Application of fuzzy neural networks for defining crystal lattice types in nanoscale images
Автор: Soldatova Olga Petrovna, Lyozin Ilya Alexandrovich, Lyozina Irina Viktorovna, Kupriyanov Alexander Victorovich, Kirsh Dmitriy Victorovich
Журнал: Компьютерная оптика @computer-optics
Рубрика: Обработка изображений: Восстановление изображений, выявление признаков, распознавание образов
Статья в выпуске: 5 т.39, 2015 года.
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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.
Pattern recognition, nanoscale images, nanostructures, crystal lattice, neural fuzzy networks, takagi-sugeno-kung network, wang-mendel network
Короткий адрес: https://sciup.org/14059424
IDR: 14059424 | DOI: 10.18287/0134-2452-2015-39-5-787-794
