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Title: Optimal design of adaptive type-2 neuro-fuzzy systems: A review
Authors: Hassan, Saima
Khanesar, Mojtaba Ahmadieh
Kayacan, Erdal
Jaafar, Jafreezal
Khosravi, Abbas
Keywords: Interval type-2 fuzzy logic systems
Optimal learning algorithm
Issue Date: 2016
Source: Hassan, S., Khanesar, M. A., Kayacan, E., Jaafar, J., & Khosravi, A. (2016). Optimal design of adaptive type-2 neuro-fuzzy systems: A review. Applied Soft Computing, 44, 134-143.
Series/Report no.: Applied Soft Computing
Abstract: 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.
ISSN: 1568-4946
DOI: 10.1016/j.asoc.2016.03.023
Schools: School of Mechanical and Aerospace Engineering 
Rights: © 2016 Elsevier. This is the author created version of a work that has been peer reviewed and accepted for publication by Applied Soft Computing, Elsevier. It incorporates referee’s comments but changes resulting from the publishing process, such as copyediting, structural formatting, may not be reflected in this document. The published version is available at: [].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Journal Articles

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