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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17610%2F20%3AA2101U0E" target="_blank" >RIV/61988987:17610/20:A2101U0E - isvavai.cz</a>

Výsledek na webu

<a href="https://www.springer.com/gp/book/9783030180577#" target="_blank" >https://www.springer.com/gp/book/9783030180577#</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-18058-4" target="_blank" >10.1007/978-3-030-18058-4</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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 in 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's always an occasion to set up (increase) an inference order and a quantity of membership relations inside the extended neo-fuzzy neuron.

Název v anglickém jazyce

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

Popis výsledku anglicky

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 in 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's always an occasion to set up (increase) an inference order and a quantity of membership relations inside the extended neo-fuzzy neuron.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10102 - Applied mathematics

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Advances in Intelligent Systems and Computing

ISBN

978-3-030-18057-7

ISSN

2194-5357

e-ISSN

2194-5365

Počet stran výsledku

11

Strana od-do

164-174

Název nakladatele

Springer

Místo vydání

Berlin

Místo konání akce

Cracow, Poland

Datum konání akce

2. 7. 2018

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000493382100013