Please use this identifier to cite or link to this item: http://archives.univ-biskra.dz/handle/123456789/739
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dc.contributor.authorBOUMEHRAZ, M-
dc.contributor.authorBENMAHAMMED, K-
dc.contributor.authorHADJILI, M. L-
dc.contributor.authorWERTZ, V-
dc.date.accessioned2013-01-01T23:49:06Z-
dc.date.available2013-01-01T23:49:06Z-
dc.date.issued2013-01-02-
dc.identifier.issn1112 - 3338-
dc.identifier.urihttp://archives.univ-biskra.dz/handle/123456789/739-
dc.description.abstractFuzzy rules for control can be effectively tuned via reinforcement learning. Reinforcement learning is a weak learning method wich only requires information on the succes or failure of the control application. In this paper a reinforcement learning method is used to tune on line the conclusion part of fuzzy inference system rules. The fuzzy rules are tuned in order to maximize the return function . To illustrate its effectivness, the learning method is applied to the well known Cart-Pole balancing system problem. The results obtained show significant improvements of the speed of learning.en_US
dc.language.isoenen_US
dc.subjectreinforcement learningen_US
dc.subjectfuzzy inference systemsen_US
dc.subjectQ-learningen_US
dc.titleFUZZY INFERENCE SYSTEMS OPTIMIZATION BY REINFORCEMENT LEARNINGen_US
dc.typeArticleen_US
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