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ABSTRACT: In this paper, we present temporal knowledge representation and
reasoning techniques using time Petri nets. A method is also proposed to
check the consistency of the temporal knowledge. The proposed method can
overcome the drawback of the one presented in Yao (1994). It provides a
useful way to check the consistency of the temporal knowledge
IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) 09/1999; · 3.08 Impact Factor
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IEEE Transactions on Systems, Man, and Cybernetics, Part B. 01/1999; 29:541-545.
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ABSTRACT: The paper presents a new method for fuzzy query translation based
on the α-cuts operations of fuzzy numbers. This proposed method
allows the retrieval conditions of SQL queries to be described by fuzzy
terms represented by fuzzy numbers. It emphasizes friendliness and
flexibility for inexperienced users. The authors have implemented a
fuzzy query translator to translate user's fuzzy queries into precise
queries for relational database systems. Because the proposed method
allows the user to construct his fuzzy queries intuitively and to choose
different retrieval threshold values for fuzzy query translation, the
existing relational database systems will be more friendly and more
flexible to the users
IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) 09/1997; · 3.08 Impact Factor
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ABSTRACT: Yao (1994) has presented a unified time Petri net model (TPN) for temporal knowledge representation and reasoning, where the TPN model presented has a good contribution to the aspect of temporal knowledge representation and reasoning. However, there are a number of errors which should be corrected. The purpose of the paper is to identify these errors, and the corrections provided permit the readers who have been confused by the errors to gain a better understanding of the good ideas presented.
IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) 03/1997; · 3.08 Impact Factor
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IEEE Transactions on Systems, Man, and Cybernetics, Part B. 01/1997; 27:165-166.
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ABSTRACT: This paper proposes a bidirectional approximate reasoning method based on interval-valued fuzzy sets, where fuzzy production rules are used for knowledge representation, and the fuzzy terms appearing in the fuzzy production rules of a rule-based system are represented by interval-valued fuzzy sets. The proposed method is more flexible than the one presented in the paper by Bien and Chun [IEEE Trans. Fuzzy Systems 2 (1994) 177] due to the fact that it allows the fuzzy terms appearing in the fuzzy production rules of a rule-based system to be represented by interval-valued fuzzy sets rather than general fuzzy sets. Furthermore, because the proposed method requires only simple arithmetic operations, and because it allows bidirectional approximate reasoning, it can be executed much faster and more flexible than the single-input-single-output approximate reasoning scheme presented in the paper by Gorzalczany [Fuzzy Sets and Systems 21 (1981) 10].
Fuzzy Sets and Systems.
