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Committee of the Combined RBF-SGTM Neural-Like Structures for Prediction Tasks

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Mobile Web and Intelligent Information Systems (MobiWIS 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11673))

Abstract

The paper describes the committee of non-iterative artificial intelligence tools for solving the regression task. It is based on the use of high-speed neural-like structures with extended inputs. Such an extension involves the combined use of primary inputs and extended inputs, via RBF. The resulting combination of inputs allows increasing the extrapolation properties of each element of the committee. This ensures a decreasing of the prediction errors for the solution of the regression tasks in cases of large volumes of data processing. The developed committee is used to solve the task of prediction of insurance costs. It is experimentally found that the proposed committee decreases training and test errors compared with the use of one neural-like structure of this type. The comparison of the committee’s effectiveness with existing iterative and non-iterative computational intelligence methods has confirmed the highest accuracy of its work with a small increase of the time of the training procedure. The developed committee in software or hardware variants can be used to solve regression and classification tasks in the condition of large volumes of data for different application areas.

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Author information

Authors and Affiliations

  1. Lviv Polytechnic National University, Lviv, Ukraine

    Roman Tkachenko, Ivan Izonin, Pavlo Vitynskyi & Yurii Tsymbal

  2. IT STEP University, Lviv, Ukraine

    Pavlo Tkachenko

  3. University of Vienna, Vienna, Austria

    Natalia Kryvinska

  4. Comenius University in Bratislava, Bratislava, Slovakia

    Natalia Kryvinska

Authors
  1. Roman Tkachenko
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  2. Pavlo Tkachenko
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  3. Ivan Izonin
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  4. Pavlo Vitynskyi
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  5. Natalia Kryvinska
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  6. Yurii Tsymbal
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Corresponding author

Correspondence to Ivan Izonin .

Editor information

Editors and Affiliations

  1. University of Bradford, Bradford, UK

    Irfan Awan

  2. Oxford Brookes University, Oxford, UK

    Muhammad Younas

  3. Teknopark Ankara, Ankara, Turkey

    Perin Ünal

  4. ABB AG Forschungszentrum, Ladenburg, Germany

    Markus Aleksy

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Tkachenko, R., Tkachenko, P., Izonin, I., Vitynskyi, P., Kryvinska, N., Tsymbal, Y. (2019). Committee of the Combined RBF-SGTM Neural-Like Structures for Prediction Tasks. In: Awan, I., Younas, M., Ünal, P., Aleksy, M. (eds) Mobile Web and Intelligent Information Systems. MobiWIS 2019. Lecture Notes in Computer Science(), vol 11673. Springer, Cham. https://doi.org/10.1007/978-3-030-27192-3_21

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  • DOI: https://doi.org/10.1007/978-3-030-27192-3_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-27191-6

  • Online ISBN: 978-3-030-27192-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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