As a vital modelling technique, fuzzy Petri nets (FPNs) have been widely used in various areas for knowledge representation and reasoning. However, the conventional FPNs have many deficiencies in representing inaccurate knowledge, acquiring knowledge parameters and conducting approximate reasoning when used in the real world. In this article, a new version of FPNs, called R‐numbers Petri nets (RPNs), is proposed to overcome the shortcomings and enhance the effectiveness of FPNs. Based on R‐numbers, expert knowledge is depicted in the form of weighted R‐numbers production rules. The interrelationships among input places (or transitions) are modelled by the R‐numbers Maclaurin symmetric mean operator in the knowledge reasoning process. In addition, the conflict opinions of experts are handled with the proposed RPN model in order to obtain more precise knowledge parameters. Finally, the effectiveness and practicality of the proposed RPNs are illustrated by a realistic example concerning reliability analysis of an electric vehicle motor.

中文翻译：

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考虑冲突意见的使用R数Petri网的知识表示和获取

作为重要的建模技术，模糊Petri网（FPN）已广泛用于知识表示和推理的各个领域。然而，常规的FPN在现实世界中使用时，在表示不准确的知识，获取知识参数和进行近似推理方面存在许多缺陷。在本文中，提出了一种新的FPN版本，称为R-数Petri网（RPN），以克服这些缺点并增强FPN的有效性。基于R编号，以加权R编号生产规则的形式描述专家知识。输入位置（或过渡）之间的相互关系由R数Maclaurin对称均值算子在知识推理过程中建模。此外，建议的RPN模型处理专家的冲突意见，以获得更精确的知识参数。最后，通过一个有关电动汽车可靠性分析的实例，说明了所提出的RPN的有效性和实用性。