A hybrid cascade neuro-fuzzy network with pools of extended neo-fuzzy neurons and its deep learning

• Autorzy: Bodyanskiy Yevgeniy V. , Tyshchenko Oleksii K.

Identyfikatory

DOI

10.2478/amcs-2019-0035

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

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

PL

strumień danych; funkcja przynależności; neuronowo rozmyty układ adaptacyjny

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2019
• Tom: Vol. 29, no. 3
• Strony: 477--488
• Opis fizyczny: Bibliogr. 35 poz., rys., tab., wykr.

Twórcy

autor

Bodyanskiy Yevgeniy V.

• Control Systems Research Laboratory, Kharkiv National University of Radio Electronics, 14 Nauky Avenue, 61166 Kharkiv, Ukraine

autor

Tyshchenko Oleksii K.

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

Bibliografia

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Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).