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Fuzzy Modeling for Uncertain Nonlinear Systems Using Fuzzy Equations and Z-Numbers

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Advances in Computational Intelligence Systems (UKCI 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 840))

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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.

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Author information

Authors and Affiliations

  1. Centre for Artificial Intelligence Research (CAIR), University of Agder, 4879, Grimstad, Norway

    Raheleh Jafari

  2. Departamento de Control Automático, CINVESTAV-IPN (National Polytechnic Institute), 07360, Mexico City, Mexico

    Sina Razvarz

  3. School of Computing, University of Portsmouth, Buckingham Building, Portsmouth, PO13HE, UK

    Alexander Gegov

  4. School of Engineering and Sciences, Tecnológico de Monterrey, 64849, Monterrey, Nuevo Leon, Mexico

    Satyam Paul

Authors
  1. Raheleh Jafari
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  2. Sina Razvarz
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  3. Alexander Gegov
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  4. Satyam Paul
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Corresponding author

Correspondence to Raheleh Jafari .

Editor information

Editors and Affiliations

  1. School of Science and Technology, Nottingham Trent University, Nottingham, United Kingdom

    Ahmad Lotfi

  2. Faculty of Science and Technology, Bournemouth University, Poole, Dorset, United Kingdom

    Hamid Bouchachia

  3. School of Computing, University of Portsmouth, Portsmouth, Hampshire, United Kingdom

    Alexander Gegov

  4. School of Science and Technology, Nottingham Trent University, Nottingham, United Kingdom

    Caroline Langensiepen

  5. College of Science and Technology, Nottingham Trent University, Nottingham, United Kingdom

    Martin McGinnity

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Jafari, R., Razvarz, S., Gegov, A., Paul, S. (2019). Fuzzy Modeling for Uncertain Nonlinear Systems Using Fuzzy Equations and Z-Numbers. In: Lotfi, A., Bouchachia, H., Gegov, A., Langensiepen, C., McGinnity, M. (eds) Advances in Computational Intelligence Systems. UKCI 2018. Advances in Intelligent Systems and Computing, vol 840. Springer, Cham. https://doi.org/10.1007/978-3-319-97982-3_8

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