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dc.contributor.authorMohamed Boumehraz-
dc.contributor.authorKhier Benmahammed-
dc.description.abstractAbstract: Fuzzy 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 learningen_US
dc.subjectreinforcement learning, fuzzy inference systems, Q-learningen_US
dc.titleTuning Fuzzy Inference Systems by Q-Learningen_US
Appears in Collections:Communications Internationales

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