Evolutionary learning of fuzzy rules for regression

  1. Rodríguez Fernández, Ismael
unter der Leitung von:
  1. Alberto José Bugarín Diz Doktorvater
  2. Manuel Mucientes Molina Co-Doktorvater

Universität der Verteidigung: Universidade de Santiago de Compostela

Fecha de defensa: 16 von Dezember von 2016

Gericht:
  1. Francisco Herrera Triguero Präsident/in
  2. Senén Barro Sekretär
  3. Paulo Cortez Vocal
Fachbereiche:
  1. Departamento de Electrónica e Computación

Art: Dissertation

Teseo: 441274 DIALNET lock_openMINERVA editor

Zusammenfassung

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.