Title: Structural identification of crystal lattices based on fuzzy neural network approach
Authors: Kirsh, Dmitriy
Kupriyanov, Alexandr
Paringer, Rustam
Citation: WSCG '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.
Publisher: Václav Skala - UNION Agency
Document type: konferenční příspěvek
conferenceObject
URI: wscg.zcu.cz/WSCG2018/!!_CSRN-2802.pdf
http://hdl.handle.net/11025/34671
ISBN: 978-80-86943-41-1
Keywords: krystalové mřížky;fuzzy neuronové sítě;identifikace krystalové struktury;mřížkový systém;buňka;neuronová síť typu Takagi-Sugeno-Kang;neuronová síť typu Wang-Mendel
Keywords in different language: crystal lattice;fuzzy neural networks;crystal structure identification;lattice system;unit cell;Takagi-Sugeno-Kang neural network;Wang-Mendel neural network
Abstract: Each 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.
Rights: © Václav Skala - UNION Agency
Appears in Collections:WSCG '2018: Short Papers Proceedings

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