Modeling of chaotic time series by interval type-2 NEO-fuzzy neural network

Abstract

This paper describes the development of Interval Type-2 NEO-Fuzzy Neural Network for modeling of complex dynamics. The proposed network represents a parallel set of multiple zero order Sugeno type approximations, related only to their own input argument. The induced gradient based learning procedure, adjusts solely the consequent network parameters. To improve the robustness of the network and the possibilities for handling uncertainties, Type-2 Gaussian fuzzy sets are introduced into the network topology. The potentials of the proposed approach in modeling of Mackey-Glass and Rossler Chaotic time series are studied. © 2014 Springer International Publishing Switzerland.