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Neural Computing and Applications

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01.09.2013 | Original Article

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

Erschienen in: Neural Computing and Applications | Ausgabe 3-4/2013

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Abstract

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).

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Titel: A class of type-2 fuzzy neural networks for nonlinear dynamical system identification
Publikationsdatum: 01.09.2013
Erschienen in: Neural Computing and Applications / Ausgabe 3-4/2013
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-012-0981-7

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