information for offshore operations that are critical with regard to motion. In this study, a long short-term memory (LSTM) -based machine learning model was developed to predict heave and surge motions of a semi-submersible. The training and test data came from a model test carried out in the deep-water ocean basin, at Shanghai Jiao Tong University, China. The motion and measured waves were fed into LSTM cells and then went through serval fully connected (FC) layers to obtain the prediction. With the help of measured waves, the prediction extended 46.5 s into future with an average accuracy close to 90%. Using a noise-extended dataset, the trained model effectively worked with a noise level up to 0.8. As a further step, the model could predict motions only based on the motion itself. Based on sensitive studies on the architectures of the model, guidelines for the construction of the machine learning model are proposed. The proposed LSTM model shows a strong ability to predict vessel wave-excited motions.
