Abstract
In this paper, we propose an indirect method to fuzzy modeling which implements a clustering algorithm to build a linguistic fuzzy controller. Based on output data clustering and projection onto the input spaces, the number of clusters is determined and rules are generated automatically. A new methodology based on output sensitivity is developed for input variable selection. Then, implementing an Adapted Neural Network for the selection of membership functions optimizes all membership function parameters. The unbounded parameters of fuzzy operators and the inference methods of FATI (First Aggregate, Then Infer) and FITA (First Infer, Then Aggregate) are optimized through a simple and efficient tuning strategy.