Razvarz, S, Jafari, R orcid.org/0000-0001-7298-2363, Granmo, O-C et al. (1 more author) (2018) Solution of Dual Fuzzy Equations Using a New Iterative Method. In: Nguyen, N, Hoang, D, Hong, TP, Pham, H and Trawiński, B, (eds.) ACIIDS 2018: Intelligent Information and Database Systems. ACIIDS 2018, 19-21 Mar 2018, Dong Hoi City, Vietnam. Springer International Publishing , pp. 245-255. ISBN 9783319754192
Abstract
In this paper, a new hybrid scheme based on learning algorithm of fuzzy neural network (FNN) is offered in order to extract the approximate solution of fully fuzzy dual polynomials (FFDPs). Our FNN in this paper is a five-layer feed-back FNN with the identity activation function. The input-output relation of each unit is defined by the extension principle of Zadeh. The output from this neural network, which is also a fuzzy number, is numerically compared with the target output. The comparison of the feed-back FNN method with the feed-forward FNN method shows that the less error is observed in the feed-back FNN method. An example based on applications are given to illustrate the concepts, which are discussed in this paper.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © Springer International Publishing AG, part of Springer Nature 2018. This is a post-peer-review, pre-copyedit version of a conference paper published in ACIIDS 2018: Intelligent Information and Database Systems. The final authenticated version is available online at: http://doi.org/10.1007/978-3-319-75420-8_23 |
Keywords: | Fully fuzzy dual polynomials; Fuzzy neural network; Approximate solution |
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: | 01 Jun 2020 18:27 |
Last Modified: | 01 Jun 2020 20:52 |
Status: | Published |
Publisher: | Springer International Publishing |
Identification Number: | 10.1007/978-3-319-75420-8_23 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:160701 |