Quantifying the Multi-Objective Cost of Uncertainty
Quantifying the Multi-Objective Cost of Uncertainty
复制标题
量化不确定性的多目标成本
DOI：
10.1109/access.2021.3085486
复制
发表时间：
2021-01-01
期刊：
影响因子：
3.9
通讯作者：
Dougherty, Edward R.
中科院分区：
文献类型：
--
作者：
Yoon, Byung-Jun;Qian, Xiaoning;Dougherty, Edward R.
各种现实世界的应用涉及建模具有巨大不确定性的复杂系统，并基于不确定模型优化多个目标。量化模型不确定性对给定操作目标的影响对于设计最佳实验至关重要，这些实验可以最有效地降低影响与当前应用相关的目标的不确定性。在本文中，我们提出了平均多目标成本的不确定性（多目标MOCU）的概念，可用于基于目标的量化的不确定性复杂的不确定性系统，考虑多个操作目标。我们提供了几个说明性的例子，证明了所提出的多目标MOCU的概念和优势。此外，我们提出了一个现实世界的例子的基础上的哺乳动物细胞周期网络来演示如何多目标MOCU可以用于量化模型的不确定性的操作影响时，有多个，可能竞争，目标。
Various real-world applications involve modeling complex systems with immense uncertainty and optimizing multiple objectives based on the uncertain model. Quantifying the impact of the model uncertainty on the given operational objectives is critical for designing optimal experiments that can most effectively reduce the uncertainty that affect the objectives pertinent to the application at hand. In this paper, we propose the concept of mean multi-objective cost of uncertainty (multi-objective MOCU) that can be used for objective-based quantification of uncertainty for complex uncertain systems considering multiple operational objectives. We provide several illustrative examples that demonstrate the concept and strengths of the proposed multi-objective MOCU. Furthermore, we present a real-world example based on the mammalian cell cycle network to demonstrate how the multi-objective MOCU can be used for quantifying the operational impact of model uncertainty when there are multiple, possibly competing, objectives.