relation: https://khub.utp.edu.my/scholars/8888/
title: Comparative analysis of three approaches of antecedent part generation for an IT2 TSK FLS
creator: Hassan, S.
creator: Khanesar, M.A.
creator: Jaafar, J.
creator: Khosravi, A.
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.
publisher: Elsevier Ltd
date: 2017
type: Article
type: PeerReviewed
identifier:   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.  ISSN 15684946     
relation: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85007270691&doi=10.1016%2fj.asoc.2016.11.015&partnerID=40&md5=c93a4527081bb156773e2caa36a7eb08
relation: 10.1016/j.asoc.2016.11.015
identifier: 10.1016/j.asoc.2016.11.015