Simulation metamodeling in the presence of model inadequacy
Simulation metamodeling in the presence of model inadequacy
复制标题
模型不足时的仿真元建模
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Lu Zou
中科院分区：
文献类型：
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作者：
Xiaowei Zhang;Lu Zou
在决策过程中，仿真模型通常用作感兴趣的真实的系统的代理。然而，没有一个仿真模型完全代表现实。应仔细评估模型不足对系统性能预测的影响。我们提出了一种新的元建模方法，同时表征仿真模型和模型的不足。我们的方法利用仿真输出和真实的数据来预测系统的性能，并占四种类型的不确定性，所产生的未知性能测量的仿真模型，仿真误差，未知的模型不足，观测误差的真实的系统，分别。数值结果表明，新的方法提供了更准确的预测一般。
A simulation model is often used as a proxy for the real system of interest in a decision-making process. However, no simulation model is totally representative of the reality. The impact of the model inadequacy on the prediction of system performance should be carefully assessed. We propose a new metamodeling approach to simultaneously characterize both the simulation model and its model inadequacy. Our approach utilizes both simulation outputs and real data to predict system performance, and accounts for four types of uncertainty that arise from the unknown performance measure of the simulation model, simulation errors, unknown model inadequacy, and observation errors of the real system, respectively. Numerical results show that the new approach provides more accurate predictions in general.