Diffusion Modelling Topographic Error of SOM Under Control
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F22%3A00359863" target="_blank" >RIV/68407700:21340/22:00359863 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/s11063-021-10660-1" target="_blank" >https://doi.org/10.1007/s11063-021-10660-1</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s11063-021-10660-1" target="_blank" >10.1007/s11063-021-10660-1</a>
Alternative languages
Result language
angličtina
Original language name
Diffusion Modelling Topographic Error of SOM Under Control
Original language description
The traditional self-organized map (SOM) is learned by Kohonen learning and the most common 2-dimensional grids defining the structure of the map are the hexagonal grid and the rectangular grid. A novel model of self-organization is based on hexagonal grid and diffusion modeling in continuous space which is a good approximation of endorphins propagation and nitric oxide generation in the real brain. Therefore the structure of the system is described by neuron coordinates instead of neighborhood relationships in traditional SOM. The discussed neuron activation using the diffusion process and novel diffusive learning algorithm is based on this activation mentioned above. The novel structure and algorithm are demonstrated on simple examples and real economic applications.
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/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Research Center for Informatics</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
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 Processing Letters
ISSN
1370-4621
e-ISSN
1573-773X
Volume of the periodical
54
Issue of the periodical within the volume
2
Country of publishing house
CH - SWITZERLAND
Number of pages
18
Pages from-to
835-852
UT code for WoS article
000713078000001
EID of the result in the Scopus database
2-s2.0-85118305719