Jafarian, A, Jafari, R orcid.org/0000-0001-7298-2363, Al Qurashi, MM et al. (1 more author) (2016) A novel computational approach to approximate fuzzy interpolation polynomials. SpringerPlus, 5. 1428. ISSN 2193-1801
Abstract
This paper build a structure of fuzzy neural network, which is well sufficient to gain a fuzzy interpolation polynomial of the form yp=anxnp+⋯+a1xp+a0 where aj is crisp number (for j=0,…,n), which interpolates the fuzzy data (xj,yj)(forj=0,…,n). Thus, a gradient descent algorithm is constructed to train the neural network in such a way that the unknown coefficients of fuzzy polynomial are estimated by the neural network. The numeral experimentations portray that the present interpolation methodology is reliable and efficient.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016, The Author(s). This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. |
Keywords: | Fuzzy neural networks; Fuzzy interpolation polynomial; Cost function; Learning algorithm |
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: | 28 Jan 2020 14:17 |
Last Modified: | 28 Jan 2020 14:17 |
Status: | Published |
Publisher: | Springer |
Identification Number: | 10.1186/s40064-016-3077-5 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:156081 |