Please use this identifier to cite or link to this item: http://hdl.handle.net/123456789/4240
Title: Tuning Fuzzy Inference Systems by Q-Learning
Authors: Mohamed Boumehraz
Khier Benmahammed
Keywords: reinforcement learning, fuzzy inference systems, Q-learning
Issue Date: 25-Nov-2014
Abstract: Abstract: 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 learning
URI: http://dspace.univ-biskra.dz:8080/jspui/handle/123456789/4240
Appears in Collections:Communications Internationales

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