Data driven fuzzy logic systems for system modeling

An interpretation is discussed with regard to using fuzzy logic systems (FLSs) as system models. The development of FLSs by a pseudo-clustering technique is presented which bypasses the use of conventional clustering algorithms. This method is shown to provide reasonably good responses with very little development overhead. A second modeling technique relies on interpreting the available data itself as the FLS. These methods provide a transition between artificial neural network (ANN) realizations and classical FLSs, in that most of their computations could be performed in parallel.