The invention discloses a PSO-LSTM-based ship motion prediction method, electronic equipment and a storage medium, and belongs to the technical field of ship engineering. In order to solve the problems of poor prediction precision and low efficiency of a traditional ship motion prediction mode, the method comprises the following steps: constructing a ship motion model, and then collecting ship motion data through simulation; after data cleaning and normalization processing are carried out, the obtained data are divided into a training set and a test set; building an LSTM network, setting structural parameters of the LSTM network, and inputting the obtained training set and test set into the LSTM network for training in a window sliding prediction mode to obtain a trained LSTM network; and carrying out optimization on related hyper-parameters in the network structure by adopting a particle swarm optimization algorithm to obtain an optimal value of the related hyper-parameters of the LSTM network and the optimized LSTM network, and then testing the optimized LSTM network by adopting the obtained test set. The method is accurate in prediction.
一种基于PSO‑LSTM的船舶运动预测方法、电子设备及存储介质,属于船舶工程技术领域。为解决船舶运动传统预测方式预测精度差,效率低的问题,本发明包括构建船舶运动模型,然后通过仿真采集船舶运动数据;进行数据清洗,归一化处理后,对得到的数据进行划分为训练集和测试集;搭建LSTM网络,设置LSTM网络的结构参数,然后将得到的训练集和测试集采用窗口滑动的预测方式,输入到LSTM网络中进行训练,得到训练后的LSTM网络;采用粒子群优化算法对网络结构中的相关超参数进行寻优,得到LSTM网络相关超参数的最优值及优化后的LSTM网络,然后采用得到的测试集对优化后的LSTM网络进行测试。本发明预测准确。
PSO-LSTM-based ship motion prediction method, electronic equipment and storage medium
一种基于PSO-LSTM的船舶运动预测方法、电子设备及存储介质
22.10.2024
Patent
Elektronische Ressource
Chinesisch
IPC: | B63B Schiffe oder sonstige Wasserfahrzeuge , SHIPS OR OTHER WATERBORNE VESSELS |
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