Ponencia
Thing Complex Fuzzy Systems by Supervised Learning Algorithms
Autor/es | Moreno Velo, Francisco José
Baturone, I. Senhadji Navarro, Raouf Sánchez Solano, Santiago |
Departamento | Universidad de Sevilla. Departamento de Arquitectura y Tecnología de Computadores |
Fecha de publicación | 2003 |
Fecha de depósito | 2020-07-07 |
Publicado en |
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ISBN/ISSN | 0-7803-7810-5 |
Resumen | Tuning a fuzzy system to meet a given set of inpuffoutput
patterns is usually a difficult task that involves many parameters.
This paper presents an study of different approaches
that can be applied to perform this ... Tuning a fuzzy system to meet a given set of inpuffoutput patterns is usually a difficult task that involves many parameters. This paper presents an study of different approaches that can be applied to perform this tuning process automatically, and describes a CAD tool, named xfsl, which allows applying a wide set of these approaches: (a) a large number of supervised learning algorithms; (b) different processes to simplify the learned system; (c) tuning only specific parameters of the system; (d) the ability to tune hierarchical fuzzy systems, systems with continuous output (like fuzzy controller) as well as with categorical output (like fuzzy classifiers), and even systems that employ user-defined fuzzy functions; and, finally, (e) the ability to employ this tuning within the design flow of a fuzzy system, because xfsl is integrated into the fuzzy system development environment Xfuzzy 3.0. |
Agencias financiadoras | Comisión Interministerial de Ciencia y Tecnología (CICYT). España |
Identificador del proyecto | TIC2001-1726-C02-01 |
Cita | Moreno Velo, F.J., Baturone, I., Senhadji Navarro, R. y Sánchez Solano, S. (2003). Thing Complex Fuzzy Systems by Supervised Learning Algorithms. En FUZZ 2003: 12th IEEE International Conference on Fuzzy Systems (226-231), St Louis, MO, USA: IEEE Computer Society. |
Ficheros | Tamaño | Formato | Ver | Descripción |
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Tuning complex fuzzy systems.pdf | 431.3Kb | [PDF] | Ver/ | |