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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.

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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, AbbasORCID iD for 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
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