Researchers usually rely on simulations to predict the response of complex systems, we recognize that the models that underlie these simulations are never perfect. Model validation is a crucial ingredient in simulation credibility assessment. Multivariate responses under uncertainty often exist in complex simulation model, and the corresponding validation problem is not be solved effectively based on the existing validation methods. Hence, this paper presents a new validation method based on evidence theory for simulation model under uncertainty. For analyzing the extent of agreement between simulation outputs and experimental observations under uncertainty, the data features of system responses under uncertainty are extracted primarily. Next, the validation data such as large sample, small sample, data features, and expert opinions are represented as evidence theory. Then the traditional evidence distance method is improved to measure the agreement extent of simulation outputs and experimental observations. The proposed method is verified through an application example on validation of a simulation model about the terminal guidance stage of flight vehicle to illustrate their validity and potential benefits.

研究人员通常依靠模拟来预测复杂系统的响应，我们认识到作为这些模拟基础的模型从来都不是完美的。模型验证是仿真可信度评估的关键要素。复杂的仿真模型中往往存在不确定性下的多元响应，基于现有的验证方法无法有效解决相应的验证问题。为此，本文提出了一种新的基于证据理论的不确定性仿真模型验证方法。为了分析不确定性下模拟输出与实验观测值的一致性程度，主要提取了不确定性下系统响应的数据特征。接下来是大样本、小样本、数据特征等验证数据，专家意见被表述为证据理论。然后改进传统的证据距离方法来衡量模拟输出和实验观察的一致程度。通过对飞行器末端制导阶段仿真模型验证的应用实例验证了所提出的方法的有效性和潜在效益。