<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

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

Kharkiv National University of Radio Electronics

Institute for Research and Applications of Fuzzy Modeling, CE IT4Innovations

International Journal of Applied Mathematics and Computer Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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...

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

Online veröffentlicht: 28. Sept. 2019

Seitenbereich: 477 - 488

Eingereicht: 22. Okt. 2018

Akzeptiert: 03. Juli 2019

DOI: https://doi.org/10.2478/amcs-2019-0035

Schlüsselwörter
data stream, membership function, training procedure, adaptive neuro-fuzzy system, extended neo-fuzzy neuron

© 2019 Yevgeniy V. Bodyanskiy et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

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

Online veröffentlicht: 28. Sept. 2019

Seitenbereich: 477 - 488

Eingereicht: 22. Okt. 2018

Akzeptiert: 03. Juli 2019

DOI: https://doi.org/10.2478/amcs-2019-0035

Schlüsselwörterdata stream, membership function, training procedure, adaptive neuro-fuzzy system, extended neo-fuzzy neuron

© 2019 Yevgeniy V. Bodyanskiy et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Schlüsselwörter
data stream, membership function, training procedure, adaptive neuro-fuzzy system, extended neo-fuzzy neuron