To Örebro University

oru.seÖrebro University Publications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Fuzzy Modeling for Uncertain Nonlinear Systems Using Fuzzy Equations and Z-Numbers
Centre for Artificial Intelligence Research (CAIR), University of Agder, Grimstad, Norway.
Departamento de Control Automático, CINVESTAV-IPN (National Polytechnic Institute), Mexico City, Mexico.
School of Computing, University of Portsmouth, Portsmouth, UK.
Örebro University, School of Science and Technology. School of Engineering and Sciences, Tecnológico de Monterrey, Monterrey, Mexico.ORCID iD: 0000-0003-4720-0897
2019 (English)In: Advances in Intelligent Systems and Computing / [ed] Lotfi, Ahmad; Bouchachia, Hamid; Gegov, Alexander; Langensiepen, Caroline; McGinnity, Martin, Springer, 2019, Vol. 840, p. 96-107Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
Springer, 2019. Vol. 840, p. 96-107
Keywords [en]
Fuzzy modeling, Z-number, Uncertain nonlinear system
National Category
Computational Mathematics Control Engineering
Identifiers
URN: urn:nbn:se:oru:diva-71789DOI: 10.1007/978-3-319-97982-3_8ISI: 000456013900008Scopus ID: 2-s2.0-85052217113OAI: oai:DiVA.org:oru-71789DiVA, id: diva2:1281991
Conference
UK Workshop on Computational Intelligence (UKCI), Nottingham, UK, September 5-7, 2018
Available from: 2019-01-23 Created: 2019-01-23 Last updated: 2021-12-30Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Paul, Satyam

Search in DiVA

By author/editor
Paul, Satyam
By organisation
School of Science and Technology
Computational MathematicsControl Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 300 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf