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

Raheleh  Jafari; Sina  Razvarz; Alexander Gegov; Paul Satyam

doi:10.1007/978-3-319-97982-3_8

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

Raheleh Jafari, Sina Razvarz, Alexander Gegov, Paul Satyam

School of Computing

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English
Title of host publication	Advances in Computational Intelligence Systems
Subtitle of host publication	Contributions Presented at the 18th UK Workshop on Computational Intelligence, September 5-7, 2018, Nottingham, UK
Editors	Ahmad Lotfi, Hamid Bouchachia, Alexander Gegov, Caroline Langensiepen, Martin McGinnity
Publisher	Springer
Pages	96-107
ISBN (Electronic)	978-3-319-97982-3
ISBN (Print)	978-3-319-97981-6
DOIs	https://doi.org/10.1007/978-3-319-97982-3_8
Publication status	Published - Sept 2018
Event	18th UK Workshop on Computational Intelligence - Nottingham, United Kingdom Duration: 5 Sept 2018 → 7 Sept 2018

Publication series

Name	Advances in Intelligent Systems and Computing
Publisher	Springer
Volume	840
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

Workshop

Workshop	18th UK Workshop on Computational Intelligence
Country/Territory	United Kingdom
Period	5/09/18 → 7/09/18

Keywords

Fuzzy Modeling
Z-number
Uncertain Nonlinear System

Access to Document

10.1007/978-3-319-97982-3_8

UKCI_2018_PAPER_2
The final authenticated version is available online at: http://dx.doi.org/10.1007/978-3-319-97982-3_8.
Accepted author manuscript (Post-print), 386 KBLicence: Unspecified

Cite this

Jafari, R., Razvarz, S., Gegov, A., & Satyam, P. (2018). Fuzzy modeling for uncertain nonlinear systems using fuzzy equations and Z-numbers. In A. Lotfi, H. Bouchachia, A. Gegov, C. Langensiepen, & M. McGinnity (Eds.), Advances in Computational Intelligence Systems: Contributions Presented at the 18th UK Workshop on Computational Intelligence, September 5-7, 2018, Nottingham, UK (pp. 96-107). (Advances in Intelligent Systems and Computing; Vol. 840). Springer. https://doi.org/10.1007/978-3-319-97982-3_8

Jafari, Raheleh ; Razvarz, Sina ; Gegov, Alexander et al. / Fuzzy modeling for uncertain nonlinear systems using fuzzy equations and Z-numbers. Advances in Computational Intelligence Systems: Contributions Presented at the 18th UK Workshop on Computational Intelligence, September 5-7, 2018, Nottingham, UK. editor / Ahmad Lotfi ; Hamid Bouchachia ; Alexander Gegov ; Caroline Langensiepen ; Martin McGinnity. Springer, 2018. pp. 96-107 (Advances in Intelligent Systems and Computing).

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title = "Fuzzy modeling for uncertain nonlinear systems using fuzzy equations and Z-numbers",

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 fuzzyequations.",

keywords = "Fuzzy Modeling, Z-number, Uncertain Nonlinear System",

author = "Raheleh Jafari and Sina Razvarz and Alexander Gegov and Paul Satyam",

year = "2018",

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doi = "10.1007/978-3-319-97982-3_8",

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editor = "Ahmad Lotfi and Hamid Bouchachia and Alexander Gegov and Caroline Langensiepen and Martin McGinnity",

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Jafari, R, Razvarz, S, Gegov, A & Satyam, P 2018, Fuzzy modeling for uncertain nonlinear systems using fuzzy equations and Z-numbers. in A Lotfi, H Bouchachia, A Gegov, C Langensiepen & M McGinnity (eds), Advances in Computational Intelligence Systems: Contributions Presented at the 18th UK Workshop on Computational Intelligence, September 5-7, 2018, Nottingham, UK. Advances in Intelligent Systems and Computing, vol. 840, Springer, pp. 96-107, 18th UK Workshop on Computational Intelligence, United Kingdom, 5/09/18. https://doi.org/10.1007/978-3-319-97982-3_8

Fuzzy modeling for uncertain nonlinear systems using fuzzy equations and Z-numbers. / Jafari, Raheleh ; Razvarz, Sina ; Gegov, Alexander et al.
Advances in Computational Intelligence Systems: Contributions Presented at the 18th UK Workshop on Computational Intelligence, September 5-7, 2018, Nottingham, UK. ed. / Ahmad Lotfi; Hamid Bouchachia; Alexander Gegov; Caroline Langensiepen; Martin McGinnity. Springer, 2018. p. 96-107 (Advances in Intelligent Systems and Computing; Vol. 840).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Fuzzy modeling for uncertain nonlinear systems using fuzzy equations and Z-numbers

AU - Jafari, Raheleh

AU - Razvarz, Sina

AU - Gegov, Alexander

AU - Satyam, Paul

PY - 2018/9

Y1 - 2018/9

N2 - 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 fuzzyequations.

AB - 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 fuzzyequations.

KW - Fuzzy Modeling

KW - Z-number

KW - Uncertain Nonlinear System

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DO - 10.1007/978-3-319-97982-3_8

M3 - Conference contribution

SN - 978-3-319-97981-6

T3 - Advances in Intelligent Systems and Computing

SP - 96

EP - 107

BT - Advances in Computational Intelligence Systems

A2 - Lotfi, Ahmad

A2 - Bouchachia, Hamid

A2 - Gegov, Alexander

A2 - Langensiepen, Caroline

A2 - McGinnity, Martin

PB - Springer

T2 - 18th UK Workshop on Computational Intelligence

Y2 - 5 September 2018 through 7 September 2018

ER -

Jafari R, Razvarz S, Gegov A, Satyam P. Fuzzy modeling for uncertain nonlinear systems using fuzzy equations and Z-numbers. In Lotfi A, Bouchachia H, Gegov A, Langensiepen C, McGinnity M, editors, Advances in Computational Intelligence Systems: Contributions Presented at the 18th UK Workshop on Computational Intelligence, September 5-7, 2018, Nottingham, UK. Springer. 2018. p. 96-107. (Advances in Intelligent Systems and Computing). Epub 2018 Aug 11. doi: 10.1007/978-3-319-97982-3_8

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

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Keywords

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