文献类型：期刊文献

英文题名：Multiscale attention-based LSTM for ship motion prediction

作者：Zhang, Tao[1,2];Zheng, Xiao-Qing[1,2];Liu, Ming-Xin[3]

机构：[1]Yan Shan Univ, Sch Informat Sci & Engn, Qinhuangdao 066004, Hebei, Peoples R China;[2]Hebei Key Lab Informat Transmiss & Signal Proc, Qinhuangdao 066004, Hebei, Peoples R China;[3]Guangdong Ocean Univ, Coll Elect & Informat Engn, Zhanjiang 524088, Peoples R China

年份：2021

卷号：230

外文期刊名：OCEAN ENGINEERING

收录：SCI-EXPANDED(收录号：WOS:000656923300035)、、EI(收录号：20211810306963)、Scopus(收录号：2-s2.0-85105081855)、WOS

基金：This work is financially supported by the National Natural Science Foundation of China (under Grants 61871465), the Equipment Advance Research Joint Technology Project Foundation of 13th Five-year Plan (under Grants 41412040302) and the Natural Science Foundation of Hebei Province (under Grants F2020203010).

语种：英文

外文关键词：Ship motion prediction; Long short-term memory; Multiscale; Attention mechanism; Two-stage training mechanism

外文摘要：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.