Application of fuzzy neural networks for defining crystal lattice types in nanoscale images
O.P. Soldatova, I.A. Lyozin, I.V. Lyozina, A.V. Kupriyanov, D.V. Kirsh

 

Samara State Aerospace University, Samara, Russia,

Image Processing Systems Institute, Russian Academy of Sciences, Samara, Russia

Full text of article: Russian language.

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Abstract:
The article proposes the application of neural fuzzy networks for defining the overlapping classes of crystal lattices. We discuss the following neural fuzzy networks: Takagi-Sugeno-Kung network and a modification of Wang-Mendel neural fuzzy network proposed by the authors. A three-step scheme of neural network training is proposed. The results prove the efficiency of the proposed approach for the determination of crystal lattice types.

Keywords:
pattern recognition, nanoscale images, nanostructures, crystal lattice, neural fuzzy networks, Takagi-Sugeno-Kung network, Wang-Mendel network.

Citation:
Soldatova OP, Lyozin IA, Lyozina IV, Kupriyanov AV, Kirsh DV. Application of fuzzy neural networks for defining crystal lattice types in nanoscale images. Computer Optics: 2015; 39(5): 787-94. DOI: 10.18287/0134-2452-2015-39-5-787-94.

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