A Hybrid Cascade Neuro–Fuzzy Network with Pools of Extended Neo–Fuzzy Neurons and its Deep Learning

Yevgeniy V. Bodyanskiy; Oleksii K. Tyshchenko

doi:10.2478/amcs-2019-0035

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A Hybrid Cascade Neuro–Fuzzy Network with Pools of Extended Neo–Fuzzy Neurons and its Deep Learning

International Journal of Applied Mathematics and Computer Science

Volume 29 (2019): Issue 3 (September 2019)

By: Yevgeniy V. Bodyanskiy and Oleksii K. Tyshchenko

Open Access

|Sep 2019

Abstract

This research contribution instantiates a framework of a hybrid cascade neural network based 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 offers the possibility to operate with non-stationary data streams in an attempt to provide online training of multiple parametric variables. A new training criterion is examined for handling non-stationary objects. Additionally, there is always an occasion to set up (increase) the inference order and the number of membership relations inside the extended neo-fuzzy neuron.

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Articles in this issue

DOI: https://doi.org/10.2478/amcs-2019-0035 | Journal eISSN: 2083-8492 | Journal ISSN: 1641-876X

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Language: English

Page range: 477 - 488

Submitted on: Oct 22, 2018

Accepted on: Jul 3, 2019

Published on: Sep 28, 2019

Published by: University of Zielona Góra

In partnership with: Paradigm Publishing Services

Publication frequency: 4 issues per year

Keywords:

data stream,

membership function,

training procedure,

adaptive neuro-fuzzy system,

extended neo-fuzzy neuron

Related subjects:

Mathematics,

Applied mathematics

© 2019 Yevgeniy V. Bodyanskiy, Oleksii K. Tyshchenko, published by University of Zielona Góra
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Volume 29 (2019): Issue 3 (September 2019)