A high level net approach for discovering potential inconsistencies in fuzzy knowledge bases
Fuzzy Sets and Systems
Knowledge Representation Using Fuzzy Petri Nets
IEEE Transactions on Knowledge and Data Engineering
A fuzzy Petri net-based expert system and its application to damageassessment of bridges
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A multilevel weighted fuzzy reasoning algorithm for expert systems
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A reasoning algorithm for high-level fuzzy Petri nets
IEEE Transactions on Fuzzy Systems
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In a complicated expert reasoning system, it is inefficient for commonly fuzzy production rules to depict the vague and modified knowledge. Fuzzy Petri nets are more accurate for dynamic knowledge proposition in describing expert knowledge. However, the bad learning ability of fuzzy Petri net constrains its application in dynamic knowledge expert system. In this paper, an advanced self-adaptation learning way based on error back-propagation is proposed to train parameters of fuzzy production rules in fuzzy Petri net. In order to enhance reasoning and learning efficiency, fuzzy Petri net is transformed into hierarchy model and continuous functions are built to approximate transition firing and fuzzy reasoning. Simulation results show that the designed advanced learning way can make rule parameters arrive at optimization rapidly. These techniques used in this paper are quite effective and can be applied to most practical Petri net models and fuzzy expert systems.