Neural Network for the identification of a functional dependence using data preselection
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21220%2F21%3A00352319" target="_blank" >RIV/68407700:21220/21:00352319 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.14311/NNW.2021.31.006" target="_blank" >https://doi.org/10.14311/NNW.2021.31.006</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.14311/NNW.2021.31.006" target="_blank" >10.14311/NNW.2021.31.006</a>
Alternative languages
Result language
angličtina
Original language name
Neural Network for the identification of a functional dependence using data preselection
Original language description
A neural network can be used in the identification of a given functional dependency. An undetermined problem (with more degrees of freedom) has to be converted to a determined one by adding other conditions. This is easy for a well-defined problem, described by a theoretical functional dependency; in this case, no identification (using a neural network) is necessary. The article describes how to apply a fitness (or a penalty) function directly to the data, before a neural network is trained. As a result, the trained neural network is near to the best possible solution according to the selected fitness function. In comparison to implementing the fitness function during the training of the neural network, the method described here is simpler and more reliable. The new method is demonstrated on the kinematics control of a redundant 2D manipulator.
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
20204 - Robotics and automatic control
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
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
Neural Network World
ISSN
1210-0552
e-ISSN
2336-4335
Volume of the periodical
2021
Issue of the periodical within the volume
2
Country of publishing house
CZ - CZECH REPUBLIC
Number of pages
16
Pages from-to
109-124
UT code for WoS article
000670425000002
EID of the result in the Scopus database
2-s2.0-85119446906