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

Jafari, Raheleh; Razvarz, Sina; Gegov, Alexander; Paul, Satyam

Abstract

© Springer Nature Switzerland AG 2019. 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.

Citation

Jafari, R., Razvarz, S., Gegov, A., & Paul, S. (2019). Fuzzy modeling for uncertain nonlinear systems using fuzzy equations and Z-numbers. https://doi.org/10.1007/978-3-319-97982-3_8

• Acceptance Date: Mar 1, 2018
• Online Publication Date: Aug 11, 2018
• Publication Date: 2019
• Deposit Date: Mar 9, 2020
• Publisher: Springer Verlag
• Volume: 840
• Pages: 96-107
• ISBN: 9783319979816
• DOI: https://doi.org/10.1007/978-3-319-97982-3_8
• Public URL: https://uwe-repository.worktribe.com/output/5628834