Learning fuzzy rule-based neural networks for function approximation

Abstract

The authors present a method for the induction of fuzzy logic rules to predict a numerical function from samples of the function and its dependent variables. This method uses an information-theoretic approach based on the authors' previous work with discrete-valued data (see Proc. Int. Joint. Conf. on Neur. Net., vol.1, p.875-80, 1991). The rules learned can then be used in a neural network to predict the function value based on its dependent variables. An example is shown of learning a control system function.<>