Evolution of Voronoi based fuzzy recurrent controllers
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Identification of a class of nonlinear systems by a continuous-time recurrent neurofuzzy network
ACC'09 Proceedings of the 2009 conference on American Control Conference
A recurrent self-evolving interval type-2 fuzzy neural network for dynamic system processing
IEEE Transactions on Fuzzy Systems
Black-box identification of a class of nonlinear systems by a recurrent neurofuzzy network
IEEE Transactions on Neural Networks
Recurrent wavelet-based neuro fuzzy networks for dynamic system identification
Mathematical and Computer Modelling: An International Journal
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We investigate dynamic versions of fuzzy logic systems (FLSs) and, specifically, their non-Singleton 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. 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 FLSs in the predictive modeling of a NARMA process