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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

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • 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

  • UT code for WoS article

    000615983200020

  • EID of the result in the Scopus database

    2-s2.0-85099818887