引用本文:锁斌,孙东阳,曾超,等.考虑随机不确定性的常态仿真模型确认试验设计[J].控制与决策,2020,35(8):1923-1928
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考虑随机不确定性的常态仿真模型确认试验设计
锁斌1,孙东阳2, 曾超1, 张保强3
(1. 中国工程物理研究院电子工程研究所,四川绵阳621900;2. 重庆大学航空航天学院,重庆400044;3. 厦门大学航空航天学院,福建厦门361005)
摘要:
模型确认试验是一种新的试验,其目的在于度量仿真模型的可信度.为了得到低成本、高可信度的模型确认试验方案,提出一种随机不确定性模型确认试验设计方法.首先,基于面积确认度量指标提出一种新的无量纲的模型确认度量指标(面积确认度量指标因子),并且在其基础上发展了基于专家系统的仿真模型准确性定性评判准则;然后,建立随机不确定性模型确认试验优化设计模型,提出该优化模型的求解方法;最后,通过两个数值算例对提出的模型确认试验设计方法进行验证.结果表明,小样本情况下,试验方案的随机性会影响模型评判结果的可信度;面积度量指标因子随试验样本数量的增加而收敛;随机不确定性模型确认试验设计方法能够避免试验方案对模型确认结果的影响.
关键词:  确认试验  确认度量  面积度量  不确定性  专家系统  优化设计
DOI:10.13195/j.kzyjc.2018.1579
分类号:TP273
基金项目:国家自然科学基金项目(51505398,51275240);国家自然科学基金委员会与中国工程物理研究院联合基金项目(U1530122,U183010080);国防军工十三五跨行业预研项目(41424050101);中央军委装备发展部技术基础科研项目(171ZW31001).
Model validation experiment design of time-invariant model with random uncertainty
SUO Bin1,SUN Dong-yang2,ZENG Chao1,ZHANG Bao-qiang3
(1. Institute of Electronic Engineering,China Academy of Engineering Physics,Mianyang 621900,China;2. College of Aerospace Engineering,Chongqing University,Chongqing 400044,China;3. School of Aerospace Engineering,Xiamen University,Xiamen 361005,China)
Abstract:
Model validation experiment is a new type of experiments, of which the goal is to determine the credibility of the simulation model. In order to obtain low cost and high credibility of a model validation experiment, a methodology of model validation experiment design considering random uncertainty is presented. Firstly, by extending the concept of area metrics, a dimensionless model validation metric (area metric factor) is proposed, and a criterion for qualitative assessment of accuracy of simulation models based on expert systems is developed. Then, an optimization model for model validation experiment design considering random uncertainty is constructed. Meanwhile, an optimization method is developed to solve the optimization model. Finally, two simulation examples are used to illustrate the proposed methods. The simulation results show that the randomness of experiment design affects the reliability of the model evaluation results in the case of small samples. The area metric factor converges with the increase of the number of experimental observations. The proposed method for model validation experiment design can avoid the effect of testing program on the result of model validation.
Key words:  validation experiment design  validation metric  area metric  uncertainty  expert system  optimization design

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