A Generalized Concept for Fuzzy Rule Interpolation
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Altmetric Citations
Baranyi, Peter; Gedeon, Tamas (Tom); Koczy, Lazlo
Description
The concept of fuzzy rule interpolation in sparse rule bases was introduced in 1993. It has become a widely researched topic in recent years because of its unique merits in the topic of fuzzy rule base complexity reduction. The first implemented technique of fuzzy rule interpolation was termed as α-cut distance based fuzzy rule base interpolation. Despite its advantageous properties in various approximation aspects and in complexity reduction, it was shown that it has some essential...[Show more]
dc.contributor.author | Baranyi, Peter | |
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dc.contributor.author | Gedeon, Tamas (Tom) | |
dc.contributor.author | Koczy, Lazlo | |
dc.date.accessioned | 2015-12-13T22:56:13Z | |
dc.identifier.issn | 1063-6706 | |
dc.identifier.uri | http://hdl.handle.net/1885/82728 | |
dc.description.abstract | The concept of fuzzy rule interpolation in sparse rule bases was introduced in 1993. It has become a widely researched topic in recent years because of its unique merits in the topic of fuzzy rule base complexity reduction. The first implemented technique of fuzzy rule interpolation was termed as α-cut distance based fuzzy rule base interpolation. Despite its advantageous properties in various approximation aspects and in complexity reduction, it was shown that it has some essential deficiencies, for instance, it does not always result in immediately interpretable fuzzy membership functions. This fact inspired researchers to develop various kinds of fuzzy rule interpolation techniques in order to alleviate these deficiencies. This paper is an attempt into this direction. It proposes an interpolation methodology, whose key idea is based on the interpolation of relations instead of interpolating α-cut distances, and which offers a way to derive a family of interpolation methods capable of eliminating some typical deficiencies of fuzzy rule interpolation techniques. The proposed concept of interpolating relations is elaborated here using fuzzy- and semantic-relations. This paper presents numerical examples, in comparison with former approaches, to show the effectiveness of the proposed interpolation methodology. | |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE Inc) | |
dc.source | IEEE Transactions on Fuzzy Systems | |
dc.subject | Keywords: Algorithms; Approximation theory; Interpolation; Mathematical models; Mathematical transformations; Vectors; Complexity reduction; Fuzzy rule interpolation; Single rule inference; Sparse fuzzy rule base; Fuzzy sets Fuzzy rule interpolation; Sparse fuzzy rule-base | |
dc.title | A Generalized Concept for Fuzzy Rule Interpolation | |
dc.type | Journal article | |
local.description.notes | Imported from ARIES | |
local.description.refereed | Yes | |
local.identifier.citationvolume | 12 | |
dc.date.issued | 2004 | |
local.identifier.absfor | 080108 - Neural, Evolutionary and Fuzzy Computation | |
local.identifier.ariespublication | MigratedxPub10938 | |
local.type.status | Published Version | |
local.contributor.affiliation | Baranyi, Peter, Budapest University of Technology and Economics | |
local.contributor.affiliation | Gedeon, Tamas (Tom), College of Engineering and Computer Science, ANU | |
local.contributor.affiliation | Koczy, Lazlo, Budapest University of Technology and Economics | |
local.description.embargo | 2037-12-31 | |
local.bibliographicCitation.issue | 6 | |
local.bibliographicCitation.startpage | 820 | |
local.bibliographicCitation.lastpage | 837 | |
local.identifier.doi | 10.1109/TFUZZ.2004.836085 | |
local.identifier.absseo | 890399 - Information Services not elsewhere classified | |
dc.date.updated | 2015-12-11T11:14:44Z | |
local.identifier.scopusID | 2-s2.0-10944267254 | |
Collections | ANU Research Publications |
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