Type-2 fuzzy elliptic membership functions for modeling uncertainty

Whereas type-1 and type-2 membership functions (MFs) are the core of any fuzzy logic system, there are no performance criteria available to evaluate the goodness or correctness of the fuzzy MFs. In this paper, we make extensive analysis in terms of the capability of type-2 elliptic fuzzy MFs in mode...

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Main Authors: Kayacan, Erdal, Sarabakha, Andriy, Coupland, Simon, John, Robert, Khanesar, Mojtaba Ahmadieh
Other Authors: School of Mechanical and Aerospace Engineering
Format: Article
Language:English
Published: 2020
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Online Access:https://hdl.handle.net/10356/139696
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1396962020-05-21T03:22:39Z Type-2 fuzzy elliptic membership functions for modeling uncertainty Kayacan, Erdal Sarabakha, Andriy Coupland, Simon John, Robert Khanesar, Mojtaba Ahmadieh School of Mechanical and Aerospace Engineering Engineering::Mechanical engineering Elliptic Membership Function Type-2 Fuzzy Logic Theory Whereas type-1 and type-2 membership functions (MFs) are the core of any fuzzy logic system, there are no performance criteria available to evaluate the goodness or correctness of the fuzzy MFs. In this paper, we make extensive analysis in terms of the capability of type-2 elliptic fuzzy MFs in modeling uncertainty. Having decoupled parameters for its support and width, elliptic MFs are unique amongst existing type-2 fuzzy MFs. In this investigation, the uncertainty distribution along the elliptic MF support is studied, and a detailed analysis is given to compare and contrast its performance with existing type-2 fuzzy MFs. Furthermore, fuzzy arithmetic operations are also investigated, and our finding is that the elliptic MF has similar features to the Gaussian and triangular MFs in addition and multiplication operations. Moreover, we have tested the prediction capability of elliptic MFs using interval type-2 fuzzy logic systems on oil price prediction problem for a data set from 2nd Jan 1985 till 25th April 2016. Throughout the simulation studies, an extreme learning machine is used to train the interval type-2 fuzzy logic system. The prediction results show that, in addition to their various advantages mentioned above, elliptic MFs have comparable prediction results when compared to Gaussian and triangular MFs. Finally, in order to test the performance of fuzzy logic controller with elliptic interval type-2 MFs, extensive real-time experiments are conducted for the 3D trajectory tracking problem of a quadrotor. We believe that the results of this study will open the doors to elliptic MFs’ wider use of real-world identification and control applications as the proposed MF is easy to interpret in addition to its unique features. 2020-05-21T03:22:39Z 2020-05-21T03:22:39Z 2018 Journal Article Kayacan, E., Sarabakha, A., Coupland, S., John, R., & Khanesar, M. A. (2018). Type-2 fuzzy elliptic membership functions for modeling uncertainty. Engineering Applications of Artificial Intelligence, 70, 170-183. doi:10.1016/j.engappai.2018.02.004 0952-1976 https://hdl.handle.net/10356/139696 10.1016/j.engappai.2018.02.004 2-s2.0-85042282189 70 170 183 en Engineering Applications of Artificial Intelligence © 2018 Elsevier Ltd. All rights reserved.
institution Nanyang Technological University
building NTU Library
country Singapore
collection DR-NTU
language English
topic Engineering::Mechanical engineering
Elliptic Membership Function
Type-2 Fuzzy Logic Theory
spellingShingle Engineering::Mechanical engineering
Elliptic Membership Function
Type-2 Fuzzy Logic Theory
Kayacan, Erdal
Sarabakha, Andriy
Coupland, Simon
John, Robert
Khanesar, Mojtaba Ahmadieh
Type-2 fuzzy elliptic membership functions for modeling uncertainty
description Whereas type-1 and type-2 membership functions (MFs) are the core of any fuzzy logic system, there are no performance criteria available to evaluate the goodness or correctness of the fuzzy MFs. In this paper, we make extensive analysis in terms of the capability of type-2 elliptic fuzzy MFs in modeling uncertainty. Having decoupled parameters for its support and width, elliptic MFs are unique amongst existing type-2 fuzzy MFs. In this investigation, the uncertainty distribution along the elliptic MF support is studied, and a detailed analysis is given to compare and contrast its performance with existing type-2 fuzzy MFs. Furthermore, fuzzy arithmetic operations are also investigated, and our finding is that the elliptic MF has similar features to the Gaussian and triangular MFs in addition and multiplication operations. Moreover, we have tested the prediction capability of elliptic MFs using interval type-2 fuzzy logic systems on oil price prediction problem for a data set from 2nd Jan 1985 till 25th April 2016. Throughout the simulation studies, an extreme learning machine is used to train the interval type-2 fuzzy logic system. The prediction results show that, in addition to their various advantages mentioned above, elliptic MFs have comparable prediction results when compared to Gaussian and triangular MFs. Finally, in order to test the performance of fuzzy logic controller with elliptic interval type-2 MFs, extensive real-time experiments are conducted for the 3D trajectory tracking problem of a quadrotor. We believe that the results of this study will open the doors to elliptic MFs’ wider use of real-world identification and control applications as the proposed MF is easy to interpret in addition to its unique features.
author2 School of Mechanical and Aerospace Engineering
author_facet School of Mechanical and Aerospace Engineering
Kayacan, Erdal
Sarabakha, Andriy
Coupland, Simon
John, Robert
Khanesar, Mojtaba Ahmadieh
format Article
author Kayacan, Erdal
Sarabakha, Andriy
Coupland, Simon
John, Robert
Khanesar, Mojtaba Ahmadieh
author_sort Kayacan, Erdal
title Type-2 fuzzy elliptic membership functions for modeling uncertainty
title_short Type-2 fuzzy elliptic membership functions for modeling uncertainty
title_full Type-2 fuzzy elliptic membership functions for modeling uncertainty
title_fullStr Type-2 fuzzy elliptic membership functions for modeling uncertainty
title_full_unstemmed Type-2 fuzzy elliptic membership functions for modeling uncertainty
title_sort type-2 fuzzy elliptic membership functions for modeling uncertainty
publishDate 2020
url https://hdl.handle.net/10356/139696
_version_ 1681056505280856064