Abstract
In this paper a fuzzy neural network based on a fuzzy relational “IF-THEN” reasoning scheme is designed. To define the structure of the model different
t-norms and
t-conorms are proposed. The fuzzification and the defuzzification phases are then added to the model so that we can consider the model like a controller. A learning algorithm to tune the parameters that is based on a back-propagation algorithm and a recursive pseudoinverse matrix technique is introduced. Different experiments on synthetic and benchmark data are made. Several results using the UCI repository of Machine learning database are showed for classification and approximation tasks. The model is also compared with some other methods known in literature.