Training neural networks and neuro-fuzzy systems: a unified view

Ruano, Antonio; Ferreira, P. M.; Cabrita, Cristiano Lourenço; Matos, S.

http://hdl.handle.net/10400.1/2313

Utilize este identificador para referenciar este registo.

Nome:	Descrição:	Tamanho:	Formato:
2795.pdf		113.58 KB	Adobe PDF	Ver/Abrir

Contacte-nos

Autores

Ruano, Antonio

Ferreira, P. M.

Cabrita, Cristiano Lourenço

Matos, S.

Resumo(s)

Neural and neuro-fuzzy models are powerful nonlinear modelling tools. Different structures, with different properties, are widely used to capture static or dynamical nonlinear mappings. Static (non-recurrent) models share a common structure: a nonlinear stage, followed by a linear mapping. In this paper, the separability of linear and nonlinear parameters is exploited for completely supervised training algorithms. Examples of this unified view are presented, involving multilayer perceptrons, radial basis functions, wavelet networks, B-splines, Mamdani and TSK fuzzy systems.

Palavras-chave

Neural Networks Neuro-Fuzzy Systems Multilayer Perceptrons Radial Basis Functions Wavelet Neural Networks

URI

http://hdl.handle.net/10400.1/2313

Citação

Ruano, A. E.; Ferreira, P.M.; Cabrita, C.; Matos, S. Training neural networks and neuro-fuzzy systems: A unified view, Trabalho apresentado em World Congress, In Proceedings of the 15th IFAC World Congress, 2002, Barcelona, 2002.

Editora

IFAC, Elsevier

Coleções

FCT2-Artigos (em revistas ou actas indexadas)

Ver registo completo