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Neural Computing and Applications
Published in: Neural Computing and Applications 3-4/2013

01-09-2013 | Original Article

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

Authors: Jafar Tavoosi, Mohammad Ali Badamchizadeh

Published in: Neural Computing and Applications | Issue 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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Metadata
Title
A class of type-2 fuzzy neural networks for nonlinear dynamical system identification
Authors
Jafar Tavoosi
Mohammad Ali Badamchizadeh
Publication date
01-09-2013
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 3-4/2013
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-012-0981-7

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