Fuzzy identification of systems with unsupervised learning

The present paper describes a mathematical tool to build a fuzzy model whose membership functions and consequent parameters rely on the estimates of a data set. The proposed method proved to be capable of approximating any real continuous function, also if a strongly nonlinear one, on a compact set to arbitrary accuracy. Without resorting to domain experts, the algorithm constructs a model-free, complete function approximation system. Applications to the modelling of several functions among which classical nonlinear ones such as the Rosenbrock and the sine (x.y) functions are reported, The proposed algorithm can find applications in the development of fuzzy logic controllers (FLC).