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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/51381

Title: A self-Leaning Fuzzy Logic Controller Using Genetic Algorithm with Reinforcements
Authors: Chih-Knan Chiang
Hung-Yuan Chung
Jin-Jye Lin
Contributors: 國立臺灣海洋大學電機工程學系
Keywords: Fuzzy logic control
genetic algorithm
reinforcement learning
Date: 1997-08
Issue Date: 2018-11-27T01:01:02Z
Publisher: IEEE trans. on Fuzzy System
Abstract: Abstract: This paper presents a new method for learning a fuzzy logic controller automatically. A reinforcement learning technique is applied to a multilayer neural network model of a fuzzy logic controller. The proposed self-learning fuzzy logic control that uses the genetic algorithm through reinforcement learning architecture, called a genetic reinforcement fuzzy logic controller, can also learn fuzzy logic control rules even when only weak information such as a binary target of "success" or "failure" signal is available. In this paper, the adaptive heuristic critic algorithm of Barto et al. (1987) is extended to include a priori control knowledge of human operators. It is shown that the system can solve more concretely a fairly difficult control learning problem. Also demonstrated is the feasibility of the method when applied to a cart-pole balancing problem via digital simulations.
Relation: 5(3) pp.460-467
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/51381
Appears in Collections:[電機工程學系] 期刊論文

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