Fuzzy Modeling for Uncertain Nonlinear Systems Using Fuzzy Equations and Z-Numbers

  • Raheleh JafariEmail author
  • Sina Razvarz
  • Alexander Gegov
  • Satyam Paul
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 840)


In this paper, the uncertainty property is represented by Z-number as the coefficients and variables of the fuzzy equation. This modification for the fuzzy equation is suitable for nonlinear system modeling with uncertain parameters. Here, we use fuzzy equations as the models for the uncertain nonlinear systems. The modeling of the uncertain nonlinear systems is to find the coefficients of the fuzzy equation. However, it is very difficult to obtain Z-number coefficients of the fuzzy equations.

Taking into consideration the modeling case at par with uncertain nonlinear systems, the implementation of neural network technique is contributed in the complex way of dealing the appropriate coefficients of the fuzzy equations. We use the neural network method to approximate Z-number coefficients of the fuzzy equations.


Fuzzy modeling Z-number Uncertain nonlinear system 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Centre for Artificial Intelligence Research (CAIR)University of AgderGrimstadNorway
  2. 2.Departamento de Control AutomáticoCINVESTAV-IPN (National Polytechnic Institute)Mexico CityMexico
  3. 3.School of ComputingUniversity of PortsmouthPortsmouthUK
  4. 4.School of Engineering and SciencesTecnológico de MonterreyMonterreyMexico

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