dc.contributor.authorHassan, Saima
dc.contributor.authorKhanesar, Mojtaba Ahmadieh
dc.contributor.authorKayacan, Erdal
dc.contributor.authorJaafar, Jafreezal
dc.contributor.authorKhosravi, Abbas
dc.date.accessioned2017-07-27T03:52:40Z
dc.date.available2017-07-27T03:52:40Z
dc.date.issued2016
dc.identifier.citationHassan, 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.en_US
dc.identifier.issn1568-4946en_US
dc.identifier.urihttp://hdl.handle.net/10220/43459
dc.description.abstractType-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.en_US
dc.format.extent35 p.en_US
dc.language.isoenen_US
dc.relation.ispartofseriesApplied Soft Computingen_US
dc.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: [http://dx.doi.org/10.1016/j.asoc.2016.03.023].en_US
dc.subjectInterval type-2 fuzzy logic systemsen_US
dc.subjectOptimal learning algorithmen_US
dc.titleOptimal design of adaptive type-2 neuro-fuzzy systems: A reviewen_US
dc.typeJournal Article
dc.contributor.schoolSchool of Mechanical and Aerospace Engineeringen_US
dc.identifier.doihttp://dx.doi.org/10.1016/j.asoc.2016.03.023
dc.description.versionAccepted versionen_US


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