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 language | English |
|---|---|
| Title of host publication | Supervised and unsupervised Fuzzy Concept Learning |
| Editors | N Steele |
| Place of Publication | Canada |
| Pages | 196-202 |
| Number of pages | 7 |
| Volume | 1 |
| Edition | 1 |
| Publication status | Published - 12 Feb 1997 |
| Externally published | Yes |
| Event | International Fuzzy Logic and Applications ISFL97 Symposium - Switzerland, Zurich, Swaziland Duration: 12 Feb 1997 → 14 Feb 1997 |
Conference
| Conference | International Fuzzy Logic and Applications ISFL97 Symposium |
|---|---|
| Abbreviated title | IFL97 |
| Country/Territory | Swaziland |
| City | Zurich |
| Period | 12/02/97 → 14/02/97 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 8 Decent Work and Economic Growth -
SDG 9 Industry, Innovation, and Infrastructure
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