A fuzzy neural hybrid system modeling

In this paper, we propose and discuss a fuzzy-neural system development schema. For this purpose, we identify three knowledge representation and approximate reasoning approaches. For the Type I fuzzy theory, we describe the extraction of fuzzy sets and fuzzy rules with the application of an improved fuzzy clustering technique which is essentially an unsupervised learning of the fuzzy sets and rules from a given input-output data set. Next we describe how this set of rules and their fuzzy sets may be adapted and/or modified for known target sets with supervised learning within a fuzzified neural network architecture. Finally, we introduce a unified (fuzzy) approximate reasoning formulation for fuzzy modeling and control.