用于在任意深度和航线上预测船舶磁标的神经网络:与多极极模型进行比较
Kajetan Zielonacki1, Jarosław Tarnawski2, Miroslaw Woloszyn2
1Faculty of Electrical and Control Engineering, Gdansk University of Technology, Narutowicza 11/12, 80-233, Gdansk, Poland. kajetan.zielonacki@pg.edu.pl.
一个新的神经网络模型准确地预测了船只的磁场,为传统的减磁和风险评估方法提供了更快,更强大的替代方案.
科学领域:
- 海军建筑和海洋工程
- 计算物理 计算物理
- 人工智能的人工智能
背景情况:
- 传统的用于预测船舶磁场的多极极模型是计算密集的,对位置错误敏感.
- 对船舶磁场的准确预测对于脱磁和运营风险评估至关重要.
研究的目的:
- 开发和评估一种用于预测船舶磁场分布的新型神经网络模型.
- 为了比较神经网络模型与多双极方法的性能.
主要方法:
- 通过有限元法 (FEM) 模拟生成的合成数据训练了一个神经网络模型.
- 贝叶斯优化被用来微调神经网络的架构和超参数.
- 使用各种指标评估模型准确性,将预测与多双极模型在扰乱定位数据下进行比较.
主要成果:
- 神经网络模型的预测准确度与多双极模型的预测准确度相似或更高.
- 神经网络模型对船舶定位数据中的错误具有显著更大的稳定性,质量指数的相对恶化降低了6-7倍.
- 神经网络模型与多双极方法相比,可以大幅降低计算成本.
结论:
- 开发的神经网络模型为预测船舶磁场分布提供了更快,更强大和更准确的方法.
- 这种方法非常适合用于实践应用,如船舶去和海上风险评估.
- 虽然需要更多的数据以获得最佳准确性,但神经网络模型对现有方法来说是一个显著的进步.
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