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

Raheleh Jafari, Sina Razvarz, Alexander Gegov

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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 languageEnglish
Pages (from-to)1230-1241
Number of pages11
JournalIEEE Transactions on Fuzzy Systems
Volume28
Issue number7
Early online date11 Sep 2019
DOIs
Publication statusEarly online - 11 Sep 2019

Keywords

  • Uncertain nonlinear system
  • fuzzy equation
  • Z number
  • multilayer neural network

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