Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.1/2210
Título: Supervised training algorithms for B-spline neural networks and fuzzy systems
Autor: Ruano, A. E.
Cabrita, Cristiano Lourenço
Oliveira, J. V.
Tikk, D.
Kóczy, László T.
Palavras-chave: Training algorithms
Neural networks
B-splines
Fuzzy systems
Llinear and nonlinear separability
Data: 2001
Editora: IEEE
Citação: Ruano, A. E.; Cabrita, C.; Oliveira, J. V.; Tikk, D.; Koczy, L. T. Supervised training algorithms for B-spline neural networks and fuzzy systems, Trabalho apresentado em Joint 9th IFSA World Congress and 20th NAFIPS International Conference, In Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569), Vancouver, BC, Canada, 2001.
Resumo: Complete supervised training algorithms for B-spline neural networks and fuzzy rule-based systems are discussed. By introducing the relationships between B-spline neural networks and Mamdani (satisfying certain assumptions) fuzzy model, training algorithms developed initially for neural networks can be adapted to fuzzy systems. The standard training criterion is reformulated, by separating the linear and nonlinear parameters. By employing this reformulated criterion with the Levenberg-Marquardt algorithm, a new training method, offering a fast rate of convergence is obtained. It is also shown that the standard Error-Back Propagation algorithm, the most common training method for this class of systems, exhibits a very poor performance.
Peer review: yes
URI: http://hdl.handle.net/10400.1/2210
ISBN: 0-7803-7078-3
Aparece nas colecções:FCT2-Artigos (em revistas ou actas indexadas)

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