NDLI: Genetic fuzzy logic controllers

Content Provider	IEEE Xplore Digital Library
Author	Yu-Chiun Chiou Lan, L.W.
Copyright Year	2002
Description	Author affiliation: Traffic & Transp. Eng. & Manage. Dept., Feng Chia Univ., Taichung, Taiwan (Yu-Chiun Chiou)
Abstract	The conventional fuzzy logic controller (CFLC) is limited in application, because its logic rules and membership functions have to be preset with expert knowledge. To avoid such drawbacks, a genetic fuzzy logic controller (GFLC) is proposed by employing an iterative evolution algorithm to promote the learning performance of logic rules and the tuning effectiveness of membership functions from examples In sequence. In addition, an encoding method is developed to overcome the difficulties in dealing with numerous constraints while employing genetic algorithms in tuning membership functions. A case of GM car-following behaviors is experimented to verify the applicability and robustness of GFLC. The results demonstrate that GFLC can predict the car-following behaviors precisely. Due to the similarity between fuzzy neural networks (FNN) and GFLC, a comparison is also made and the results indicate that GFLC performs superior to FNN.
Starting Page	200
Ending Page	205
File Size	411644
Page Count	6
File Format	PDF
ISBN	0780373898
DOI	10.1109/ITSC.2002.1041214
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2002-09-06
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Fuzzy logic Fuzzy control Genetic algorithms Control systems Fuzzy systems Encoding Robustness Neural networks Fuzzy neural networks Programmable control
Content Type	Text
Resource Type	Article

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