Linguistic modelling based on experimental data

This paper describes a method for constructing linguistic models from observed data. A linguistic model is derived from the reduction, based on clustering, of the number of fuzzy sets and rules which constitute a fuzzy model. This, in its turn, is built by applying a new method, called the local approximation method which determines a piecewise linear approximation of a set of samples of the system to be modelled. The approximation error due to linearisation can be chosen based on the degree of detail of the required model. In particular, if the final model is a linguistic one, the major requirements are readability and understandability, which normally correspond to reduced precision