Supervised and unsupervised Fuzzy Concept Learning

Masoud Mohammadian, Irshad Nainar

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

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In this paoer the supervised and unsupervised fuzzy concept learning using Genetic Algorithms is considered. Genetic Algorithms is first applied to the learning of fuzzy rules for a fuzzy logic system for the prediction of unemployment rate, in a supervised learning manner. Genetic algorithms is then used to extract fuzzy knowledge from a set of date in an unsupervised learning manner. Specifically it's application to urban traffic control is considered. In both cases, Generic algorithms is employed as an adaptive method for learning the knowledge and behaviour of a system.
Original languageEnglish
Title of host publicationSupervised and unsupervised Fuzzy Concept Learning
EditorsN Steele
Place of PublicationCanada
Number of pages7
Publication statusPublished - 12 Feb 1997
Externally publishedYes
EventInternational Fuzzy Logic and Applications ISFL97 Symposium - Switzerland, Zurich, Swaziland
Duration: 12 Feb 199714 Feb 1997


ConferenceInternational Fuzzy Logic and Applications ISFL97 Symposium
Abbreviated titleIFL97


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