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Numerical Solution of Fuzzy Equations with Z-numbers Using Neural Networks

Raheleh Jafaria, Wen Yua, Xiaoou Lib

a Departamento de Control Automático, CINVESTAV-IPN (National Polytechnic Institute), Mexico City, Mexico;
b Departamento de Computación, CINVESTAV-IPN (National Polytechnic Institute), Mexico City, Mexico

* Corresponding Author:Wen Yu, email

Intelligent Automation & Soft Computing 2018, 24(1), 151-158.


In this paper, the uncertainty property is represented by the Z-number as the coefficients of the fuzzy equation. This modification for the fuzzy equation is suitable for nonlinear system modeling with uncertain parameters. We also extend the fuzzy equation into dual type, which is natural for linearin-parameter nonlinear systems. The solutions of these fuzzy equations are the controllers when the desired references are regarded as the outputs. The existence conditions of the solutions (controllability) are proposed. Two types of neural networks are implemented to approximate solutions of the fuzzy equations with Z-number coefficients.


Cite This Article

. Raheleh Jafari, . Wen Yu and . Xiaoou Li, "Numerical solution of fuzzy equations with z-numbers using neural networks," Intelligent Automation & Soft Computing, vol. 24, no.1, pp. 151–158, 2018.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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