Results 251 to 260 of about 247,756 (301)

On Fuzzy Confirmation Measures of Fuzzy Association Rules

2020
Many researchers from different sciences focused their attention on quantifying the degree to which an antecedent in a rule supports a conclusion. This long-standing problem results to be particularly interesting in the case of fuzzy association rules between a fuzzy antecedent and a fuzzy consequence: in fact, rules become much more flexible in ...
Celotto Emilio, Ellero Andrea, Ferretti Paola   +2 more
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On the representation of fuzzy rules in terms of crisp rules

Information Sciences, 2003
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Dubois, Didier, Hüllermeier, Eyke, Prade, Henri   +2 more
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On the interpretation of fuzzy if then rules

Applied Intelligence, 1996
The basic algorithm for reasoning in the fuzzy systems modeling method is introduced. Two classes of operators for interpreting the rules in these models are described, the Mamdani-Zadeh operator and the logical operator. The basic characteristics of these operators are presented and it is shown that the two classes of operators are distinguished by ...
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Fuzzy logic and the calculus of fuzzy if-then rules

[1992] Proceedings The Twenty-Second International Symposium on Multiple-Valued Logic, 2003
Summary form only given. Fuzzy logic and the calculus of fuzzy if-then rules are reviewed. The agenda of the calculus of fuzzy if-then rules is set forth briefly. Their importance is discussed. >
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Fuzzy reasoning based on generalized fuzzy If-Then rules

International Journal of Intelligent Systems, 2002
Summary: This paper focuses on a fuzzy reasoning method based on a generalized If-Then rule. Firstly, the antecedent and the consequent of an If-Then rule are considered and expressed as a component of a kind of binary \(L\)-type fuzzy relation on the product of the universes of discourse and the range of definition for a certain fuzzy attribute.
Yang Xu 0001, Jun Liu 0001, Da Ruan 0001, Wenjiang Li   +3 more
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Fuzzy rules generation from data through fuzzy evaluation of fuzzy rules

KES'2000. Fourth International Conference on Knowledge-Based Intelligent Engineering Systems and Allied Technologies. Proceedings (Cat. No.00TH8516), 2002
This paper deals with generating fuzzy rules from numerical data. The rule evaluation which is necessary in order to determine rule fitness with respect to given data is often based on mere heuristics and usually lacks in semantic interpretation. The method proposed in this paper is based on two central ideas which face these shortcomings.
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On fuzzy association rules based on fuzzy cardinalities

10th IEEE International Conference on Fuzzy Systems. (Cat. No.01CH37297), 2002
The paper discusses the benefit of using fuzzy sets in data summaries based on generalized association rules. Fuzzy sets provide a convenient interface between labels and data and allow for partial belonging to connex but distinct classes. They thus offer a robust reading of the data.
Bosc, Patrick, Dubois, Didier, Prade, Henri, Pivert, Olivier   +3 more
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Fuzzy neural network with relational fuzzy rules

Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks. IJCNN 2000. Neural Computing: New Challenges and Perspectives for the New Millennium, 2000
The paper presents a fuzzy neural network whose structure accounts for relations between input variables of the system under consideration. This modification results in a simple fuzzy model with improved approximation accuracy. An example of nonlinear time series prediction using the proposed fuzzy neural network is also included.
Adam E. Gaweda, Jacek M. Zurada
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INCORPORATING FUZZINESS INTO ACTIVE RULES

International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2008
Knowledge intensive applications require an intelligent environment, which can perform deductions in response to user queries or events that occur inside or outside of the applications. For that, we propose a fuzzy active object-oriented database for modeling knowledge intensive applications.
Burcin Bostan-Korpeoglu, Adnan Yazici
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Graded Fuzzy Rules

2007
In this contribution, we will recall graded fuzzy rules introduced in [5] and explain the difference from the classical fuzzy rules. Moreover, properties of formulae, which are used to formalize the graded fuzzy rules, will be recalled.
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