Neural Network for the identification of a functional dependence using data preselection

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Neural Network for the identification of a functional dependence using data preselection

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Neural Network for the identification of a functional dependence using data preselection

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20204 - Robotics and automatic control

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Neural Network World

ISSN

1210-0552

e-ISSN

2336-4335

Svazek periodika

2021

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

CZ - Česká republika

Počet stran výsledku

16

Strana od-do

109-124

Kód UT WoS článku

000670425000002

EID výsledku v databázi Scopus

2-s2.0-85119446906