Sparse Fuzzy Systems Generation and Fuzzy Rule Interpolation: A Practical Approach

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dc.contributor.authorChong, A
dc.contributor.authorGedeon, Tamas (Tom)
dc.contributor.authorKovacs, Sz.
dc.contributor.authorKoczy, Laszlo T.
dc.coverage.spatialSt. Louis USA
dc.date.accessioned2015-12-08T22:25:36Z
dc.date.createdMay 25-28 2003
dc.date.issued2003
dc.date.updated2015-12-08T09:07:17Z
dc.description.abstractIn this paper, we explore the use of a sparse fuzzy system generation technique in conjunction with simple projection-based fuzzy rule interpolation, to generate sparse fuzzy systems with relatively few rules whilst still achieving reasonable system accuracy. Through setting a parameter value, the user is able to control, to some extent, the number of rules generated by the rule extraction technique. The rule interpolation approach enables the sparse fuzzy system to maintain a reasonable accuracy. The effectiveness of this approach is validated experimentally.
dc.identifier.isbn0780378105
dc.identifier.urihttp://hdl.handle.net/1885/33507
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE Inc)
dc.relation.ispartofseriesIEEE International Conference on Fuzzy Systems (FUZZ-IEEE 2003)
dc.sourceProceedings of the 12th International Conference on Fuzzy Systems, 2003. FUZZ '03
dc.subjectKeywords: Extrapolation; Fuzzy sets; Interpolation; Sparse fuzzy systems; Fuzzy control
dc.titleSparse Fuzzy Systems Generation and Fuzzy Rule Interpolation: A Practical Approach
dc.typeConference paper
local.bibliographicCitation.lastpage499
local.bibliographicCitation.startpage494
local.contributor.affiliationChong, A, Murdoch University
local.contributor.affiliationGedeon, Tamas (Tom), College of Engineering and Computer Science, ANU
local.contributor.affiliationKovacs, Sz., University of Miskolc
local.contributor.affiliationKoczy, Laszlo T., Budapest University of Technology and Economics
local.contributor.authoruidGedeon, Tamas (Tom), u4088783
local.description.embargo2037-12-31
local.description.notesImported from ARIES
local.description.refereedYes
local.identifier.absfor080108 - Neural, Evolutionary and Fuzzy Computation
local.identifier.ariespublicationU3594520xPUB103
local.identifier.doi10.1109/FUZZ.2003.1209413
local.identifier.scopusID2-s2.0-0038537483
local.type.statusPublished Version

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