Evolutionary learning of fuzzy rules for regression

Autores: Ismael Rodríguez Fernández
Directores de la Tesis: Alberto José Bugarín Diz (dir. tes.) , Manuel Mucientes Molina (codir. tes.)
Lectura: En la Universidade de Santiago de Compostela ( España ) en 2016
Idioma: inglés
Tribunal Calificador de la Tesis: Francisco Herrera Triguero (presid.) , Senén Barro (secret.) , Paulo Cortez (voc.)
Enlaces
- Tesis en acceso abierto en: MINERVA
Resumen
- 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.