Optimal parameters of an ELM-based interval type 2 fuzzy logic system: a hybrid learning algorithm Hassan, Saima, Khanesar, Mojtaba Ahmadieh, Jaafar, Jafreezal and Khosravi, Abbas 2016, Optimal parameters of an ELM-based interval type 2 fuzzy logic system: a hybrid learning algorithm, Neural computing and applications, pp. 1-14, doi: 10.1007/s00521-016-2503-5. Attached Files Name Description MIMEType Size Downloads Title Optimal parameters of an ELM-based interval type 2 fuzzy logic system: a hybrid learning algorithm Author(s) Hassan, Saima Khanesar, Mojtaba Ahmadieh Jaafar, Jafreezal Khosravi, Abbas orcid.org/0000-0001-6927-0744 Journal name Neural computing and applications Start page 1 End page 14 Total pages 14 Publisher Springer Verlag Place of publication Berlin, Germany Publication date 2016-08-01 ISSN 0941-0643 Keyword(s) Interval type 2 fuzzy logic systems Optimal parameters Hybrid learning Artificial bee colony Extreme learning machine Summary An optimized design of a fuzzy logic system can be regarded as setting of different parameters of the system automatically. For a single parameter, there may exist multiple feasible values. Consequently, with the increase in number of parameters, the complexity of a system increases. Type 2 fuzzy logic system has more parameters than the type 1 fuzzy logic system and is therefore much more complex than its counterpart. This paper proposes optimal parameters for an extreme learning machine-based interval type 2 fuzzy logic system to learn its best configuration. Extreme learning machine (ELM) is utilized to tune the consequent parameters of the interval type 2 fuzzy logic system (IT2FLS). A disadvantage of ELM is the random generation of its hidden neuron that causes additional uncertainty, in both approximation and learning. In order to overcome this limitation in an ELM-based IT2FLS, artificial bee colony optimization algorithm is utilized to obtain its antecedent parts parameters. The simulation results verified better performance of the proposed IT2FLS over other models with the benchmark data sets. Notes In press Language eng DOI 10.1007/s00521-016-2503-5 Field of Research 099999 Engineering not elsewhere classified 0801 Artificial Intelligence And Image Processing 1702 Cognitive Science Socio Economic Objective 0 Not Applicable HERDC Research category C1 Refereed article in a scholarly journal Copyright notice ©2016, The Natural Computing Applications Forum Persistent URL http://hdl.handle.net/10536/DRO/DU:30087391 Document type: Journal Article Collection: Centre for Intelligent Systems Research Related Links Link Description Connect to published version Go to link with your DU access privileges   Connect to Elements publication management system Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Thu, 13 Oct 2016, 13:36:18 EST Fri, 14 Oct 2016, 04:07:53 EST Fri, 14 Oct 2016, 07:21:03 EST Fri, 14 Oct 2016, 07:27:24 EST Mon, 03 Apr 2017, 09:29:34 EST Tue, 04 Apr 2017, 06:02:41 EST Fri, 21 Apr 2017, 13:46:40 EST Mon, 05 Jun 2017, 14:57:14 EST Mon, 05 Jun 2017, 15:01:04 EST Thu, 15 Jun 2017, 08:14:11 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 11 Abstract Views, 3 File Downloads  -  Detailed Statistics Created: Mon, 03 Apr 2017, 09:29:34 EST

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