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Improved basic inference models of fuzzy Petri nets

The reasoning efficiency and reliability of fuzzy Petri nets (FPNs) have been the crucial and intractable issues. This paper proposes improved basic inference models of FPNs. One of the major differences between the proposed models and the conventional ones is that inhibitor arcs are introduced to the former. The improved inference models provide a new mechanism and approach for forward reasoning, enhancing reasoning efficiency, and increasing the response speed of a rule-based system. Especially, for complex or large fuzzy Petri nets, the inference advantages are more evident. An effective concurrent reasoning algorithm is given. An instance is presented to illustrate the feasibility and validity of the proposed inference models.