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Multi-stage genetic fuzzy systems based on the iterative rule learning approach
dc.contributor.author | González Muñoz, Antonio |
dc.contributor.author | Herrera Triguero, Francisco |
dc.date.accessioned | 2007-09-17T11:53:22Z |
dc.date.available | 2007-09-17T11:53:22Z |
dc.date.issued | 1997 |
dc.identifier.issn | 1134-5632 |
dc.identifier.uri | http://hdl.handle.net/2099/3495 |
dc.description.abstract | Genetic algorithms (GAs) represent a class of adaptive search techniques inspired by natural evolution mechanisms. The search properties of GAs make them suitable to be used in machine learning processes and for developing fuzzy systems, the so-called genetic fuzzy systems (GFSs). In this contribution, we discuss genetics-based machine learning processes presenting the iterative rule learning approach, and a special kind of GFS, a multi-stage GFS based on the iterative rule learning approach, by learning from examples. |
dc.format.extent | 233-249 |
dc.language.iso | eng |
dc.publisher | Universitat Politècnica de Catalunya. Secció de Matemàtiques i Informàtica |
dc.relation.ispartof | Mathware & soft computing . 1997 Vol. 4 Núm. 3 |
dc.rights | Reconeixement-NoComercial-CompartirIgual 3.0 Espanya |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject.other | Fuzzy logic |
dc.subject.other | Fuzzy rules |
dc.subject.other | Genetic algoritms |
dc.subject.other | Machine learning |
dc.subject.other | GFS |
dc.subject.other | Genetic fuzzy systems |
dc.title | Multi-stage genetic fuzzy systems based on the iterative rule learning approach |
dc.type | Article |
dc.subject.lemac | Intel·ligència artificial |
dc.subject.lemac | Aprenentatge automàtic |
dc.subject.lemac | Sistemes difusos |
dc.subject.ams | Classificació AMS::68 Computer science::68T Artificial intelligence |
dc.rights.access | Open Access |
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Aquest ítem apareix a les col·leccions següents
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1997, Vol. IV, Núm. 3 [10]
3rd. Hispano-Polish Symposium Systems Analysis & Computer Science