基于云模型的仿真可信度评估方法

Evaluation method for simulation credibility based on cloud model

针对云模型在非正态分布条件下不适用的问题,提出了基于均匀分布的一维逆向云算法,并将其应用于仿真系统的可信度评估体系。首先,阐述了仿真可信度的重要性,并以实际工程为背景建立了某型装备抗干扰能力评估结果可信度评估指标;其次,运用基于云模型的仿真可信度评估方法对系统进行评估,并对该评估方法进行改进;最后,为了完善该评估方法,推导出基于均匀分布的一维逆向云算法,并且设计实验验证了该算法的有效性。仿真实验结果表明,该逆向云算法在较大数据时平均绝对误差小于5%,具有较高实用性,为云模型理论的完善提供一种思路。此外仿真可信度评估结果表明,该评估方法精度高,包含数据的分散度和凝聚度信息,可以进行更全面评估和错误数据预测。

A cloud model is not suitable for non-normal distribution. In order to solve the problem, a new onedimensional backward cloud algorithm based on uniform distribution was proposed and applied to the credibility evaluation system of simulation system. Firstly, the importance of simulation credibility was expounded, and the credibility evaluation index of the evaluation results for a type of equipment concerning anti-jamming capability was established based on the actual project background. Secondly, the system was evaluated by using the evaluation method for simulation credibility based on cloud model, and the evaluation method was improved. Finally, in order to improve the evaluation method, a one-dimensional backward cloud algorithm based on uniform distribution was derived, and the experiment was designed for verifying the validity of the algorithm. The simulation experimental results show that, the average absolute error of the proposed backward cloud algorithm is less than 5% for large data, which has high applicability and provides a way of thinking for the perfection of cloud model theory. In addition, the simulation credibility evaluation results show that, the proposed method has high accuracy and contains the data information of dispersion and agglomeration, which can provides more comprehensive evaluation and the prediction of error data.