Results 201 to 210 of about 1,268 (239)

Fuzzy Rule Interpolation-based Q-learning

2009 5th International Symposium on Applied Computational Intelligence and Informatics, 2009
Reinforcement learning is a well known topic in computational intelligence. It can be used to solve control problems in unknown environments without defining an exact method on how to solve problems in various situations. Instead the goal is defined and all the actions done in the different states are given feedback, called reward or punishment ...
David Vincze, Szilveszter Kovacs
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Interpolation in structured fuzzy rule bases

[Proceedings 1993] Second IEEE International Conference on Fuzzy Systems, 2002
Fuzzy-rule-based systems are important in many control engineering applications. In real problems, often the number of input variables is very high, but a few or maximally a few dozen variables dominate the system in a certain area of the state space. The subset of variables changes when the working point changes.
L. Koczy, K. Hirota
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Fuzzy Rule Interpolation Developer Toolbox Library

2012 7th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI), 2012
In fuzzy applications which run in real environment the complete rule base is not always available due to real manner and performance issue. In case of real application which based on sparse rule base Fuzzy model the conclusion is interpolated using more rules.
Zoltan Krizsan, Szilveszter Kovacs
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Fuzzy Rule Interpolation

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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Fuzzy rule interpolation and reinforcement learning

2017 IEEE 15th International Symposium on Applied Machine Intelligence and Informatics (SAMI), 2017
Reinforcement Learning (RL) methods became popular decades ago and still maintain to be one of the mainstream topics in computational intelligence. Countless different RL methods and variants can be found in the literature, each one having its own advantages and disadvantages in a specific application domain.
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Size reduction by interpolation in fuzzy rule bases

IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 1997
Fuzzy control is at present still the most important area of real applications for fuzzy theory. It is a generalized form of expert control using fuzzy sets in the definition of vague/linguistic predicates, modeling a system by If...then rules. In the classical approaches it is necessary that observations on the actual state of the system partly match (
L T, Koczy, K, Hirota
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Similarity, interpolation, and fuzzy rule construction

Fuzzy Sets and Systems, 1993
Abstract A method for the construction of fuzzy concepts and fuzzy if-then rules based on similarity and paradigmatic examples is presented. It is shown that all normal fuzzy sets may be realized as the interpolation of paradigmatic examples by a similarity relation.
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Extending the concept of Fuzzy Rule Interpolation with the interpolation of fuzziness

2012 IEEE International Conference on Fuzzy Systems, 2012
Fuzzy Rule Interpolation (FRI) methods are not always suitable for describing changes in the conclusion fuzziness. For example, it is difficult to describe cases in which the conclusion for a crisp observation must be fuzzy, or in which an increase in the fuzziness of an observation yields less fuzziness in the conclusion.
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Genetic algorithm-aided dynamic fuzzy rule interpolation

2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2014
Fuzzy rule interpolation (FRI) is a well established area for reducing the complexity of fuzzy models and for making inference possible in sparse rule-based systems. Regardless of the actual FRI approach employed, the interpolative reasoning process generally produces a large number of interpolated rules, which are then discarded as soon as the ...
Nitin Naik, Ren Diao, Qiang Shen
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Maintenance of local fuzziness in rule interpolation

Proceedings of IEEE International Conference on Intelligent Engineering Systems, 2002
Approximate reasoning using fuzzy rule based systems has a wide application in, for example, industrial control, property prediction, and in pattern recognition areas. We introduce our method which is conservative with respect to the degree of local fuzziness in the rule base, and demonstrate its utility on a petroleum engineering problem.
T.D. Gedeon, L.T. Koczy, Y. Huang, P.M. Wong   +3 more
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