Neural network approach to solving fuzzy nonlinear equations using Z-numbers

Raheleh  Jafari; Sina  Razvarz; Alexander Gegov

doi:10.1109/TFUZZ.2019.2940919

Neural network approach to solving fuzzy nonlinear equations using Z-numbers

Raheleh Jafari, Sina Razvarz, Alexander Gegov

School of Computing

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Abstract

In this work, the fuzzy property is described by means of the Z-number as the coefficients and variables of the fuzzy equations. This alteration for the fuzzy equation is appropriate for system modeling with Z-number parameters. In this paper, the fuzzy equation with Z-number coefficients and variables is tended to be used as the models for the uncertain systems. The modeling issue related to the uncertain system is to obtain the Z-number coefficients and variables of the fuzzy equation. Nevertheless, it is extremely hard to get the Z-number coefficients of the fuzzy equations. In this paper in order to model the uncertain nonlinear systems, a novel structure of the multilayer neural network is utilized in such a manner that it is able to obtain the Z-number coefficients of the fuzzy equation. The suggested technique is validated by some examples with applications.

Original language	English
Pages (from-to)	1230-1241
Number of pages	11
Journal	IEEE Transactions on Fuzzy Systems
Volume	28
Issue number	7
Early online date	11 Sept 2019
DOIs	https://doi.org/10.1109/TFUZZ.2019.2940919
Publication status	Early online - 11 Sept 2019

Keywords

Uncertain nonlinear system
fuzzy equation
Z number
multilayer neural network

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year = "2019",

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T1 - Neural network approach to solving fuzzy nonlinear equations using Z-numbers

AU - Jafari, Raheleh

AU - Razvarz, Sina

AU - Gegov, Alexander

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N2 - In this work, the fuzzy property is described by means of the Z-number as the coefficients and variables of the fuzzy equations. This alteration for the fuzzy equation is appropriate for system modeling with Z-number parameters. In this paper, the fuzzy equation with Z-number coefficients and variables is tended to be used as the models for the uncertain systems. The modeling issue related to the uncertain system is to obtain the Z-number coefficients and variables of the fuzzy equation. Nevertheless, it is extremely hard to get the Z-number coefficients of the fuzzy equations. In this paper in order to model the uncertain nonlinear systems, a novel structure of the multilayer neural network is utilized in such a manner that it is able to obtain the Z-number coefficients of the fuzzy equation. The suggested technique is validated by some examples with applications.

AB - In this work, the fuzzy property is described by means of the Z-number as the coefficients and variables of the fuzzy equations. This alteration for the fuzzy equation is appropriate for system modeling with Z-number parameters. In this paper, the fuzzy equation with Z-number coefficients and variables is tended to be used as the models for the uncertain systems. The modeling issue related to the uncertain system is to obtain the Z-number coefficients and variables of the fuzzy equation. Nevertheless, it is extremely hard to get the Z-number coefficients of the fuzzy equations. In this paper in order to model the uncertain nonlinear systems, a novel structure of the multilayer neural network is utilized in such a manner that it is able to obtain the Z-number coefficients of the fuzzy equation. The suggested technique is validated by some examples with applications.

KW - Uncertain nonlinear system

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JO - IEEE Transactions on Fuzzy Systems

JF - IEEE Transactions on Fuzzy Systems

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Neural network approach to solving fuzzy nonlinear equations using Z-numbers

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Keywords

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