Development of unified approaches to building neural network and mathematical models based on digital data

The paper considers the problem of developing approaches to building mathematical models based on digital data of real objects. The data are in text format and contains information about the behavior of the dynamic system. The information selected from the text data enables building of neural network and mathematical models of the dynamic system. The adequacy of the models is evaluated by analytical and numerical methods. The results are meaningfully interpreted. As a result of the study, it was confirmed that the algorithms and approaches for building mathematical models to solve the considered range of problems using digital data can be unified. The analysis of the obtained solutions showed that the conclusions drawn on the basis of the built mathematical models and the conclusions drawn with the semantic neural network analysis of texts are consistent with each other. Therefore, one can talk about the positive results of the models developed. The models developed can be used in solving managerial tasks, planning and situation prediction. © 2020 ASSA.

Authors
Gabdrakhmanova N. 1 , Pilgun M.2
Publisher
International Institute for General Systems Studies
Number of issue
4
Language
English
Pages
113-124
Status
Published
Volume
20
Year
2020
Organizations
  • 1 Peoples Friendship University of Russia (RUDN University), Moscow, Russian Federation
  • 2 Institute of Linguistics, Russian Academy of Sciences, Moscow, Russian Federation
Keywords
Differential equations; Dynamic system; Neural network; Stability; Text analysis; Time series
Date of creation
20.04.2021
Date of change
20.04.2021
Short link
https://repository.rudn.ru/en/records/article/record/72759/
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