Multi-stage genetic fuzzy systems based on the iterative rule learning approach
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 contributi...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 1997 |
| País: | España |
| Institución: | Universitat Politècnica de Catalunya (UPC) |
| Repositorio: | UPCommons. Portal del coneixement obert de la UPC |
| Idioma: | inglés |
| OAI Identifier: | oai:upcommons.upc.edu:2099/3495 |
| Acceso en línea: | https://hdl.handle.net/2099/3495 |
| Access Level: | acceso abierto |
| Palabra clave: | Fuzzy logic Fuzzy rules Genetic algoritms Machine learning GFS Genetic fuzzy systems Intel·ligència artificial Aprenentatge automàtic Sistemes difusos Classificació AMS::68 Computer science::68T Artificial intelligence |
| Sumario: | 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. |
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