Tesis doctoral de Jesús Antonio Acosta Sarmiento
It is commonly established that more intelligent systems can be obtained by the hybridization of soft computing methodologies, in order that the weaknesses of some systems be compensated with the strengths of others . Neural fuzzy systems (nfss) and evolutionary fuzzy systems (efss) are the most notorious representatives of these hybrid systems. An evolutionary fuzzy system is basically a fuzzy system augmented by a learning process based on an evolutionary algorithm (ea), particularly genetic algorithms (gas), which are currently considered as the most well-known employed global search technique. This kind of algorithms have the ability to explore and to exploit complex search spaces, which allows the obtaining of solutions very close to the optimal ones within these spaces. Besides, the genetic codification employed allows to incorporate a priori knowledge in a very simple way and to use it to guide the search. In this phd. Thesis, we propose efss that improves a modeling and simulation technique the fuzzy inductive reasoning (fir). The main goal of the efss is to take advantage of the potentialities of gas to learn the fuzzification parameters of fir, i.E. The number of classes per variable (granularity) and the membership functions (landmarks) that define its semantics. Due to the fact that it is a methodology based on fuzzy logic, fir modeling and prediction performance is directly influenced by these discretization parameters. Therefore, the automatic determination of precise fuzzification parameters in the fir methodology is an interesting and useful alternative to the use of heuristics and/or default values. Moreover, it is expected that the automatic selection of adequate values for these parameters will open up the fir methodology to new users, with no experience neither in systems modeling nor in fuzzy logic, guaranteeing the best performance of this methodology. Three evolutionary methods of automatic learning of fuzzy partitions are presented: a) the le
Datos académicos de la tesis doctoral «Aprendizaje de particiones difusas para razonamiento inductivo«
- Título de la tesis: Aprendizaje de particiones difusas para razonamiento inductivo
- Autor: Jesús Antonio Acosta Sarmiento
- Universidad: Politécnica de catalunya
- Fecha de lectura de la tesis: 22/12/2006
Dirección y tribunal
- Director de la tesis
- Josep María Fuertes I Armengol
- Tribunal
- Presidente del tribunal: e. Cellier francois
- pedro Villar castro (vocal)
- cecilio Angulo bahon (vocal)
- rené Alquézar mancho (vocal)