Neural intuitionistic fuzzy system with justified granularity
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216275%3A25410%2F22%3A39919466" target="_blank" >RIV/00216275:25410/22:39919466 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/article/10.1007/s00521-022-07504-x" target="_blank" >https://link.springer.com/article/10.1007/s00521-022-07504-x</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s00521-022-07504-x" target="_blank" >10.1007/s00521-022-07504-x</a>
Alternative languages
Result language
angličtina
Original language name
Neural intuitionistic fuzzy system with justified granularity
Original language description
Fuzzy systems are intensively investigated and extended to construct forecasting models. In particular, intuitionistic fuzzy sets are used to capture higher levels of uncertainty occurring in the modeled data. Neural networks are also used to reflect nonlinearity relationships frequently observed in time series. This paper proposes a new hybrid system merging fuzzy system with neural networks and an advanced optimization technique, the principle of justified granularity. Using this technique, we construct an innovative time-series forecasting model. In the experimental part of the paper, we demonstrate the advantages arising from applying the proposed approach to metal price forecasting. Finally, we provide evidence that the proposed model is competitive with the current state-of-the-art models for the forecasting horizons of one and five days.
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
<a href="/en/project/GA19-15498S" target="_blank" >GA19-15498S: Modelling emotions in verbal and nonverbal managerial communication to predict corporate financial risk</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Neural Computing and Applications
ISSN
0941-0643
e-ISSN
1433-3058
Volume of the periodical
34
Issue of the periodical within the volume
Neuveden
Country of publishing house
US - UNITED STATES
Number of pages
17
Pages from-to
19423-19439
UT code for WoS article
000820553800004
EID of the result in the Scopus database
2-s2.0-85133455875