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Optimal design of adaptive type-2 neuro-fuzzy systems: a review

Hassan, Saima, Khanesar, Mojtaba Ahmadieh, Kayacan, Erdal, Jaafar, Jafreezal and Khosravi, Abbas 2016, Optimal design of adaptive type-2 neuro-fuzzy systems: a review, Applied soft computing, vol. 44, pp. 134-143, doi: 10.1016/j.asoc.2016.03.023.

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Title Optimal design of adaptive type-2 neuro-fuzzy systems: a review
Author(s) Hassan, Saima
Khanesar, Mojtaba Ahmadieh
Kayacan, Erdal
Jaafar, Jafreezal
Khosravi, AbbasORCID iD for Khosravi, Abbas orcid.org/0000-0001-6927-0744
Journal name Applied soft computing
Volume number 44
Start page 134
End page 143
Total pages 10
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2016-07
ISSN 1568-4946
1872-9681
Keyword(s) interval type-2 fuzzy logic systems
optimal learning algorithm
hybrid learning
parameter update rules
genetic algorithms
particle swarm optimization
Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science, Interdisciplinary Applications
Computer Science
UNIVERSAL APPROXIMATORS
LEARNING ALGORITHM
LOGIC CONTROLLERS
OPTIMIZATION
PARTITION
NETWORKS
HYBRID
CLASSIFICATION
IDENTIFICATION
Summary Type-2 fuzzy logic systems have extensively been applied to various engineering problems, e.g. identification, prediction, control, pattern recognition, etc. in the past two decades, and the results were promising especially in the presence of significant uncertainties in the system. In the design of type-2 fuzzy logic systems, the early applications were realized in a way that both the antecedent and consequent parameters were chosen by the designer with perhaps some inputs from some experts. Since 2000s, a huge number of papers have been published which are based on the adaptation of the parameters of type-2 fuzzy logic systems using the training data either online or offline. Consequently, the major challenge was to design these systems in an optimal way in terms of their optimal structure and their corresponding optimal parameter update rules. In this review, the state of the art of the three major classes of optimization methods are investigated: derivative-based (computational approaches), derivative-free (heuristic methods) and hybrid methods which are the fusion of both the derivative-free and derivative-based methods.
Language eng
DOI 10.1016/j.asoc.2016.03.023
Field of Research 0102 Applied Mathematics
0801 Artificial Intelligence And Image Processing
0806 Information Systems
Socio Economic Objective 0 Not Applicable
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2016, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30092616

Document type: Journal Article
Collection: Centre for Intelligent Systems Research
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