Abstract
This article presents a net-based structure to model approximate reasoning using fuzzy logic, the fuzzy Petri net model. The knowledge bases to be considered here are assumed to be fuzzy production rules. After a brief introduction of knowledge representation and fuzzy reasoning, we give a new definition of the Fuzzy Petri net model. Next, the basic net structures for complex forms of rules such as rules with conjunction in the antecedent, rules with linguistic quantifiers, and rules with certainty factors are presented. Typical rules sets like parallel rules and conflicting rules are addressed as well. Design techniques to be used when the basic structures are mixed in the context of a large knowledge base are also included. A fuzzy reasoning algorithm is provided. Finally, an application example concerning manufacturing cells modeling is introduced to illustrate the usefulness of the approach proposed.
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