.png){kind=icon}
返回
A self-optimised informer-based model for nonlinear ship roll motion forecasting
.png){kind=icon}
PRE
AI
DOI：10.1080/23249935.2025.2606152.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Ship motion exhibits strong nonlinearity due to the combined effects of wind, waves, and currents, which brings difficulties to ship motion forecasting. To overcome the above challenges, this paper attempts to propose a novel nonlinear forecasting approach for ship motion. Firstly, focusing on the nonlinear characteristics of ship motion, an instantaneous input data encoder is designed, and an Instantaneous-Informer (I-Informer) is established based on the Informer network. Then, targeting the hyper-parameter optimisation problem of the I-Informer and focusing on the inherent defects of the Pelican Optimisation Algorithm (POA), a new quantum POA (QPOA) is proposed to construct a hyper-parameter optimisation method for the I-Informer. Finally, based on the established I-Informer and the proposed QPOA, a novel ship motion forecasting approach is proposed, namely I-Informer_QPOA. Subsequently, rolling data of a C11 container ship in irregular waves is used to test the performance of the new approach. Through example analysis, this paper discusses the recommended value ranges of output length, input and output ratio, and hyper-parameter for the I-Informer, and conducts comparative tests within the recommended value ranges. The experimental results demonstrate that the established I-Informer can obtain more accurate forecasting results than the model selected in this paper. In addition, the proposed QPOA can provide a better and more stable hyper-parameter combination for the I-Informer compared with the algorithm selected in this paper.
Keyword:
Ship motion forecasting
Nonlinear dynamical system
Deep learning model
Informer
Pelican optimisation algorithm
期刊
IF:
3.1
论文数:
941
被引数:
2.2K
机构
引用论文
The Sailfish Optimizer: A novel nature-inspired metaheuristic algorithm for solving constrained engineering optimization problemsSailfish优化器: 一种新颖的自然启发的元启发式算法，用于解决约束工程优化问题
PREAI
分享收藏Dynamic Positional Attention Fusion (DPAF): Adaptive Encoding and Weighted Attention for Ship Motion Attitude Prediction.png)
IEEE SENSORS JOURNAL
IEEE SENSORS JOURNALIF4.5
PREAI
分享收藏Multipoint Heave Motion Prediction Method for Ships Based on the PSO-TGCN Model基于PSO-TGCN模型的船舶多点垂向运动预测方法
PREAI
分享收藏Dynamic chaos unveiled: enhancing ship’s attitude time series prediction through spatiotemporal embedding and improved transformer model动态混沌的揭示：通过时空嵌入和改进的Transformer模型提升船舶姿态时间序列预测
PREAI
分享收藏
查看更多内容