Solution of dual fuzzy equations using a new iterative method

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

doi:10.1007/978-3-319-75420-8_23

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 proceeding › Conference contribution

170 Downloads (Pure)

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 language	English
Title of host publication	Proceedings of the 10th Asian Conference on Intelligent Information and Database Systems
Publisher	Springer
Pages	245-255
Number of pages	11
ISBN (Print)	978-3-319-75419-2
DOIs	https://doi.org/10.1007/978-3-319-75420-8_23
Publication status	Early online - 14 Feb 2018
Event	10th Asian Conference on Intelligent Information and Database Systems - Dong Hoi City, Vietnam Duration: 19 Mar 2018 → 21 Mar 2018

Publication series

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

Conference

Conference	10th Asian Conference on Intelligent Information and Database Systems
Abbreviated title	CIIDS 2018
Country/Territory	Vietnam
City	Dong Hoi City
Period	19/03/18 → 21/03/18

Keywords

Fully Fuzzy Dual Polynomials
Fuzzy Neural Network
Approximate Solution

Access to Document

10.1007/978-3-319-75420-8_23

LNCS_CHAPTER
The final authenticated version is available online at https://doi.org/10.1007/978-3-319-75420-8_23.
Accepted author manuscript (Post-print), 351 KB

https://aciids.pwr.edu.pl/2018/

Cite this

Razvarz, S., Jafari, R., Granmo, O.-C., & Gegov, A. (2018). Solution of dual fuzzy equations using a new iterative method. In Proceedings of the 10th Asian Conference on Intelligent Information and Database Systems (pp. 245-255). (Lecture Notes in Artificial Intelligence (subseries of LNCS)). Springer. Advance online publication. https://doi.org/10.1007/978-3-319-75420-8_23

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author = "Sina Razvarz and Raheleh Jafari and Ole-Christoffer Granmo and Alexander Gegov",

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Razvarz, S, Jafari, R, Granmo, O-C & Gegov, A 2018, Solution of dual fuzzy equations using a new iterative method. in Proceedings of the 10th Asian Conference on Intelligent Information and Database Systems. Lecture Notes in Artificial Intelligence (subseries of LNCS), Springer, pp. 245-255, 10th Asian Conference on Intelligent Information and Database Systems, Dong Hoi City, Vietnam, 19/03/18. https://doi.org/10.1007/978-3-319-75420-8_23

Solution of dual fuzzy equations using a new iterative method. / Razvarz, Sina ; Jafari, Raheleh ; Granmo, Ole-Christoffer et al.
Proceedings of the 10th Asian Conference on Intelligent Information and Database Systems. Springer, 2018. p. 245-255 (Lecture Notes in Artificial Intelligence (subseries of LNCS)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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N2 - 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.

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Solution of dual fuzzy equations using a new iterative method

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Keywords

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