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Nonlinear predictive modeling using dynamic non-singleton fuzzy logic systems

We investigate the dynamic versions of fuzzy logic systems (FLSs), and specifically their nonsingleton generalizations (NSFLSs), and derive a dynamic learning algorithm to train the system parameters. The history-sensitive output of the dynamic systems gives them a significant advantage over static systems in modeling processes of unknown order. Since dynamic NSFLSs can be considered to belong to the family of general nonlinear autoregressive moving average (NARMA) models, they are capable of parsimoniously modeling NARMA processes. We study the performance of both dynamic and static FLSs in the predictive modeling of a NARMA process.