Jafari, R orcid.org/0000-0001-7298-2363, Razvarz, S and Gegov, A (Cover date: February 2020) A novel technique for solving fully fuzzy nonlinear systems based on neural networks. Vietnam Journal of Computer Science, 07 (01). pp. 93-107. ISSN 2196-8888
Abstract
Predicting the solutions of complex systems is a crucial challenge. Complexity exists because of the uncertainty as well as nonlinearity. The nonlinearity in complex systems makes uncertainty irreducible in several cases. In this paper, two new approaches based on neural networks are proposed in order to find the estimated solutions of the fully fuzzy nonlinear system (FFNS). For obtaining the estimated solutions, a gradient descent algorithm is proposed in order to train the proposed networks. An example is proposed in order to show the efficiency of the considered approaches.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © The Author(s). This is an Open Access article published by World Scientific Publishing Company. It is distributed under the terms of the Creative Commons Attribution 4.0 (CC BY) License which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
Keywords: | Estimated solutions; complex system; fully fuzzy nonlinear system; neural network |
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: | 27 Jan 2020 09:59 |
Last Modified: | 29 Dec 2023 15:38 |
Published Version: | https://www.worldscientific.com/doi/10.1142/S21968... |
Status: | Published |
Publisher: | World Scientific Publishing |
Identification Number: | 10.1142/S2196888820500050 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:156070 |
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