论统计学习理论在结构健康监测模型选择中的应用
On the use of Statistical Learning Theory for model selection in Structural Health Monitoring
AI总结：
针对结构健康监测中模型选择依赖启发式验证的问题，本文从统计学习理论角度研究核平滑器选择，证明融入领域知识可降低保证风险、提升模型泛化能力。
摘要：
在工程应用中采用基于数据的系统时，定义最优统计表示会面临模型选择问题。本文聚焦于结构健康监测（SHM）中模型的泛化能力。尽管该领域的统计模型验证通常采用启发式方法，但利用统计学习理论（SLT）提供的界可以更严格地估计泛化能力。因此，本文从SLT的角度探讨了用于线性振荡器脉冲响应建模的核平滑器的选择过程。研究表明，将领域知识融入回归问题可得到更低的保证风险，从而提升泛化能力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
