Simulation workflow is a top-level model for the design and control of simulation process. It connects multiple simulation components with time and interaction restrictions to form a complete simulation system. Before the construction and evaluation of the component models, the validation of upper-layer simulation workflow is of the most importance in a simulation system. However, the methods especially for validating simulation workflow is very limit. Many of the existing validation techniques are domain-dependent with cumbersome questionnaire design and expert scoring. Therefore, this paper present an empirical learning-based validation procedure to implement a semi-automated evaluation for simulation workflow. First, representative features of general simulation workflow and their relations with validation indices are proposed. The calculation process of workflow credibility based on Analytic Hierarchy Process (AHP) is then introduced. In order to make full use of the historical data and implement more efficient validation, four learning algorithms, including back propagation neural network (BPNN), extreme learning machine (ELM), evolving new-neuron (eNFN) and fast incremental gaussian mixture model (FIGMN), are introduced for constructing the empirical relation between the workflow credibility and its features. A case study on a landing-process simulation workflow is established to test the feasibility of the proposed procedure. The experimental results also provide some useful overview of the state-of-the-art learning algorithms on the credibility evaluation of simulation models.

翻译：模拟流程是设计和控制模拟过程的最高级模型,它把多个模拟组件与时间和互动限制联系起来,形成完整的模拟系统。在构件模型的建造和评价之前,验证上层模拟工作流程在模拟系统中最为重要。然而,验证模拟工作流程的方法特别有限。许多现有的验证技术都依赖域域,其问卷设计和专家评分繁琐。因此,本文件介绍了实施模拟工作流程半自动评价的经验性学习性验证程序。首先,提出了一般模拟工作流程的代表性特点及其与验证指数的关系。随后,采用了基于分析性分层过程的工作流程可信度计算程序(AHP),以便充分利用历史数据,实施更有效的验证,使用四种学习算法,包括后传神经网络(BPNNN)、极端学习机器(ELM)、不断演进的新中中子(eNFNF)和快速递增的伽西南混合模型(FIGMN),以构建工作流程可靠性及其特征之间的实证关系。随后引入了基于分析性分级过程(AHP)的工作流程可信度计算过程的计算过程。关于着陆过程模拟模型的案例研究分析结果,也对拟议的可行性进行了可靠的模拟分析。