A Hybrid Cascade Neuro-Fuzzy Network with Pools 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%2F19%3AA2001WKD" target="_blank" >RIV/61988987:17610/19:A2001WKD - isvavai.cz</a>

Výsledek na webu

<a href="https://www.amcs.uz.zgora.pl/?action=paper&paper=1505" target="_blank" >https://www.amcs.uz.zgora.pl/?action=paper&paper=1505</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.2478/amcs-2019-0035" target="_blank" >10.2478/amcs-2019-0035</a>

Jazyk výsledku

angličtina

Název v původním jazyce

A Hybrid Cascade Neuro-Fuzzy Network with Pools 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 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’s always an occasion to set up (increase) an inference order and a number of membership relations inside the extended neo-fuzzy neuron.

Název v anglickém jazyce

A Hybrid Cascade Neuro-Fuzzy Network with Pools 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 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’s always an occasion to set up (increase) an inference order and a number of membership relations inside the extended neo-fuzzy neuron.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10102 - Applied mathematics

Projekt

—

Návaznosti

V - Vyzkumna aktivita podporovana z jinych verejnych zdroju

Rok uplatnění

2019

Kód důvěrnosti údajů

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

Název periodika

International Journal of Applied Mathematics and Computer Science

ISSN

1641-876X

e-ISSN

2083-8492

Svazek periodika

29

Číslo periodika v rámci svazku

3

Stát vydavatele periodika

PL - Polská republika

Počet stran výsledku

12

Strana od-do

477-488

Kód UT WoS článku

000488598300005

EID výsledku v databázi Scopus

2-s2.0-85073331969