Heuristic design of fuzzy inference systems : a review of three decades of research

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dc.contributor.author Ojha, Varun
dc.contributor.author Abraham, Ajith
dc.contributor.author Snasel, Vaclav
dc.date.accessioned 2019-09-16T09:56:47Z
dc.date.issued 2019-10
dc.description.abstract This paper provides an in-depth review of the optimal design of type-1 and type-2 fuzzy inference systems (FIS) using five well known computational frameworks: genetic-fuzzy systems (GFS), neuro-fuzzy systems (NFS), hierarchical fuzzy systems (HFS), evolving fuzzy systems (EFS), and multi-objective fuzzy systems (MFS), which is in view that some of them are linked to each other. The heuristic design of GFS uses evolutionary algorithms for optimizing both Mamdani-type and Takagi–Sugeno–Kang-type fuzzy systems. Whereas, the NFS combines the FIS with neural network learning systems to improve the approximation ability. An HFS combines two or more low-dimensional fuzzy logic units in a hierarchical design to overcome the curse of dimensionality. An EFS solves the data streaming issues by evolving the system incrementally, and an MFS solves the multi-objective trade-offs like the simultaneous maximization of both interpretability and accuracy. This paper ofers a synthesis of these dimensions and explores their potentials, challenges, and opportunities in FIS research. This review also examines the complex relations among these dimensions and the possibilities of combining one or more computational frameworks adding another dimension: deep fuzzy systems. en_ZA
dc.description.department Computer Science en_ZA
dc.description.embargo 2020-10-01
dc.description.librarian hj2019 en_ZA
dc.description.uri http://www.elsevier.com/locate/engappai en_ZA
dc.identifier.citation Ojha, V., Abraham, A. & Snášel, V. 2019, 'Heuristic design of fuzzy inference systems: a review of three decades of research', Engineering Applications of Artificial Intelligence, vol. 85, pp. 845-864. en_ZA
dc.identifier.issn 0952-1976 (print)
dc.identifier.issn 1873-6769 (online)
dc.identifier.other 10.1016/j.engappai.2019.08.010
dc.identifier.uri http://hdl.handle.net/2263/71358
dc.language.iso en en_ZA
dc.publisher Elsevier en_ZA
dc.rights © 2019 Elsevier Ltd. All rights reserved. Notice : this is the author’s version of a work that was accepted for publication in Engineering Applications of Artificial Intelligence. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. A definitive version was subsequently published in Engineering Applications of Artificial Intelligence, vol. 85, pp. 845-864, 2019. doi : 10.1016/j.engappai.2019.08.010. en_ZA
dc.subject Fuzzy inference systems (FIS) en_ZA
dc.subject Genetic-fuzzy systems (GFS) en_ZA
dc.subject Neuro-fuzzy systems (NFS) en_ZA
dc.subject Hierarchical fuzzy systems (HFS) en_ZA
dc.subject Evolving fuzzy systems (EFS) en_ZA
dc.subject Multi-objective fuzzy systems (MFS) en_ZA
dc.subject Evolutionary algorithms en_ZA
dc.subject Deep fuzzy system en_ZA
dc.title Heuristic design of fuzzy inference systems : a review of three decades of research en_ZA
dc.type Postprint Article en_ZA


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