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

  • Yevgeniy V. Bodyanskiy
  • Oleksii K. TyshchenkoEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (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.

Keywords

Training procedure Data stream Computational intelligence Adaptive neuro-fuzzy system Extended neo-fuzzy neuron Membership function 

Notes

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).

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Control Systems Research LaboratoryKharkiv National University of Radio ElectronicsKharkivUkraine
  2. 2.Institute for Research and Applications of Fuzzy Modeling, CE IT4InnovationsUniversity of OstravaOstravaCzech Republic

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