Cover Image

Generating type 2 trapezoidal fuzzy membership function using genetic tuning

Fuzzy inference system (FIS) is a process of fuzzy logic reasoning to produce the output based on fuzzified inputs. The system starts with identifying input from data, applying the fuzziness to input using membership functions (MF), generating fuzzy rules for the fuzzy sets and obtaining the output....

Full description

Saved in:
Bibliographic Details
Main Authors: Khairuddin, S.H., Hasan, M.H., Akhir, E.A.P., Hashmani, M.A.
Format: Article
Published: 2022
Online Access:http://scholars.utp.edu.my/id/eprint/33986/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118557277&doi=10.32604%2fcmc.2022.020666&partnerID=40&md5=68ff0daf0ccd0c9201a09f8b25d868d3
Tags: Add Tag
No Tags, Be the first to tag this record!
id oai:scholars.utp.edu.my:33986
record_format eprints
spelling oai:scholars.utp.edu.my:339862022-12-20T04:01:34Z http://scholars.utp.edu.my/id/eprint/33986/ Generating type 2 trapezoidal fuzzy membership function using genetic tuning Khairuddin, S.H. Hasan, M.H. Akhir, E.A.P. Hashmani, M.A. Fuzzy inference system (FIS) is a process of fuzzy logic reasoning to produce the output based on fuzzified inputs. The system starts with identifying input from data, applying the fuzziness to input using membership functions (MF), generating fuzzy rules for the fuzzy sets and obtaining the output. There are several types of inputMFs which can be introduced in FIS, commonly chosen based on the type of real data, sensitivity of certain rule implied and computational limits. This paper focuses on the construction of interval type 2 (IT2) trapezoidal shape MF from fuzzy C Means (FCM) that is used for fuzzification process of mamdani FIS. In the process, upper MF (UMF) and lowerMF (LMF) of theMF need to be identified to get the range of the footprint of uncertainty (FOU). This paper proposes Genetic tuning process, which is a part of genetic algorithm (GA), to adjust parameters in order to improve the behavior of existing system, especially to enhance the accuracy of the system model. This novel process is a hybrid approach which produces Genetic Fuzzy System (GFS) that helps to enhance fuzzy classification problems and performance. The approach provides a new method for the construction and tuning process of the IT2MF, based on the FCM outcomes. The result is compared to Gaussian shape IT2 MF and trapezoid IT2 MF generated by the classicGAmethod. It is shown that the proposed approach is able to outperformthe mentioned benchmarked approaches. Thework implies a wider range of IT2MF types, constructed based on FCM outcomes, and an optimum generation of the FOU so that it can be implemented in practical applications such as prediction, analytics and rule-based solutions. © 2022 Tech Science Press. All rights reserved. 2022 Article NonPeerReviewed Khairuddin, S.H. and Hasan, M.H. and Akhir, E.A.P. and Hashmani, M.A. (2022) Generating type 2 trapezoidal fuzzy membership function using genetic tuning. Computers, Materials and Continua, 71 (1). pp. 717-734. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118557277&doi=10.32604%2fcmc.2022.020666&partnerID=40&md5=68ff0daf0ccd0c9201a09f8b25d868d3 10.32604/cmc.2022.020666 10.32604/cmc.2022.020666 10.32604/cmc.2022.020666
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description Fuzzy inference system (FIS) is a process of fuzzy logic reasoning to produce the output based on fuzzified inputs. The system starts with identifying input from data, applying the fuzziness to input using membership functions (MF), generating fuzzy rules for the fuzzy sets and obtaining the output. There are several types of inputMFs which can be introduced in FIS, commonly chosen based on the type of real data, sensitivity of certain rule implied and computational limits. This paper focuses on the construction of interval type 2 (IT2) trapezoidal shape MF from fuzzy C Means (FCM) that is used for fuzzification process of mamdani FIS. In the process, upper MF (UMF) and lowerMF (LMF) of theMF need to be identified to get the range of the footprint of uncertainty (FOU). This paper proposes Genetic tuning process, which is a part of genetic algorithm (GA), to adjust parameters in order to improve the behavior of existing system, especially to enhance the accuracy of the system model. This novel process is a hybrid approach which produces Genetic Fuzzy System (GFS) that helps to enhance fuzzy classification problems and performance. The approach provides a new method for the construction and tuning process of the IT2MF, based on the FCM outcomes. The result is compared to Gaussian shape IT2 MF and trapezoid IT2 MF generated by the classicGAmethod. It is shown that the proposed approach is able to outperformthe mentioned benchmarked approaches. Thework implies a wider range of IT2MF types, constructed based on FCM outcomes, and an optimum generation of the FOU so that it can be implemented in practical applications such as prediction, analytics and rule-based solutions. © 2022 Tech Science Press. All rights reserved.
format Article
author Khairuddin, S.H.
Hasan, M.H.
Akhir, E.A.P.
Hashmani, M.A.
spellingShingle Khairuddin, S.H.
Hasan, M.H.
Akhir, E.A.P.
Hashmani, M.A.
Generating type 2 trapezoidal fuzzy membership function using genetic tuning
author_facet Khairuddin, S.H.
Hasan, M.H.
Akhir, E.A.P.
Hashmani, M.A.
author_sort Khairuddin, S.H.
title Generating type 2 trapezoidal fuzzy membership function using genetic tuning
title_short Generating type 2 trapezoidal fuzzy membership function using genetic tuning
title_full Generating type 2 trapezoidal fuzzy membership function using genetic tuning
title_fullStr Generating type 2 trapezoidal fuzzy membership function using genetic tuning
title_full_unstemmed Generating type 2 trapezoidal fuzzy membership function using genetic tuning
title_sort generating type 2 trapezoidal fuzzy membership function using genetic tuning
publishDate 2022
url http://scholars.utp.edu.my/id/eprint/33986/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85118557277&doi=10.32604%2fcmc.2022.020666&partnerID=40&md5=68ff0daf0ccd0c9201a09f8b25d868d3
_version_ 1753790763936776192
score 13.223943

Similar Items