Modeling and verification of fuzzy knowledge base with fuzzy colored Petri nets (FCPN)

The use of fuzzy set theory and approximate reasoning expands the expert knowledge representation capability to a higher degree of uncertainty and facilitates the knowledge acquisition and reasoning process. Since then, considerable amount of knowledge-based expert systems have been developed and implemented in various areas such as: medical diagnosis, industrial management product and process control, and etc. In this paper, we present a high level Petri net model, called fuzzy colored Petri net (FCPN), which is designed to model and verify the production rules in fuzzy knowledge bases and a methodology to classify the fuzziness of consistency. Based on the FCPN model, we have also developed a methodology to transform the acquired expert knowledge into FCPN model and perform the verification process automatically. This methodology is implemented under PC-window environment and can be incorporated with commercial fuzzy knowledge base building software