A self-learning fuzzy logic controller using genetic algorithms with reinforcements

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Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/29289

Title:	A self-learning fuzzy logic controller using genetic algorithms with reinforcements
Authors:	Chiang,CK;Chung,HY;Lin,JJ
Contributors:	電機工程研究所
Date:	1997
Issue Date:	2010-06-29 20:20:58 (UTC+8)
Publisher:	中央大學
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 (GRFLC), 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 (AHC) algorithm of Barto et al. 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:	IEEE TRANSACTIONS ON FUZZY SYSTEMS
Appears in Collections:	[Graduate Institute of Electrical Engineering] journal & Dissertation

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