An equivalent generating algorithm to model fuzzy Petri net for knowledge-based system

The simulation of knowledge-based systems (KBSs) has become a significant challenge owing to the rapid increase in the scale of accumulated data. The extended formalisms that are widely used to test, model, and analyze such systems include the fuzzy production rule (FPR) and fuzzy Petri net (FPN). However, with the growth in magnitude of KBSs, it has become difficult to manually generate an FPN. Hence, the authors propose an equivalent transformation algorithm that automatically models an FPN for a sizeable KBS. The proposed method produces a final FPR by initially investigating the inner-inference path(s) between FPRs, followed by a four-phase transformation algorithm that automatically generates an equivalent FPN model for the corresponding KBS rooted in the inner-inference path(s) obtained. A KBS with 13 FPRs is used to demonstrate both the validity and feasibly of the proposed transformation algorithm. The results validate the capability of the generated FPN to fully represent the complete information base contained in the corresponding KBS.