International Journal of Computational Intelligence Systems

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Volume 10, Issue 1, 2017, Pages 734 - 744

A Piecewise Type-2 Fuzzy Regression Model

Authors
Narges Shafaei Bajestani1, Narges.shafaei@gmail.com, Ali Vahidian Kamyad2, *, a.vahidian.kamyad@gmail.com, Assef Zare3, assefzare@gmail.com
1Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
2Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
3Department of Electrical Engineering, Gonabad Branch, Islamic Azad University, Gonabad, Iran
* Corresponding author, Address: Department of Electrical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran. Email:a.vahidian.kamyad@gmail.com
Corresponding Author
Ali Vahidian Kamyada.vahidian.kamyad@gmail.com
Received 22 February 2016, Accepted 7 February 2017, Available Online 22 February 2017.
DOI
10.2991/ijcis.2017.10.1.49How to use a DOI?
Keywords
Interval type-2 fuzzy regression; Quadratic programming; type-2 fuzzy piecewise regression
Abstract

The type-2 fuzzy logic system permits us to model uncertainties existing in membership functions. Accordingly, this study aims to propose a linear and a piecewise framework for an interval type-2 fuzzy regression model based on the existing possibilistic models. In this model, vagueness is minimized, under the circumstances where the hcut of observed value is included in predicted value. In this model both primary and secondary membership function of predicted value fit the observed value. Developing the proposed model to piecewise model makes it helpful in dealing with the fluctuating data. This model, without the additional complexities, demonstrates its ability compared to previous type-2 fuzzy models.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

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<Previous Article In Issue
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Journal
International Journal of Computational Intelligence Systems
Volume-Issue
10 - 1
Pages
734 - 744
Publication Date
2017/02/22
ISSN (Online)
1875-6883
ISSN (Print)
1875-6891
DOI
10.2991/ijcis.2017.10.1.49How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article under the CC BY-NC license (http://creativecommons.org/licences/by-nc/4.0/).

Cite this article

risenwbib
TY  - JOUR
AU  - Narges Shafaei Bajestani
AU  - Ali Vahidian Kamyad
AU  - Assef Zare
PY  - 2017
DA  - 2017/02/22
TI  - A Piecewise Type-2 Fuzzy Regression Model
JO  - International Journal of Computational Intelligence Systems
SP  - 734
EP  - 744
VL  - 10
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2017.10.1.49
DO  - 10.2991/ijcis.2017.10.1.49
ID  - Bajestani2017
ER  -