Genetic fuzzy logic controllers

Yu-Chiun Chiou*, Lawrence W. Lan

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations


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 or 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 behavior 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.

Original languageEnglish
Title of host publicationIEEE 5th International Conference on Intelligent Transportation Systems, ITSC 2002 - Proceedings
EditorsDer-Horng Lee, Dipti Srinivasan, Ruey Long Cheu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages6
ISBN (Electronic)0780373898
StatePublished - 1 Jan 2002
Event5th IEEE International Conference on Intelligent Transportation Systems, ITSC 2002 - Singapore, Singapore
Duration: 3 Sep 20026 Sep 2002

Publication series

NameIEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC


Conference5th IEEE International Conference on Intelligent Transportation Systems, ITSC 2002


  • Ear-following behaviors
  • Fuzzy logic controller
  • Fuzzy neural network
  • Genetic algorithms
  • Genetic fuzzy logic controller


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