Abstract
Fuzzy modeling is an effective approach for system identification. It is based on fuzzy sets and logic and describes the system behaviour by means of fuzzy IF-THEN rules. In its turn, data driven fuzzy modeling (DDFM) extracts these models from a set of input-output observations about the system. Three main stages compose DDFM: rules number identification, rules generation and parameter optimization. One way to carry out a DDFM process is by means of a combination of techniques, each one solving one of the DDFM phases. In this paper, the authors applied hybridizations of clustering algorithms and neural networks (NN) in order to solve several regression problems from different domains showing up the suitability and success of hybridization in DDFM
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