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Title of Journal: Neural Comput Applic

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Abbravation: Neural Computing and Applications

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Springer London

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DOI

10.1002/chin.201626214

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1433-3058

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A class of type2 fuzzy neural networks for nonlin

Authors: Jafar Tavoosi Mohammad Ali Badamchizadeh
Publish Date: 2012/06/16
Volume: 23, Issue: 3-4, Pages: 707-717
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Abstract

This paper presents the ability of the interval type2 Takagi–Sugeno–Kang fuzzy neural networks IT2TSKFNN for nonlinear dynamical system identification The proposed IT2TSKFNN has seven layers The first two layers consist of type2 fuzzy neurons with uncertainty in the mean of Gaussian membership functions Third layer is rule layer Typereduction 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 IT2TSKFNN is used for the identification of three nonlinear systems and then results are compared with adaptivenetworkbased fuzzy inference system ANFIS

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