Advisor(s)
Abstract(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.
Description
Keywords
Neural Networks Neuro-Fuzzy Systems Multilayer Perceptrons Radial Basis Functions Wavelet Neural Networks
Citation
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.