Kirsh,D., Kupriyanov,A., Soldatova,O., Lyozin,I., Lyozina,I.
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 two types of fuzzy neural networks: the Takagi-Sugeno-Kang model and Mamdani-Zadeh model (a modification of the Wang-Mendel fuzzy neural network). 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.