采用机制-数据双驱动框架的预训练多步骤船舶运动预测信息器

IF 7.5 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS Engineering Applications of Artificial Intelligence Pub Date : 2024-10-30 DOI:10.1016/j.engappai.2024.109523
Wenhe Shen , Xinjue Hu , Jialun Liu , Shijie Li , Hongdong Wang
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引用次数: 0

摘要

随着自主海上水面舰艇的发展,决策、运动规划和实时控制任务更加需要精确、快速的多步骤船舶运动预测。本文提出了一种基于 Informer 的多步预测方法,采用预训练策略实现精确、快速的船舶运动预测,该方法以生成推理代替滚动预测,避免了因时间跨度增大而产生的累积误差。由于长期控制行动和短期状态序列的时间特征不同,编码器和解码器的异构输入设计分别捕获它们的信息,而不会出现信息冗余。为解决真实数据获取成本高和深度学习方法对数据需求大之间的瓶颈,我们提出了机制-数据双驱动框架。该框架利用先验机制模型生成虚拟数据,其中包含根据自由运行模型测试结果设计的一系列激励信号。为了减少对真实数据的需求并提高可解释性,改进后的 Informer 在使用真实数据进行训练之前,先使用来自机构模型的虚拟数据进行预训练。我们对多步骤船舶运动预测的实验表明,与最先进的方法和经典方法相比,所提出的方法平均误差和时间分别减少了 41.36% 和 13.20%。
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A pre-trained multi-step prediction informer for ship motion prediction with a mechanism-data dual-driven framework
The advancement of autonomous maritime surface ships has increased the need for accurate and rapid multi-step prediction of ship motion for decision-making, motion planning, and real-time control tasks. This paper proposes a multi-step prediction method based on Informer with a pre-trained strategy to achieve accurate and fast motion prediction for ships, which substitutes generative inference for rolling prediction to avoid the cumulative error caused by the increasing time horizon. Due to the difference in temporal features from long-term control actions and short-term state sequences, heterogeneous inputs of encoder and decoder are designed to respectively capture their information without information redundancy. To address the bottleneck between the high cost of real data acquisition and the high demand for deep learning methods for data, we propose a mechanism-data dual-driven framework. This framework utilizes a prior mechanism model to generate virtual data incorporating a range of excitation signals designed in accordance with the results of free-running model tests. To reduce the need for real data and increase interpretability, the improved Informer is pre-trained by virtual data from the mechanism model before being trained by real data. Our experiments for multi-step ship motion prediction demonstrate that the proposed method respectively reduces the error and time to 41.36% and 13.20% on average compared to state-of-the-art and classical methods.
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来源期刊
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence 工程技术-工程:电子与电气
CiteScore
9.60
自引率
10.00%
发文量
505
审稿时长
68 days
期刊介绍: Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.
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