Knowledge representation and acquisition using R-numbers Petri nets considering conflict opinions

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.