A class of type-2 fuzzy neural networks for nonlinear dynamical system identification

摘要

This paper presents the ability of the interval type-2 Takagi-Sugeno-Kang fuzzy neural networks (IT2-TSK-FNN) for nonlinear dynamical system identification. The proposed IT2-TSK-FNN has seven layers. The first two layers consist of type-2 fuzzy neurons with uncertainty in the mean of Gaussian membership functions. Third layer is rule layer. Type-reduction is done in fourth layer. In the fifth, sixth, and seventh layers, consequent left-right firing points, two end points, and output are evaluated, respectively. In this paper, gradient descent with adaptive learning rate backpropagation is used in learning phase. IT2-TSK-FNN is used for the identification of three nonlinear systems, and then results are compared with adaptive-network-based fuzzy inference system (ANFIS).