Uncertainty Propagation in Fuzzy Grey Cognitive Maps With Hebbian-Like Learning Algorithms
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62690094%3A18450%2F19%3A50016426" target="_blank" >RIV/62690094:18450/19:50016426 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/8115260" target="_blank" >https://ieeexplore.ieee.org/document/8115260</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/TCYB.2017.2771387" target="_blank" >10.1109/TCYB.2017.2771387</a>
Alternative languages
Result language
angličtina
Original language name
Uncertainty Propagation in Fuzzy Grey Cognitive Maps With Hebbian-Like Learning Algorithms
Original language description
This paper is focused on an innovative fuzzy cognitive maps extension called fuzzy grey cognitive maps (FGCMs). FGCMs are a mixture of fuzzy cognitive maps and grey systems theory. These have become a useful framework for facing problems with high uncertainty, under discrete small and incomplete datasets. This paper deals with the problem of uncertainty propagation in FGCM dynamics with Hebbian learning. In addition, this paper applies differential Hebbian learning (DHL) and balanced DHL to FGCMs for the first time. We analyze the uncertainty propagation in eight different scenarios in a classical chemical control problem. The results give insight into the propagation of the uncertainty or greyness in the iterations of the FGCMs. The results show that the nonlinear Hebbian learning is the choice with less uncertainty in steady final grey states for Hebbian learning algorithms.
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
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
IEEE TRANSACTIONS ON CYBERNETICS
ISSN
2168-2267
e-ISSN
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Volume of the periodical
49
Issue of the periodical within the volume
1
Country of publishing house
US - UNITED STATES
Number of pages
10
Pages from-to
211-220
UT code for WoS article
000454242300017
EID of the result in the Scopus database
2-s2.0-85035797845