Jafari, R orcid.org/0000-0001-7298-2363 and Yu, W (2017) Uncertain non near system control with Fuzzy Differential Equations and Z-numbers. In: 2017 IEEE International Conference on Industrial Technology (ICIT). 2017 IEEE International Conference on Industrial Technology (ICIT), 22-25 Mar 2017, Toronto, ON, Canada. IEEE , pp. 890-895. ISBN 9781509053209
Abstract
In this paper, the solutions of fuzzy differential equations (FDEs) are estimated by using two types of Bernstein neural networks. Here, the uncertainties are in the form of Z numbers. Firstly, we transform the FDE to four ordinary differential equations (ODEs) at par with Hukuhara differentiability. After that we develop neural models having the structure of ODEs. By using modified backpropagation technique for Z number variables, the training of neural networks are carried out. The results of the simulation illustrate that these innovative models, Bernstein neural networks, are efficient to approximate the solutions of FDEs which are on the basis of Z-numbers.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Keywords: | backpropagation , differential equations , fuzzy set theory , neurocontrollers , nonlinear control systems , uncertain systems |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Arts, Humanities and Cultures (Leeds) > School of Design (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 26 Oct 2020 16:31 |
Last Modified: | 26 Oct 2020 16:31 |
Status: | Published |
Publisher: | IEEE |
Identification Number: | 10.1109/ICIT.2017.7915477 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:167144 |