Comparative analysis of three approaches of antecedent part generation for an IT2 TSK FLS

Since extreme learning machine is a non-iterative estimation procedure, it is faster than gradient-based algorithms which are iterative. Moreover, the extreme learning machine does not have any design parameters such as learning rate, covariance matrix, etc. The rigorous proof of universal approxima...

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Main Authors: Hassan, S., Khanesar, M.A., Jaafar, J., Khosravi, A.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.19641 /
Published: Elsevier Ltd 2017
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85007270691&doi=10.1016%2fj.asoc.2016.11.015&partnerID=40&md5=c93a4527081bb156773e2caa36a7eb08
http://eprints.utp.edu.my/19641/
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spelling utp-eprints.196412018-04-20T07:20:20Z Comparative analysis of three approaches of antecedent part generation for an IT2 TSK FLS Hassan, S. Khanesar, M.A. Jaafar, J. Khosravi, A. Since extreme learning machine is a non-iterative estimation procedure, it is faster than gradient-based algorithms which are iterative. Moreover, the extreme learning machine does not have any design parameters such as learning rate, covariance matrix, etc. The rigorous proof of universal approximation of extreme learning machine with much milder conditions makes it a preferable choice in many different approaches. Although this algorithm is optimal for the parameters which appear linearly in the consequent part of interval type-2 fuzzy logic systems, it is not optimal for the parameters of the antecedent part as it uses random parameters. In this paper, heuristic optimization approaches such as genetic algorithm and artificial bee colony are used to optimize the parameters of the antecedent part of interval type-2 fuzzy logic systems. As these methods are global optimizers, there is less possibility that they will fall in a local minima and are suitable for the selection of the parameters of the antecedent part. A comparative analysis of the optimal parameters with the randomly and manually generated parameters is presented here using noise-free and noisy Mackey-Glass time series data sets and a real world data set. Simulation results support this idea over randomly and manually generated parameters. © 2016 Elsevier B.V. Elsevier Ltd 2017 Article PeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85007270691&doi=10.1016%2fj.asoc.2016.11.015&partnerID=40&md5=c93a4527081bb156773e2caa36a7eb08 Hassan, S. and Khanesar, M.A. and Jaafar, J. and Khosravi, A. (2017) Comparative analysis of three approaches of antecedent part generation for an IT2 TSK FLS. Applied Soft Computing Journal, 51 . pp. 130-144. http://eprints.utp.edu.my/19641/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description Since extreme learning machine is a non-iterative estimation procedure, it is faster than gradient-based algorithms which are iterative. Moreover, the extreme learning machine does not have any design parameters such as learning rate, covariance matrix, etc. The rigorous proof of universal approximation of extreme learning machine with much milder conditions makes it a preferable choice in many different approaches. Although this algorithm is optimal for the parameters which appear linearly in the consequent part of interval type-2 fuzzy logic systems, it is not optimal for the parameters of the antecedent part as it uses random parameters. In this paper, heuristic optimization approaches such as genetic algorithm and artificial bee colony are used to optimize the parameters of the antecedent part of interval type-2 fuzzy logic systems. As these methods are global optimizers, there is less possibility that they will fall in a local minima and are suitable for the selection of the parameters of the antecedent part. A comparative analysis of the optimal parameters with the randomly and manually generated parameters is presented here using noise-free and noisy Mackey-Glass time series data sets and a real world data set. Simulation results support this idea over randomly and manually generated parameters. © 2016 Elsevier B.V.
format Article
author Hassan, S.
Khanesar, M.A.
Jaafar, J.
Khosravi, A.
spellingShingle Hassan, S.
Khanesar, M.A.
Jaafar, J.
Khosravi, A.
Comparative analysis of three approaches of antecedent part generation for an IT2 TSK FLS
author_sort Hassan, S.
title Comparative analysis of three approaches of antecedent part generation for an IT2 TSK FLS
title_short Comparative analysis of three approaches of antecedent part generation for an IT2 TSK FLS
title_full Comparative analysis of three approaches of antecedent part generation for an IT2 TSK FLS
title_fullStr Comparative analysis of three approaches of antecedent part generation for an IT2 TSK FLS
title_full_unstemmed Comparative analysis of three approaches of antecedent part generation for an IT2 TSK FLS
title_sort comparative analysis of three approaches of antecedent part generation for an it2 tsk fls
publisher Elsevier Ltd
publishDate 2017
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85007270691&doi=10.1016%2fj.asoc.2016.11.015&partnerID=40&md5=c93a4527081bb156773e2caa36a7eb08
http://eprints.utp.edu.my/19641/
_version_ 1741196239481864192
score 11.62408

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