Title:
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Método multiobjetivo de aprendizaje para razonamiento inductivo difuso
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Author:
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Acosta, Jesús; Nebot Castells, M. Àngela; Fuertes Armengol, José Mª
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Other authors:
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Universitat Politècnica de Catalunya. Departament de Llenguatges i Sistemes Informàtics; Universitat Politècnica de Catalunya. SOCO - Soft Computing; Universitat Politècnica de Catalunya. GRINS - Grup de Recerca en Robòtica Intel·ligent i Sistemes |
Abstract:
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It has been recognized in various studies that the variations in the granularity (number of classes per variable) and the membership functions have a significant effect in the behaviour of the fuzzy systems. The FIR methodology is not an exception. The efficiency of the qualitative model identification and fuzzy forecast processes of FIR is very influenced by the fuzzification parameters of the system variables (i.e. number of classes and shape of the membership functions). To resolve this problematic we have been presented in previous works hybrid methodologies called Genetic Fuzzy Systems (GFSs) that try to learn in a joint way or by separated those parameters. These methods have used monoobjetive functions for the evaluation of the chromosomes. In this investigation another method of automatic learning is presented. This new method permits to obtain at the same time the fuzzification parameters of the FIR methodology but using Multiobjective Genetic Algorithms. Its main components are described and the results obtained on an environmental application are presented. |
Subject(s):
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-Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial -Algoritmos genéticos multiobjetivo -Razonamiento inductivo difuso -Sistemas genéticos difusos -Machine learning -Concentraciones de ozono -Contaminación del aire -Modelado medioambiental -Multiobjective genetic algorithms -Fuzzy inductive reasoning |
Rights:
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Document type:
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Article - Published version Report |
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