Researchers have been investigating data-driven modeling as a key way to achieve ship intelligence for years. This paper presents a novel data analysis approach to data-driven modeling of ship motion. We propose a global sensitivity analysis (GSA) approach combining artificial neural network (ANN) and sparse polynomial chaos expansion (SPCE) techniques to accommodate high-dimensional sensor data collected from ship motion. An ANN is constructed as a surrogate model to associate ship sensor data with a certain type of ship motion. To account for the computational efficiency of GSA, an SPCE is integrated into the GSA to decrease the need for Monte Carlo (MC) samples generated by the ANN. A probe variable is designed to couple with the MC samples, which plays a role in determining the degree of convergence of variable importance. A test on benchmark function demonstrates the efficiency and accuracy of the proposed approach. A case study of ship heading with and without environment effects is conducted. The experimental results show that the proposed approach can identify and rank the most sensitive factors of ship motion. The proposed approach highlights the application of GSA in data-driven modeling for ship intelligence.

中文翻译：

---

基于神经网络的船舶运动数据驱动建模灵敏度分析方法

多年来，研究人员一直在研究数据驱动建模作为实现船舶智能的关键方法。本文提出了一种新的数据分析方法，用于船舶运动的数据驱动建模。我们提出了一种结合人工神经网络 (ANN) 和稀疏多项式混沌扩展 (SPCE) 技术的全局灵敏度分析 (GSA) 方法，以适应从船舶运动收集的高维传感器数据。ANN 被构建为代理模型，以将船舶传感器数据与某种类型的船舶运动相关联。为了考虑 GSA 的计算效率，将 SPCE 集成到 GSA 中以减少对由 ANN 生成的蒙特卡罗 (MC) 样本的需求。探针变量被设计为与 MC 样本耦合，它在确定变量重要性的收敛程度中起作用。对基准函数的测试证明了所提出方法的效率和准确性。进行了有和没有环境影响的船舶航向案例研究。实验结果表明，该方法可以对船舶运动最敏感的因素进行识别和排序。所提出的方法突出了 GSA 在船舶智能数据驱动建模中的应用。