Evolutionary learning in fuzzy logic control systems

Russel Stonier, Masoud Mohammadian

Research output: Contribution to journalArticle

Abstract

In this paper we discuss the topic of intelligent control by using genetic algorithms to learn fuzzy rules in fuzzy logic control systems. In particular, application is made through simulation studies to collision-avoidance problems and target-tracking for mobile robots. Application to the control of traffic flow approaching a set of intersections and interest rate prediction is also briefly discussed.

Original languageEnglish
Pages (from-to)1-17
Number of pages17
JournalComplexity International
Volume3
Publication statusPublished - 1 Apr 1996

Fingerprint

Fuzzy logic
Control systems
Intelligent control
Fuzzy rules
Collision avoidance
Target tracking
Mobile robots
Genetic algorithms

Cite this

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title = "Evolutionary learning in fuzzy logic control systems",
abstract = "In this paper we discuss the topic of intelligent control by using genetic algorithms to learn fuzzy rules in fuzzy logic control systems. In particular, application is made through simulation studies to collision-avoidance problems and target-tracking for mobile robots. Application to the control of traffic flow approaching a set of intersections and interest rate prediction is also briefly discussed.",
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Evolutionary learning in fuzzy logic control systems. / Stonier, Russel; Mohammadian, Masoud.

In: Complexity International, Vol. 3, 01.04.1996, p. 1-17.

Research output: Contribution to journalArticle

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