Results 221 to 230 of about 18,237 (266)
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2009
The “fuzzy dot” (or fuzzy relation) representation of fuzzy rules in fuzzy rule based systems, in case of classical fuzzy reasoning methods (e.g. the Zadeh-Mamdani- Larsen Compositional Rule of Inference (CRI) (Zadeh, 1973) (Mamdani, 1975) (Larsen, 1980) or the Takagi - Sugeno fuzzy inference (Sugeno, 1985) (Takagi & Sugeno, 1985)), are assuming ...
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The “fuzzy dot” (or fuzzy relation) representation of fuzzy rules in fuzzy rule based systems, in case of classical fuzzy reasoning methods (e.g. the Zadeh-Mamdani- Larsen Compositional Rule of Inference (CRI) (Zadeh, 1973) (Mamdani, 1975) (Larsen, 1980) or the Takagi - Sugeno fuzzy inference (Sugeno, 1985) (Takagi & Sugeno, 1985)), are assuming ...
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2001
In fuzzy rule generation, training data are preclustered or postclustered. In preclustering, we cluster the training data in advance and generate a fuzzy rule for each cluster. In postclustering, we start from one fuzzy rule and generate fuzzy rules around the training data with large estimation errors or at the points where the training data gather ...
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In fuzzy rule generation, training data are preclustered or postclustered. In preclustering, we cluster the training data in advance and generate a fuzzy rule for each cluster. In postclustering, we start from one fuzzy rule and generate fuzzy rules around the training data with large estimation errors or at the points where the training data gather ...
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Fuzzy rules extraction, reasoning and rules adaptation in fuzzy neural networks
Proceedings of International Conference on Neural Networks (ICNN'97), 2002This paper introduces a method for fuzzy rules extraction from a fuzzy neural network called FuNN. The process of reasoning in FuNN has been formalised and presented as a synergistic reasoning method. A bench-mark test data set of a chaotic time-series is used to illustrate the methods.
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1998
The first few chapters of this book have described place coding and networks of links as a mean of carrying out computation. We have assumed the existence of some conversion devices turning a conventional representation of input signals into place-coded activation patterns.
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The first few chapters of this book have described place coding and networks of links as a mean of carrying out computation. We have assumed the existence of some conversion devices turning a conventional representation of input signals into place-coded activation patterns.
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Fuzzy Predicate Calculus and Fuzzy Rules
2000The basic many-sorted fuzzy predicate calculus {tiBL∀} is presented and used to express and prove logical properties of “fuzzy IF-THEN rules”.
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2016
This chapter explores fuzzy logic controllers from the point of view of its applications. The chapter covers the fuzzy logic controllers of Mamdani and Takagi-Sugeno-Kang. These are illustrated with applications in biology, ecology, HIV dynamics, and pharmacological decay.
Laécio Carvalho de Barros +2 more
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This chapter explores fuzzy logic controllers from the point of view of its applications. The chapter covers the fuzzy logic controllers of Mamdani and Takagi-Sugeno-Kang. These are illustrated with applications in biology, ecology, HIV dynamics, and pharmacological decay.
Laécio Carvalho de Barros +2 more
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Optimization under fuzzy rule constraints
1998Suppose we are given a mathematical programming problem in which the functional relationship between the decision variables and the objective function is not completely known. Our knowledge-base consists of a block of fuzzy if-then rules, where the antecedent part of the rules contains some linguistic values of the decision variables, and the ...
GIOVE, Silvio, CARLSSON C, FULLER R.
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Fuzzy sets, fuzzy clustering and fuzzy rules in AI
1994We discuss the use of fuzzy set theory and semantic unification for fuzzy clustering and the use of fuzzy rules in knowlege bases. The paper provides a unification with probability theory and probabilistic fuzzy rules are discussed. Fuzzy sets are used to provide generalisation in clustering and pattern recognition methods.
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Background: Fuzzy Rule Interpolation
2018Conventional fuzzy reasoning methods such as Mamdani (Int J Man Mach Stud:7, 1975, [1]) and TSK (Fuzzy Sets Syst 28:15–33, 1988, [2], IEEE Trans Syst Man Cybern 1:116–132, 1985, [3]) require that the rule bases are dense. That is, the input universe of discourse is covered completely by the rule base.
Shangzhu Jin, Qiang Shen, Jun Peng
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