Supervised and unsupervised Fuzzy Concept Learning

Masoud Mohammadian, Irshad Nainar

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Abstract

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
Pages196-202
Number of pages7
Volume1
Edition1
Publication statusPublished - 12 Feb 1997
Externally publishedYes
EventInternational Fuzzy Logic and Applications ISFL97 Symposium - Switzerland, Zurich, Swaziland
Duration: 12 Feb 199714 Feb 1997

Conference

ConferenceInternational Fuzzy Logic and Applications ISFL97 Symposium
Abbreviated titleIFL97
Country/TerritorySwaziland
CityZurich
Period12/02/9714/02/97

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