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Modeling of Uncertain Nonlinear System With Z-Numbers
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Author(s): Raheleh Jafari (School of Design, University of Leeds, Leeds, UK), Sina Razvarz (Department of Automatic Control, Centre for Research and Advanced Studies of the National Polytechnic Institute (CINVESTAV-IPN), Mexico City, Mexico), Alexander Gegov (School of Computing, University of Portsmouth, UK)and Satyam Paul (Department of Engineering Design and Mathematics, University of the West of England, Bristol, UK)
Copyright: 2021
Pages: 25
Source title:
Encyclopedia of Information Science and Technology, Fifth Edition
Source Author(s)/Editor(s): Mehdi Khosrow-Pour D.B.A. (Information Resources Management Association, USA)
DOI: 10.4018/978-1-7998-3479-3.ch022
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Abstract
In order to model the fuzzy nonlinear systems, fuzzy equations with Z-number coefficients are used in this chapter. The modeling of fuzzy nonlinear systems is to obtain the Z-number coefficients of fuzzy equations. In this work, the neural network approach is used for finding the coefficients of fuzzy equations. Some examples with applications in mechanics are given. The simulation results demonstrate that the proposed neural network is effective for obtaining the Z-number coefficients of fuzzy equations.
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