Identification of enhanced fuzzy models with special membership functions and fuzzy rule bases
A special fuzzy modeling method for developing multi-variable fuzzy models on the basis of measured input and output data is presented. Forming the crucial point in fuzzy modeling, the fuzzy model identification procedure is carried out by applying a special clustering method, the fuzzy c-elliptotypes method, to provide the parameters of the fuzzy model. To enhance the efficiency of the fuzzy model, the rather simple membership functions defined at first for the input fuzzy sets are replaced by a special class of functions. Additionally, the conventional rule base expressing the main links between the inputs and the outputs of the fuzzy model is replaced by a fuzzy rule base with fuzzy assignments, leading to further improvements of the fuzzy model.
fuzzy modeling fuzzy logic fuzzy systems nonlinear models model-based control