Fuzzy Modeling for Uncertain Nonlinear Systems Using Fuzzy Equations and Z-Numbers
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
2019-01-232019-01-232021-12-30Bibliographically approved