Fuzzy models for system identification

A computerized environment for the automatic synthesis of a fuzzy model from numerical evidence is introduced. Such a fuzzy model (a controller or decisional one) is a binary-input single-output Mamdani type model. The main task is to adequate the model output to a system output sampled for some input-output relational values called training data. Thus, the model is a fuzzy approximator for the transfer function with description abilities. Fuzzy approaches are used for both the structure identification and optimization. Synthesized models are evaluated in practical cases.