2003
DOI: 10.1109/tkde.2003.1185845
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Fuzzy rule base systems verification using high-level petri nets

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Cited by 56 publications
(34 citation statements)
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“…The verification of its soundness and completeness is triggered by events and is based on our previously developed truth maintenance mechanism. 26 Detailed JESS-based adaptation planner design and implementation can refer to our previous paper.…”
Section: Adaptation Planning Transcoding and Cachingmentioning
confidence: 99%
“…The verification of its soundness and completeness is triggered by events and is based on our previously developed truth maintenance mechanism. 26 Detailed JESS-based adaptation planner design and implementation can refer to our previous paper.…”
Section: Adaptation Planning Transcoding and Cachingmentioning
confidence: 99%
“…We have developed a truth maintenance mechanism for verifying rule base concerning redundancy, inconsistency, incompleteness, and circularity to ensure the incrementally constructed rule base is sound and complete. For readers who are interested in this subject, please refer to our previous research of truth maintenance (Yang, Tsai, & Chen, 2003).…”
Section: Jess-enabled Context Elicitation Systemmentioning
confidence: 99%
“…Petri nets (PNs) is an ideal candidate for investigating and modeling of systems, and it can represent the inference process as a discrete-event dynamic system. The advantages of using PNs in rule-based systems include (1) the graphical formalism, which can visualize the inference states step by step; (2) the transparent modeling, which has well-established formal mechanisms for modeling and structure inconsistency checking; (3) the analyzing capability, which can express dynamic and structural behaviors of a rule-based system via algebraic forms (Scarpelli et al, 1996;Tsang et al, 1999;Yang et al, 2003). The fuzzy Petri nets combines the graphical technique of PNs, the fuzzy sets theory, and the fuzzy production rule, so it has the advantage of the graphical power of PNs and the capability of fuzzy to model rule-based decision system effectively (Fay, 2001).…”
Section: Introductionmentioning
confidence: 99%