Solution of dual fuzzy equations using a new iterative method

Sina Razvarz, Raheleh Jafari, Ole-Christoffer Granmo, Alexander Gegov

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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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 solu-tion 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 re-lation 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 com-pared 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 illus-trate the concepts, which are discussed in this paper.
Original languageEnglish
Title of host publicationProceedings of the 10th Asian Conference on Intelligent Information and Database Systems
PublisherSpringer
Pages245-255
Number of pages11
ISBN (Print)978-3-319-75419-2
DOIs
Publication statusEarly online - 14 Feb 2018
Event10th Asian Conference on Intelligent Information and Database Systems - Dong Hoi City, Vietnam
Duration: 19 Mar 201821 Mar 2018

Publication series

NameLecture Notes in Artificial Intelligence (subseries of LNCS)
ISSN (Print)0302-9743

Conference

Conference10th Asian Conference on Intelligent Information and Database Systems
Abbreviated titleCIIDS 2018
Country/TerritoryVietnam
CityDong Hoi City
Period19/03/1821/03/18

Keywords

  • Fully Fuzzy Dual Polynomials
  • Fuzzy Neural Network
  • Approximate Solution

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