基于区间二型模糊集合的人工交通系统可信度评估

提出了一种基于区间二型模糊集合理论的人工交通系统可信度评估方法.该方法以二型模糊集合算法为核心数据处理方法，构建了人工交通系统的评估体系.利用置信区间方法提取实际交通数据和人工交通数据的统计特征，同时为二型模糊集合提供了输入数据.利用二型模糊集合处理不确定性、随机性和噪声数据的能力，得到刻画实际交通系统和人工交通系统特性的输出数据集.并基于Jaccard算法对两个系统二型模糊集合的输出集进行了相似度运算，以Cronbach系数值为依据，实现了人工交通系统的可信度评估.与传统可信度评估方法相比，该评估方法具有较强的数据处理能力，有效地实现了基于数据驱动方法理念下人工系统与实际系统之间的比较.本文基于面向对象编程语言搭建开发的基于Agent的人工交通系统模型，对其进行了可信度评估验证，评估结果说明了所提出方法的合理性和有效性.

Artificial Traffic System Credibility Evaluation With Interval Type-2 Fuzzy Sets

In this paper, a method to assess the credibility of artificial traffic system based on the interval type-2 fuzzy sets theory is proposed. Combined with the interval algorithm and Jaccard algorithm, an evaluation system is built. The confidence interval approach is utilized to extract the statistical characteristics of data from actual and artificial systems, which at the same time provide the input data for type-2 fuzzy sets. By using the Jaccard algorithm, the similarity of the output set of fuzzy-2 sets is figured out. Based on the Cronbach coefficient, the credibility of the artificial transportation system is realized. Compared with the traditional credibility assessment method, this assessment method has a strong data processing ability to implement the comparison of two systems. In order to verify the effectiveness and rationality of the evaluation system, this paper builds an artificial traffic system model using the agent modeling method to evaluate its credibility. The evaluation results show that the method is reasonable and effective.