Research and development of synthesis technologies of transport enterprise multi-control neural network algorithms

Authors

  • Денис Юрьевич Зубенко O. M. Beketov National University of Urban Economy in Kharkiv, Str. Revolutsii, 12, Kharkiv, Ukraina, 61002, Ukraine https://orcid.org/0000-0002-6736-7849
  • В’ячеслав Михайлович Шавкун O. M. Beketov National University of Urban Economy in Kharkiv, Str. Revolutsii, 12, Kharkiv, Ukraina, 61002, Ukraine https://orcid.org/0000-0002-3253-1282
  • Владислав Ігоревич Скурихін O. M. Beketov National University of Urban Economy in Kharkiv, Str. Revolutsii, 12, Kharkiv, Ukraina, 61002, Ukraine https://orcid.org/0000-0003-2691-6167
  • Олександр Вадимович Донець O. M. Beketov National University of Urban Economy in Kharkiv, Str. Revolutsii, 12, Kharkiv, Ukraina, 61002, Ukraine https://orcid.org/0000-0002-2809-7460
  • Наталя Павлівна Лукашова O. M. Beketov National University of Urban Economy in Kharkiv, Str. Revolutsii, 12, Kharkiv, Ukraina, 61002, Ukraine https://orcid.org/0000-0003-0913-8711

DOI:

https://doi.org/10.15587/2312-8372.2016.71973

Keywords:

intelligent system, dynamically variable objects, transport enterprises

Abstract

Currently, the problem of designing automatic control systems of dynamically variable objects is characterized by the transition from adaptive management paradigm to intelligent control paradigm. This is caused by continuous complication of objects and conditions of their operation, the advent of new classes of computing devices (distributed computing), high-performance telecommunication channels, and a sharp increase in the requirements for reliability and efficiency of control processes in a significant priori and posteriori uncertainty. Accounting for these factors is possible only through the transition from «hard» algorithms of parametric and structural adaptation to the anthropomorphic principle of forming control.

Given the characteristics of the modern enterprise, when the head and structural units quickly make decisions and monitor its implementation, it comes very clearly understand the need of artificial intelligence as an assistant in the work of transport enterprise. However, existing methods are outdated and not fully perform the role of assistant. The latest trends in this matter are modern methods of creating intelligent systems that can learn in the process, based on neural networks.

The paper proposed synthesis technologies of transport enterprise neural network algorithms. Better use of major resources of the enterprise is possible through the use of self-learning neural networks to control transport enterprise. Using a synthesis of known algorithms may be more correct setup of the whole system and increase the speed of information processing and decision of optimal solution.

Author Biographies

Денис Юрьевич Зубенко, O. M. Beketov National University of Urban Economy in Kharkiv, Str. Revolutsii, 12, Kharkiv, Ukraina, 61002

Candidate of Technical Science, Associate Professor

Department of electric transport

В’ячеслав Михайлович Шавкун, O. M. Beketov National University of Urban Economy in Kharkiv, Str. Revolutsii, 12, Kharkiv, Ukraina, 61002

Candidate of Technical Science, Associate Professor

Department of electric transport

Владислав Ігоревич Скурихін, O. M. Beketov National University of Urban Economy in Kharkiv, Str. Revolutsii, 12, Kharkiv, Ukraina, 61002

Candidate of Technical Science, Associate Professor

Department of electric transport

Олександр Вадимович Донець, O. M. Beketov National University of Urban Economy in Kharkiv, Str. Revolutsii, 12, Kharkiv, Ukraina, 61002

Candidate of Technical Science, Associate Professor

Department of electric transport

Наталя Павлівна Лукашова, O. M. Beketov National University of Urban Economy in Kharkiv, Str. Revolutsii, 12, Kharkiv, Ukraina, 61002

Assistant

Department of electric transport

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Published

2016-05-26

How to Cite

Зубенко, Д. Ю., Шавкун, В. М., Скурихін, В. І., Донець, О. В., & Лукашова, Н. П. (2016). Research and development of synthesis technologies of transport enterprise multi-control neural network algorithms. Technology Audit and Production Reserves, 3(1(29), 46–51. https://doi.org/10.15587/2312-8372.2016.71973

Issue

Vol. 3 No. 1(29) (2016): Mathematical modeling. Mechanical engineering and machine building. Information technologies

Section

Information Technologies: Original Research

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Copyright (c) 2016 Денис Юрьевич Зубенко, В’ячеслав Михайлович Шавкун, Владислав Ігоревич Скурихін, Олександр Вадимович Донець, Наталя Павлівна Лукашова

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