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Artificial bee colony optimization of interval type-2 fuzzy extreme learning system for chaotic data

The major challenge in the design of Interval type-2 fuzzy logic system (IT2FLS) is to determine the optimal parameters for their antecedent and consequent parts. This paper propose a novel hybrid learning algorithm for the design of IT2FLS. The proposed hybrid learning algorithm utilizes the combin...

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Main Authors: Hassan, S., Jaafar, J., Khanesar, M.A., Khosravi, A.
Format: Conference or Workshop Item
Published: Institute of Electrical and Electronics Engineers Inc. 2016
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010426205&doi=10.1109%2fICCOINS.2016.7783237&partnerID=40&md5=4b6f671147a8d732e6a7748feb7a7432
http://eprints.utp.edu.my/30486/
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spelling my.utp.eprints.304862022-03-25T06:55:43Z Artificial bee colony optimization of interval type-2 fuzzy extreme learning system for chaotic data Hassan, S. Jaafar, J. Khanesar, M.A. Khosravi, A. The major challenge in the design of Interval type-2 fuzzy logic system (IT2FLS) is to determine the optimal parameters for their antecedent and consequent parts. This paper propose a novel hybrid learning algorithm for the design of IT2FLS. The proposed hybrid learning algorithm utilizes the combination of extreme learning machine (ELM) and artificial bee colony optimization (ABC) to tune the parameters of the consequent and antecedent parts of the IT2FLS, respectively. The effective forecasting performance of the proposed hybrid learning algorithm is analyzed by modeling a chaotic data set. It is found that the forecasted errors gradually decrease with decrease in the level of noise in data and vise versa. © 2016 IEEE. Institute of Electrical and Electronics Engineers Inc. 2016 Conference or Workshop Item NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010426205&doi=10.1109%2fICCOINS.2016.7783237&partnerID=40&md5=4b6f671147a8d732e6a7748feb7a7432 Hassan, S. and Jaafar, J. and Khanesar, M.A. and Khosravi, A. (2016) Artificial bee colony optimization of interval type-2 fuzzy extreme learning system for chaotic data. In: UNSPECIFIED. http://eprints.utp.edu.my/30486/
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 The major challenge in the design of Interval type-2 fuzzy logic system (IT2FLS) is to determine the optimal parameters for their antecedent and consequent parts. This paper propose a novel hybrid learning algorithm for the design of IT2FLS. The proposed hybrid learning algorithm utilizes the combination of extreme learning machine (ELM) and artificial bee colony optimization (ABC) to tune the parameters of the consequent and antecedent parts of the IT2FLS, respectively. The effective forecasting performance of the proposed hybrid learning algorithm is analyzed by modeling a chaotic data set. It is found that the forecasted errors gradually decrease with decrease in the level of noise in data and vise versa. © 2016 IEEE.
format Conference or Workshop Item
author Hassan, S.
Jaafar, J.
Khanesar, M.A.
Khosravi, A.
spellingShingle Hassan, S.
Jaafar, J.
Khanesar, M.A.
Khosravi, A.
Artificial bee colony optimization of interval type-2 fuzzy extreme learning system for chaotic data
author_facet Hassan, S.
Jaafar, J.
Khanesar, M.A.
Khosravi, A.
author_sort Hassan, S.
title Artificial bee colony optimization of interval type-2 fuzzy extreme learning system for chaotic data
title_short Artificial bee colony optimization of interval type-2 fuzzy extreme learning system for chaotic data
title_full Artificial bee colony optimization of interval type-2 fuzzy extreme learning system for chaotic data
title_fullStr Artificial bee colony optimization of interval type-2 fuzzy extreme learning system for chaotic data
title_full_unstemmed Artificial bee colony optimization of interval type-2 fuzzy extreme learning system for chaotic data
title_sort artificial bee colony optimization of interval type-2 fuzzy extreme learning system for chaotic data
publisher Institute of Electrical and Electronics Engineers Inc.
publishDate 2016
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85010426205&doi=10.1109%2fICCOINS.2016.7783237&partnerID=40&md5=4b6f671147a8d732e6a7748feb7a7432
http://eprints.utp.edu.my/30486/
_version_ 1738657114394460160
score 13.211869

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