Fuzzy Petri Net Modeling and Simulation: Toward Enhanced Direct Reasoning Algorithms

Many knowledge representation methods have been developed for the purpose of defining an exact (nonfuzzy) knowledge type. The introduction of fuzzy variables imposes additional challenges when representing knowledge. The application of a certainty factor in relation to uncertain knowledge is a largely used method in overcoming this challenge. Applying this method in conjunction with Petri nets and fuzzy relations is a principle of Fuzzy Petri nets (FPNs). Based on the literature reviews, we have developed a modified fuzzy reasoning algorithm, enhancing the efficiency and capability of the previous ones. Additionally, we propose a new algorithm for obtaining the inverse-immediate reachability set, required in the use of our fuzzy reasoning algorithm. The modeling and simulation results indicate that a 13% decrease in execution time needed for our proposed algorithms.