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Antecedent Redundancy Exploitation in Fuzzy Rule Interpolation-based Reinforcement Learning
2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 2020This paper introduces novel methods which could improve the efficiency of the automated knowledge extraction methods used in the FRIQ-learning (Fuzzy Rule Interpolationbased Q-learning) machine learning method.
Dávid Vincze, Alex Tóth, M. Niitsuma
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Football Simulation Modeling with Fuzzy Rule Interpolation-based Fuzzy Automaton
2020 17th International Conference on Ubiquitous Robots (UR), 2020A Fuzzy Rule Interpolation-based (FRI) fuzzy automaton for controlling a football match simulation is going to be introduced in this paper. Controlling the agents (football players) of the simulation is realized by evaluating such fuzzy rule-bases, which
Dávid Vincze, Alex Tóth, M. Niitsuma
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Fuzzy Rule Interpolation-based Q-learning
2009 5th International Symposium on Applied Computational Intelligence and Informatics, 2009Reinforcement 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 ...
Dávid Vincze, Szilveszter Kovács
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Fuzzy Rule Interpolation Developer Toolbox Library
2012 7th IEEE International Symposium on Applied Computational Intelligence and Informatics (SACI), 2012In 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.
Zoltán Krizsán, Szilveszter Kovács
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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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Feature ranking-guided fuzzy rule interpolation
2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2017Fuzzy rule interpolation (FRI) provides an alternative means to make inference with a sparse rule base, rather than directly resulting in failed reasoning when no rules can be fired for an input observation. However, existing approaches to FRI typically assume that rule antecedents are of equal significance in the implementation of interpolation ...
Fangyi Li +3 more
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Detecting Slow Port Scan Using Fuzzy Rule Interpolation
Italian Conference on Theoretical Computer Science, 2019Fuzzy Rule Interpolation (FRI) offers a convenient way for delivering rule based decisions on continuous universes avoiding the burden of binary decisions.
Mohammad Almseidin +2 more
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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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Information Sciences, 2019
In real-world applications, the performances of traditional fuzzy interpolation methods are not good enough because they may produce inconsistent fuzzy interpolation results when contradictory fuzzy interpolated results are obtained after fuzzy ...
Shyi-Ming Chen +2 more
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In real-world applications, the performances of traditional fuzzy interpolation methods are not good enough because they may produce inconsistent fuzzy interpolation results when contradictory fuzzy interpolated results are obtained after fuzzy ...
Shyi-Ming Chen +2 more
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Maintenance of local fuzziness in rule interpolation
Proceedings of IEEE International Conference on Intelligent Engineering Systems, 2002Approximate 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 +3 more
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