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dc.contributor.authorKirsh, Dmitriy
dc.contributor.authorKupriyanov, Alexandr
dc.contributor.authorParinger, Rustam
dc.contributor.editorSkala, Václav
dc.date.accessioned2019-05-14T12:39:32Z-
dc.date.available2019-05-14T12:39:32Z-
dc.date.issued2018
dc.identifier.citationWSCG '2018: short communications proceedings: The 26th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision 2016 in co-operation with EUROGRAPHICS: University of West Bohemia, Plzen, Czech Republic May 28 - June 1 2018, p. 183-189.en
dc.identifier.isbn978-80-86943-41-1
dc.identifier.uriwscg.zcu.cz/WSCG2018/!!_CSRN-2802.pdf
dc.identifier.urihttp://hdl.handle.net/11025/34671
dc.description.abstractEach crystal nanostructure consists of a set of minimal building blocks (unit cells) which parameters comprehensively describe the location of atoms or atom groups in a crystal. However, structure recognition is greatly complicated by the ambiguity of unit cell choice. To solve the problem, we propose a new approach to structural identification of crystal lattices based on fuzzy neural networks. The paper deals with the Takagi- Sugeno-Kang model of fuzzy neural networks. Moreover, a three-stage neural network learning process is presented: in the first two stages crystal lattices are grouped in non-overlapping classes, and lattices belonging to overlapping classes are recognized at the third stage. The proposed approach to structural identification of crystal lattices has shown promising results in delimiting adjacent lattice types. The structure identification failure rates decreased to 10 % on average.en
dc.format7 s.cs
dc.format.mimetypeapplication/PDF
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencyen
dc.relation.ispartofseriesWSCG '2018: short communications proceedingsen
dc.rights© Václav Skala - UNION Agencycs
dc.subjectkrystalové mřížkycs
dc.subjectfuzzy neuronové sítěcs
dc.subjectidentifikace krystalové strukturycs
dc.subjectmřížkový systémcs
dc.subjectbuňkacs
dc.subjectneuronová síť typu Takagi-Sugeno-Kangcs
dc.subjectneuronová síť typu Wang-Mendelcs
dc.titleStructural identification of crystal lattices based on fuzzy neural network approachen
dc.typekonferenční příspěvekcs
dc.typeconferenceObjecten
dc.rights.accessopenAccessen
dc.type.versionpublishedVersionen
dc.subject.translatedcrystal latticeen
dc.subject.translatedfuzzy neural networksen
dc.subject.translatedcrystal structure identificationen
dc.subject.translatedlattice systemen
dc.subject.translatedunit cellen
dc.subject.translatedTakagi-Sugeno-Kang neural networken
dc.subject.translatedWang-Mendel neural networken
dc.identifier.doihttps://doi.org/10.24132/CSRN.2018.2802.23
dc.type.statusPeer-revieweden
Appears in Collections:WSCG '2018: Short Papers Proceedings

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