Neuro-fuzzy systems for function approximation
We present a neuro-fuzzy architecture for function approximation based on supervised learning. The learning algorithm is able to determine the structure and the parameters of a fuzzy system. The approach is an extension to our already published NEFCON and NEFCLASS models which are used for control or classification purposes. The proposed extended model, which we call NEFPROX, is more general and can be used for any application based on function approximation. (C) 1999 Elsevier Science B.V. All rights reserved.
neuro-fuzzy system function approximation structure learning parameter learning