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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/51378

Title: A Self-Learning Tuning Fuzzy Logic Controller Based on Genetric Algorithm and Reinforcements
Authors: Hung‐Yuan Chung
Chih‐Kuan Chiang
Contributors: 國立臺灣海洋大學電機工程學系
Date: 1997-09
Issue Date: 2018-11-27
Publisher: International Journal of Intelligent Systems
Abstract: Abstract
This article presents a new method for learning and tuning a fuzzy logic controller automatically. A reinforcement learning and a genetic algorithm are used in conjunction with a multilayer neural network model of a fuzzy logic controller, which can automatically generate the fuzzy control rules and refine the membership functions at the same time to optimize the final system's performance. In particular, the self-learning and tuning fuzzy logic controller based on genetic algorithms and reinforcement learning architecture, which is called a Stretched Genetic Reinforcement Fuzzy Logic Controller (SGRFLC), proposed here, can also learn fuzzy logic control rules even when only weak information, such as a binary target of “success” or “failure” signal, is available. We extend the AHC algorithm of Barto, Sutton, and Anderson to include the prior control knowledge of human operators. It is shown that the system can solve a fairly difficult control learning problem more concretely, the task is a cart–pole balancing system, in which a pole is hinged to a movable cart to which a continuously variable control force is applied. © 1997 John Wiley & Sons, Inc.
Relation: 12(9)
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/51378
Appears in Collections:[電機工程學系] 期刊論文

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