Multi-stage genetic fuzzy systems based on the iterative rule learning approach
ISSN: 1134-5632
Année de publication: 1997
Volumen: 4
Número: 3
Pages: 233-249
Type: Article
D'autres publications dans: Mathware & soft computing: The Magazine of the European Society for Fuzzy Logic and Technology
Résumé
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.