- author = "Ismael {Rodriguez Fernandez}",
- title = "Evolutionary Learning of Fuzzy Rules for Regression",
- school = "Centro Singular de Investigacion en Tecnoloxias da Informacion (CiTIUS), Departamento de Electronica e Computacion, Universidade de Santiago de Compostela",
- year = "2016",
- address = "Spain",
- month = "16 " # dec,
- keywords = "genetic algorithms, genetic programming, mobile robotics, Genetic Fuzzy Systems, Iterative Quantified Fuzzy Rule Learning (IQFRL)",
-
URL = "
https://citius.usc.es/novidades/eventos/phd-defense-evolutionary-learning-fuzzy-rules-regression",
-
URL = "http://hdl.handle.net/10347/15153",
-
URL = "
https://minerva.usc.es/xmlui/bitstream/handle/10347/15153/rep_1303.pdf?sequence=1&isAll",
- size = "176 pages",
- abstract = "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.",
-
notes = "In english
Supervisors: Alberto J. Bugarin Diz, Manuel Mucientes Molina",
