Training Set Fuzzification Based on Histogram to Increase the Performance of a Neural Network

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17310%2F21%3AA22026UW" target="_blank" >RIV/61988987:17310/21:A22026UW - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0096300321000424" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0096300321000424</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s00521-021-06251-9" target="_blank" >10.1007/s00521-021-06251-9</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Training Set Fuzzification Based on Histogram to Increase the Performance of a Neural Network

Popis výsledku v původním jazyce

This article describes a new approach which uses a histogram to fuzzify variables. We used a linguistic expression to form a training set output vector. The whole fuzzification process of the training set output vector is described in detail. This proposed method was verified on a real data set. We found out that the adaptation of a neural network by fuzzified output vectors has a considerably lower prediction error rate compared with another one without such transformation. Another advantage of the fuzzification approach is that only one neural network can be used for more various data sets with a high range of data attributes (units, thousands, millions). The proposed improvements increase the performance of neural networks, which is presented in the final part.

Název v anglickém jazyce

Training Set Fuzzification Based on Histogram to Increase the Performance of a Neural Network

Popis výsledku anglicky

This article describes a new approach which uses a histogram to fuzzify variables. We used a linguistic expression to form a training set output vector. The whole fuzzification process of the training set output vector is described in detail. This proposed method was verified on a real data set. We found out that the adaptation of a neural network by fuzzified output vectors has a considerably lower prediction error rate compared with another one without such transformation. Another advantage of the fuzzification approach is that only one neural network can be used for more various data sets with a high range of data attributes (units, thousands, millions). The proposed improvements increase the performance of neural networks, which is presented in the final part.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

<a href="/cs/project/TL02000313" target="_blank" >TL02000313: Chytrý neuro-rehabilitační systém pro pacienty se získaným poškozením mozku v časných stádiích léčby</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název periodika

APPL MATH COMPUT

ISSN

0096-3003

e-ISSN

1873-5649

Svazek periodika

398

Číslo periodika v rámci svazku

125994

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

12

Strana od-do

—

Kód UT WoS článku

000615983200020

EID výsledku v databázi Scopus

2-s2.0-85099818887