用统计学习理论优化结构健康监测模型选择，提升泛化能力
On the use of Statistical Learning Theory for model selection in Structural Health Monitoring
- 基于统计学习理论构建模型选择框架
- 融合领域知识使风险上限降低，泛化性能更好
- 适合关注模型可靠性与工程应用的学者
在工程应用中使用数据驱动系统时，确定最优统计表征始终面临模型选择问题。本文聚焦结构健康监测（SHM）中模型的泛化能力。尽管该领域通常采用经验性方法进行统计模型验证，但可通过统计学习理论（SLT）提供的界对泛化性能进行更严格的估计。本文从SLT视角探索了核平滑器在建模线性振子脉冲响应过程中的选择问题。结果表明，将领域知识融入回归任务可降低保证风险，从而提升模型泛化能力。
原文摘要 · Abstract (English)
Whenever data-based systems are employed in engineering applications, defining an optimal statistical representation is subject to the problem of model selection. This paper focusses on how well models can generalise in Structural Health Monitoring (SHM). Although statistical model validation in this field is often performed heuristically, it is possible to estimate generalisation more rigorously using the bounds provided by Statistical Learning Theory (SLT). Therefore, this paper explores the selection process of a kernel smoother for modelling the impulse response of a linear oscillator from the perspective of SLT. It is demonstrated that incorporating domain knowledge into the regression problem yields a lower guaranteed risk, thereby enhancing generalisation.
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