A rapid-prototyping approach to fuzzy system modeling

We develop a three-phase design scheme for fuzzy modeling using input-output training data. The first step is to establish an approximate rule base using a clustering method, then the selective rule activation technique is applied to resize the rule base, and finally the parameters are fine-tuned by the backpropagation algorithm. Simulation results show that this scheme generates a good fuzzy model that can successfully predict the system output and the rules selected by the selective rule activation technique are appropriate for generalizing different sets of data.