A learning procedure to identify weighted rules by neural networks

In many cases the identification of systems by means of fuzzy rules is given by taking these rules from a predetermined set of possible ones. In this case, the correct description of the system is to be given by a finite set of rules each with an associated weight which assesses its correctness or accuracy. Here we present a method to learn this consistence level or weight by a neural network. The design of this neural network as well as the features of the training models are discussed. The paper concludes with an example.