Training Set Fuzzification Based on Histogram to Increase the Performance of a Neural Network
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Training Set Fuzzification Based on Histogram to Increase the Performance of a Neural Network
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10200 - Computer and information sciences
Result continuities
Project
<a href="/en/project/TL02000313" target="_blank" >TL02000313: Intelligent neuro-rehabilitation system for patients with acquired brain damage in early stages of treatment</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
APPL MATH COMPUT
ISSN
0096-3003
e-ISSN
1873-5649
Volume of the periodical
398
Issue of the periodical within the volume
125994
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
12
Pages from-to
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UT code for WoS article
000615983200020
EID of the result in the Scopus database
2-s2.0-85099818887