No abstract

We investigate dynamic versions of fuzzy logic systems (FLS's) and, specifically, their non-Singleton generalizations (NSFLS's), 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. This is illustrated through an example in nonlinear dynamic system identification. Since dynamic NSFLS's 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 FLS's in the predictive modeling of a NARMA process.

No abstract

We present a formulation of a fuzzy logic system (FLS) that can be used to construct nonparametric models of nonlinear processes, given only input-output data. In order to effectively construct such models, we discuss several design methods with different properties and features. We compare and illustrate systems designed with each one of the methods, using an example on the predictive modeling of a nonlinear dynamic (chaotic) system.