Ship motion prediction is applied to the shipboard stabilized platform to keep the equipment on the platform stable all the time, which is of great practical significance to the safety and efficiency of shipboard equipment operation. Long Short-term Memory (LSTM) Network is a classic time series prediction method that has made remarkable achievements in this field. However, the dynamic frequency range of single LSTM in ship motion prediction is insufficient to meet the stabilized platform with higher precision requirements. To improve the performance of LSTM in ship motion prediction, this paper presents a novel method named as multiscale attention-based LSTM. At first, wavelet transform is employed to decompose ship motion signals into several frequency scales, which makes LSTM to capture the inherent law of ship motion from each frequency scale. And then the weights of different scales are obtained by attention mechanism, which promote the sensitivity of the whole system by paying more attention to significant information and suppress the interference of noise signals. Both of the steps form a multiscale attention mechanism, which promote the adaptability and improve the performance of the LSTM. In addition, to avoid being trapped in local optimization, the two-stage training mechanism is designed for model training based on the model structure. Ship motion data are used to evaluate the feasibility and effectiveness. The experiments show that the proposed method achieves better performance compared with other popular methods.

将船舶运动预测应用于舰载稳定平台，使平台上的设备始终保持稳定，这对舰载设备运行的安全性和效率具有重要的现实意义。长短期记忆（LSTM）网络是一种经典的时间序列预测方法，在该领域取得了显著成就。但是，船舶运动预测中单个LSTM的动态频率范围不足以满足稳定平台对更高精度的要求。为了提高LSTM在舰船运动预测中的性能，本文提出了一种新的方法，称为多尺度基于注意力的LSTM。首先，采用小波变换将船舶运动信号分解为几个频率标度，这使得LSTM可以从每个频率标度中捕获船舶运动的固有定律。然后通过注意机制获得不同尺度的权重，通过更多地关注重要信息来提高整个系统的灵敏度，并抑制噪声信号的干扰。这两个步骤都形成了多尺度注意机制，从而提高了LSTM的适应性并提高了其性能。另外，为避免陷入局部优化中，基于模型结构设计了两阶段的训练机制进行模型训练。船舶运动数据用于评估可行性和有效性。实验表明，与其他常用方法相比，该方法具有更好的性能。通过更多地关注重要信息来提高整个系统的灵敏度，并抑制噪声信号的干扰。这两个步骤都形成了多尺度注意机制，从而提高了LSTM的适应性并提高了其性能。另外，为避免陷入局部优化中，基于模型结构设计了两阶段的训练机制进行模型训练。船舶运动数据用于评估可行性和有效性。实验表明，与其他常用方法相比，该方法具有更好的性能。通过更多地关注重要信息来提高整个系统的灵敏度，并抑制噪声信号的干扰。这两个步骤都形成了多尺度注意机制，从而提高了LSTM的适应性并提高了其性能。另外，为避免陷入局部优化中，基于模型结构设计了两阶段的训练机制进行模型训练。船舶运动数据用于评估可行性和有效性。实验表明，与其他常用方法相比，该方法具有更好的性能。为了避免陷入局部优化中，基于模型结构设计了两阶段的训练机制进行模型训练。船舶运动数据用于评估可行性和有效性。实验表明，与其他常用方法相比，该方法具有更好的性能。为了避免陷入局部优化中，基于模型结构设计了两阶段的训练机制进行模型训练。船舶运动数据用于评估可行性和有效性。实验表明，与其他常用方法相比，该方法具有更好的性能。