Now comes the time to defuzzify neuro-fuzzy models

Fuzzy models present a singular Janus-faced: On the one hand, they are knowledge-based software environments constructed from a collection of linguistic IF-THEN rules, and on the other hand, they realize nonlinear mappings which have interesting mathematical properties like "low-order interpolation", "smooth cooperation between local approximators" and "universal function approximation". Within this second vision, fuzzy models can be taken as additional members in the large family of multi-expert networks which already count as members: Radial Basis Functions, GRNN, CMAC, B-splines network, Locally Weighted Learning or Regression, Kernel Regression Estimator, Jordan and Jacob's mixture of experts, etc.. Here we will focus on this second vision trying to point out what remains original in the fuzzy approach as compared with the other members, then describing some learning strategies of these fuzzy models and presenting comparative experimental results on a classical time series prediction benchmark.