Jafarian, A, Jafari, R orcid.org/0000-0001-7298-2363, Golmankhaneh, AK et al. (1 more author) (2015) Solving fully fuzzy polynomials using feed-back neural networks. International Journal of Computer Mathematics, 92 (4). pp. 742-755. ISSN 0020-7160
Abstract
Recently, there has been a considerable amount of interest and practice in solving many problems of several applied fields by fuzzy polynomials. In this paper, we have designed an artificial fuzzified feed-back neural network. With this design, we are able to find a solution of fully fuzzy polynomial with degree n. This neural network can get a fuzzy vector as an input, and calculates its corresponding fuzzy output. It is clear that the input–output relation for each unit of fuzzy neural network is defined by the extension principle of Zadeh. In this work, a cost function is also defined for the level sets of fuzzy output and fuzzy target. Next a learning algorithm based on the gradient descent method will be defined that can adjust the fuzzy connection weights. Finally, our approach is illustrated by computer simulations on numerical examples. It is worthwhile to mention that application of this method in fluid mechanics has been shown by an example.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2014 Taylor & Francis. This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Computer Mathematics on 22 May 2014, available online: http://www.tandfonline.com/10.1080/00207160.2014.907404 |
Keywords: | fully fuzzy polynomials, fuzzy feed-forward neural networks, fuzzy feed-back neural networks, learning algorithm, cost function |
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: | 24 Mar 2020 11:40 |
Last Modified: | 24 Mar 2020 11:49 |
Status: | Published |
Publisher: | Taylor & Francis |
Identification Number: | 10.1080/00207160.2014.907404 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:156087 |