English  |  正體中文  |  简体中文  |  Items with full text/Total items : 27248/39091
Visitors : 2416748      Online Users : 57
RC Version 4.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Adv. Search
LoginUploadHelpAboutAdminister

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
neuralnetwork
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

Files in This Item:

File Description SizeFormat
index.html0KbHTML12View/Open


All items in NTOUR are protected by copyright, with all rights reserved.

 


著作權政策宣告: 本網站之內容為國立臺灣海洋大學所收錄之機構典藏,無償提供學術研究與公眾教育等公益性使用,請合理使用本網站之內容,以尊重著作權人之權益。
網站維護: 海大圖資處 圖書系統組
DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback