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Design, Simulation, Manufacturing: The Innovation Exchange

DSMIE 2019: Advances in Design, Simulation and Manufacturing II pp 311–320Cite as

Forecasting of Overloading Volumes in Transport Systems Based on the Fuzzy-Neural Model

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Part of the book series: Lecture Notes in Mechanical Engineering ((LNME))

Abstract

The article deals with the expediency of evolutionary models application for obtaining the forecast carried out with the minimal error. In research the analysis of modern approaches to the creation of qualitative forecasting models of overloading volumes of cargo in ports with the use of modern methods was carried out. The relevance of using of such network as ANFIS for forecasting of future delivery volumes of grain to the port is proved by calculation method. Conclusion about the best forecast by means of the model by ANFIS is executed by on the comparison with the results of an ARX system. Use of the last type gives bigger error than the fuzzy-neural model. In research, the preprocessing of the entering data was carried out. This information is presented in the form as time series, which contain 1095 values. The selection procedure of allowed to adjust basic data in terms of the informative ability of each value in time series. The number of the actual input parameters (nodes) in the model is decreased from 7 to 4 after the results of the selection. At the same time, a forecasting error on a control sample made up 4.99%.

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Author information

Authors and Affiliations

  1. Kharkiv Petro Vasylenko National Technical University of Agriculture, 44 Alchevskyh St., Kharkiv, 61002, Ukraine

    Natalya Shramenko & Dmitriy Muzylyov

  2. Ukrainian State University of Railway Transport, 7 Feierbakh Sq., Kharkiv, 61050, Ukraine

    Natalya Shramenko

Authors
  1. Natalya Shramenko
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  2. Dmitriy Muzylyov
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Corresponding author

Correspondence to Natalya Shramenko .

Editor information

Editors and Affiliations

  1. Sumy State University, Sumy, Ukraine

    Vitalii Ivanov

  2. Poznan University of Technology, Poznań, Poland

    Justyna Trojanowska

  3. University of Minho, Guimaraes, Portugal

    Jose Machado

  4. Sumy State University, Sumy, Ukraine

    Oleksandr Liaposhchenko

  5. Prešov, Slovakia

    Jozef Zajac

  6. Sumy State University, Sumy, Ukraine

    Ivan Pavlenko

  7. University of West Bohemia, Pilsen, Czech Republic

    Milan Edl

  8. University of Zagreb, Zagreb, Croatia

    Dragan Perakovic

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Shramenko, N., Muzylyov, D. (2020). Forecasting of Overloading Volumes in Transport Systems Based on the Fuzzy-Neural Model. In: Ivanov, V., et al. Advances in Design, Simulation and Manufacturing II. DSMIE 2019. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-22365-6_31

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  • DOI: https://doi.org/10.1007/978-3-030-22365-6_31

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-22364-9

  • Online ISBN: 978-3-030-22365-6

  • eBook Packages: EngineeringEngineering (R0)

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