Evolutionary learning of fuzzy rules for regression
- Rodríguez Fernández, Ismael
- Alberto José Bugarín Diz Director
- Manuel Mucientes Molina Co-director
Universidade de defensa: Universidade de Santiago de Compostela
Fecha de defensa: 16 de decembro de 2016
- Francisco Herrera Triguero Presidente/a
- Senén Barro Secretario
- Paulo Cortez Vogal
Tipo: Tese
Resumo
The objective of this PhD Thesis is to design Genetic Fuzzy Systems (GFS) that learn Fuzzy Rule Based Systems to solve regression problems in a general manner. Particularly, the aim is to obtain models with low complexity while maintaining high precision without using expert-knowledge about the problem to be solved. This means that the GFSs have to work with raw data, that is, without any preprocessing that help the learning process to solve a particular problem. This is of particular interest, when no knowledge about the input data is available or for a first approximation to the problem. Moreover, within this objective, GFSs have to cope with large scale problems, thus the algorithms have to scale with the data.