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A Hybrid Cascade Neural Network with Ensembles of Extended Neo-Fuzzy Neurons and Its Deep Learning

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Information Technology, Systems Research, and Computational Physics (ITSRCP 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 945))

Abstract

This research contribution instantiates a framework of a hybrid cascade neural network rest on the application of a specific sort of neo-fuzzy elements and a new peculiar adaptive training rule. The main trait of the offered system is its competence to continue intensifying its cascades until the required accuracy is gained. A distinctive rapid training procedure is also covered for this case that gives the possibility to operate with nonstationary data streams in an attempt to provide online training of multiple parametric variables. A new training criterion is examined which suits for handling nonstationary objects. Added to everything else, there is always an occasion to set up (increase) an inference order and a quantity of membership relations inside the extended neo-fuzzy neuron.

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Acknowledgment

Oleksii K. Tyshchenko is kindly grateful for the financial assistance of the Visegrad Scholarship Program—EaP #51700967 funded by the International Visegrad Fund (IVF).

Author information

Authors and Affiliations

  1. Control Systems Research Laboratory, Kharkiv National University of Radio Electronics, 14 Nauky Ave, Kharkiv, 61166, Ukraine

    Yevgeniy V. Bodyanskiy & Oleksii K. Tyshchenko

  2. Institute for Research and Applications of Fuzzy Modeling, CE IT4Innovations, University of Ostrava, 30. dubna 22, 701 03, Ostrava, Czech Republic

    Oleksii K. Tyshchenko

Authors
  1. Yevgeniy V. Bodyanskiy
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  2. Oleksii K. Tyshchenko
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Corresponding author

Correspondence to Oleksii K. Tyshchenko .

Editor information

Editors and Affiliations

  1. Faculty of Physics and Applied Computer Science, AGH University of Science and Technology, Krakow, Poland

    Piotr Kulczycki

  2. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Janusz Kacprzyk

  3. Faculty of Engineering Sciences, Széchenyi István University, Győr, Hungary

    László T. Kóczy

  4. Faculty of Civil Engineering, Slovak University of Technology, Bratislava, Slovakia

    Radko Mesiar

  5. Department of Electronic Systems, Aalborg University, Aalborg, Denmark

    Rafal Wisniewski

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Bodyanskiy, Y.V., Tyshchenko, O.K. (2020). A Hybrid Cascade Neural Network with Ensembles of Extended Neo-Fuzzy Neurons and Its Deep Learning. In: Kulczycki, P., Kacprzyk, J., Kóczy, L., Mesiar, R., Wisniewski, R. (eds) Information Technology, Systems Research, and Computational Physics. ITSRCP 2018. Advances in Intelligent Systems and Computing, vol 945. Springer, Cham. https://doi.org/10.1007/978-3-030-18058-4_13

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