仿真可信度智能评估需求及方法研究

doi:10.16182/j.issn1004731x.joss.25-0510

摘要/Abstract

摘要：

仿真能否准确代表真实世界是用户十分关注的问题，仿真可信度评估通过对仿真正确性和有效性进行评估进而确保其可信度水平能够满足应用目标需求。随着仿真技术的深入应用及新仿真形态的出现，传统评估方法在专家依赖性、数据处理能力、评估效率等方面的不足凸显，基于此对仿真可信度智能评估的研究需求、研究现状、新方法、未来发展方向等进行分析总结。基于仿真可信度评估流程和问题分析可信度智能评估需求；总结智能技术的分类及其在可信度评估领域的应用，并给出4种仿真可信度智能评估新方法；提出仿真可信度智能评估的未来研究方向。

关键词: 仿真可信度, 智能评估, 深度学习, 感知计算, 知识图谱

Abstract:

The accuracy of simulations in representing real-world systems is a critical concern for users. Simulation credibility assessment ensures trustworthiness by evaluating the correctness and effectiveness of simulations to meet application requirements. As simulation technologies are widely adopted, and new simulation paradigms emerge, traditional assessment methods are increasingly showing limitations in their dependence on experts, data processing capabilities, and assessment efficiency. This paper systematically reviewed the research demands, current progress, new technologies, and future trends of intelligent simulation credibility assessment. Based on the simulation credibility assessment process and problem analysis, the requirements for intelligent credibility assessment were discussed. Intelligent technologies were classified, and their applications in credibility assessment were summarized, with four emerging intelligent assessment methods being presented. Potential research directions for intelligent simulation credibility assessment were proposed.

Key words: simulation credibility, intelligent assessment, deep learning, perceptual computing, knowledge graph

中图分类号:

TP391.9

王秉珩,刘庭瑞,杨帆等 . 仿真可信度智能评估需求及方法研究[J]. 系统仿真学报, 2025, 37(7): 1710-1722.

Wang Bingheng,Liu Tingrui,Yang Fan,et al . Research on Requirements and Methods for Intelligent Assessment of Simulation Credibility[J]. Journal of System Simulation, 2025, 37(7): 1710-1722.

图/表 12

图1

图2

表1

图3

图4

图5

表2

图6

表3

评估指标的评估结果和权重

评估指标	可信度		权重
评估指标	可信度	STD	权重	STD
$I 1$	—	—	—	—
$I 2$	76.8	[3.5，0]	非常不重要	—
$I 3$	80.4	[5，1.5]	中等重要	—
$I 4$	[80，85]	[1，2.2]	76.5	[4，0.5]
$I 5$	缺乏可信	—	极其重要	—

表3

表4

图7

表5

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智能技术类型	可信度智能评估方法	解决的主要问题
机器学习	基于概率神经网络的仿真验证方法^[8]	多候选仿真模型验证问题
	基于聚类和判别分析的排序评估方法^[20]	多候选仿真模型验证问题
	基于PNN的指标-专家映射方法^[21]	仿真模型半自动充分性评估
	基于集成学习的复杂仿真模型验证方法^[22]	评估数据相似性度量结果与可信度等级映射关系
	基于生成对抗网络的数据增强方法^[23]	面向小样本评估数据
	基于主成分分析的特征提取方法^[24]	面向多元相关变量的高维特征
知识推理	基于知识的专家系统^[11]	考虑多种行为的复杂仿真模型的验证问题
	基于知识图谱的评估框架^[12]	数字孪生模型的集成与验证问题
	基于仿真模型预期行为知识的验证模糊知识库^[13]	模型行为智能验证问题
贝叶斯推理	基于贝叶斯推理的模型验证方法^[25]	考虑不确定性的模型验证问题
	基于贝叶斯网络的可信度外推方法^[14]	复杂仿真系统可信度推理问题
	基于融合先验信息的仿真交互可信度评估方法^[26]	仿真交互行为可信度实时评估问题
智能优化	基于自适应克里金法的结构可靠性评估方法^[27]	评估数据高效优化问题
	基于蚁群算法的评价模型优化设计方法^[18]	评估模型优化问题
	用于优化拉丁超立方采样的扩展算法^[19]	昂贵仿真模型高效采样问题
	基于遗传算法的区间数层次分析法^[17]	评估指标权重优化问题