Fuzzy Neural Networks on Embedded platforms

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F47813059%3A19240%2F17%3AA0000131" target="_blank" >RIV/47813059:19240/17:A0000131 - isvavai.cz</a>

Výsledek na webu

<a href="http://irafm.osu.cz/cjs2017/materials/cjs2017proceedings.pdf" target="_blank" >http://irafm.osu.cz/cjs2017/materials/cjs2017proceedings.pdf</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Fuzzy Neural Networks on Embedded platforms

Popis výsledku v původním jazyce

Fuzzy modeling is the method that describes a behavior of real systems using the fuzzy logic and the fuzzy reasoning. However, in cases when the need for real-time control of the process in embedded systems behavior arises, a standard HW platforms such as personal computers or the ARM platforms are not suitable regarding their limited performance. Additionally, there are many cases in which the conventional approaches fail due to nonlinear system behavior. The afore mentioned is the reason of involving state-of-the-art technologies such as the FPGAs and the Fuzzy Neural Networks into the chain of modeling. The Takagi-Sugeno fuzzy non-linear regression model is also one of the suitable Artificial Intelligence means for fuzzy modeling.

Název v anglickém jazyce

Fuzzy Neural Networks on Embedded platforms

Popis výsledku anglicky

Fuzzy modeling is the method that describes a behavior of real systems using the fuzzy logic and the fuzzy reasoning. However, in cases when the need for real-time control of the process in embedded systems behavior arises, a standard HW platforms such as personal computers or the ARM platforms are not suitable regarding their limited performance. Additionally, there are many cases in which the conventional approaches fail due to nonlinear system behavior. The afore mentioned is the reason of involving state-of-the-art technologies such as the FPGAs and the Fuzzy Neural Networks into the chain of modeling. The Takagi-Sugeno fuzzy non-linear regression model is also one of the suitable Artificial Intelligence means for fuzzy modeling.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20204 - Robotics and automatic control

Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2017

Kód důvěrnosti údajů

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

Název statě ve sborníku

PROCEEDINGS OF THE 20TH CZECH-JAPAN SEMINAR ON DATA ANALYSIS AND DECISION MAKING UNDER UNCERTAINTY

ISBN

9788074649325

ISSN

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e-ISSN

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Počet stran výsledku

9

Strana od-do

25-33

Název nakladatele

University of Ostrava

Místo vydání

Ostrava

Místo konání akce

Pardubice

Datum konání akce

1. 1. 2017

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

000418391500003