论文标题
基于多阶段模型的最佳实验设计，用于非线性估计
Robust multi-stage model-based design of optimal experiments for nonlinear estimation
论文作者
论文摘要
我们研究了在最大样本估计的背景下基于模型的实验设计的方法。这些方法通过考虑参数不确定性的影响，为设计最佳实验设计的基于模型的方法提供了鲁棒化。我们使用线性化置信区研究了非线性最小二乘参数估计的框架中实验最佳设计的问题。我们在这方面研究了几个众所周知的鲁棒框架，并提出了一种基于多阶段鲁棒优化的新方法。所提出的方法旨在解决问题，其中实验是在实验之间重新估计的可能性。多阶段形式主义有助于识别在实验早期阶段更好地进行的实验，而参数知识很差。我们使用四个不同复杂性的案例研究证明了所提出的方法的发现和有效性。
We study approaches to robust model-based design of experiments in the context of maximum-likelihood estimation. These approaches provide robustification of model-based methodologies for the design of optimal experiments by accounting for the effect of the parametric uncertainty. We study the problem of robust optimal design of experiments in the framework of nonlinear least-squares parameter estimation using linearized confidence regions. We investigate several well-known robustification frameworks in this respect and propose a novel methodology based on multi-stage robust optimization. The proposed methodology aims at problems, where the experiments are designed sequentially with a possibility of re-estimation in-between the experiments. The multi-stage formalism aids in identifying experiments that are better conducted in the early phase of experimentation, where parameter knowledge is poor. We demonstrate the findings and effectiveness of the proposed methodology using four case studies of varying complexity.
