Germashev I.V., Derbisher E.V., Derbisher V.E., Markushevskaya E.A. The Neural Network Analysis of Colored Graphs

http://dx.doi.org/10.15688/jvolsu1.2016.2.3

Ilya Vasilyevich Germashev
Doctor of Technical Sciences,
Professor, Department of Fundamental Informatics and Optimal Control,
Volgograd State University
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Prosp. Universitetskу, 100, 400062 Volgograd, Russian Federation

Evgeniya Vyacheslavovna Derbisher
Candidate of Technical Sciences,
Associate Professor, Department of Analytical, Physical Chemistry and Physico-Chemistry of Polymers,
Volgograd State Technical University
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Prosp. Lenina, 28, 400005 Volgograd, Russian Federation

Vyacheslav Evgenyevich Derbisher
Doctor of Chemical Sciences,
Professor, Department of High-Molecular and Fibrous Materials Technology,
Volgograd State Technical University
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Prosp. Lenina, 28, 400005 Volgograd, Russian Federation

Elena Aleksandrovna Markushevskaya
Candidate of Pedagogical Sciences,
Associate Professor, Department of Pedagogy and Psychology of Primary Education,
Volgograd State Social-Pedagogical University
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Prosp. Lenina, 27, 400005 Volgograd, Russian Federation

Abstract. The article deals with the problem of colored graph identification. This problem arises when solving tasks in the subject area which is formalized in terms of graph theory including cases connected with the investigation of “chemical structure – property” dependence. The authors propose the model of chemical structure in the form of a colored graph. The upper bound for the algorithm complexity is obtained, and its feasibility is shown. The learning samples are represented by graphs with a given property. The problem solved in the paper is the method development and analysis allowing to identify the property of graph that is not included in the learning samples. To solve the problem it is proposed to use the mechanism of artificial neural network of the original structure, the principles of which differ significantly from generally accepted, which is in the signals form and signals distribution ways across the network. The graph analysis is based on simple chains statistics for which the breadth-first search algorithm is described, and the algorithm analysis is given. The proposed algorithm allows also to handle disconnected graphs and thus to analyze multi-component systems. The article presents the formal result of its training as a formula allowing to calculate the output signal for input signals vector. The use of artificial neural network for graphs identification is demonstrated. The obtained results represent mathematical software which enables creating a reasonable decision rules for a systems identification formalized in graph theory terms.

Key words: identification, simple chain, statistics, breadth-first search, algorithm analysis, artificial neural network training.

Creative Commons License

The Neural Network Analysis of Colored Graphs by Germashev I.V., Derbisher E.V., Derbisher V.E., Markushevskaya E.A. is licensed under a Creative Commons Attribution 4.0 International License.

Citation in EnglishScience Journal of Volgograd State University. Mathematics. Physics. №2 (33) 2016 pp. 27-35

 

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