Fuzzy modeling for uncertain nonlinear systems using fuzzy equations and Z-numbers

Raheleh Jafari, Sina Razvarz, Alexander Gegov, Paul Satyam

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

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.
Original languageEnglish
Title of host publicationAdvances in Computational Intelligence Systems
Subtitle of host publicationContributions Presented at the 18th UK Workshop on Computational Intelligence, September 5-7, 2018, Nottingham, UK
EditorsAhmad Lotfi, Hamid Bouchachia, Alexander Gegov, Caroline Langensiepen, Martin McGinnity
PublisherSpringer
Pages96-107
ISBN (Electronic)978-3-319-97982-3
ISBN (Print)978-3-319-97981-6
DOIs
Publication statusPublished - Sep 2018
Event18th UK Workshop on Computational Intelligence - Nottingham, United Kingdom
Duration: 5 Sep 20187 Sep 2018

Publication series

NameAdvances in Intelligent Systems and Computing
PublisherSpringer
Volume840
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Workshop

Workshop18th UK Workshop on Computational Intelligence
Country/TerritoryUnited Kingdom
Period5/09/187/09/18

Keywords

  • Fuzzy Modeling
  • Z-number
  • Uncertain Nonlinear System

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