Dopředné neuronové sítě v katalýze: nástroj pro aproximaci závislosti výtěžnosti na složení katalyzátorů, a pro extrakci znalostí

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F03%3A00103296" target="_blank" >RIV/67985807:_____/03:00103296 - isvavai.cz</a>

Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Feedforward Neural Networks in Catalysis: A Tool for the Approximation of the Dependency of Yield on Catalyst Composition, and for Knowledge Extraction

Popis výsledku v původním jazyce

Artificial neural networks are distributed computing systems implementing the functionality characterizing biological neural networks. This way of computing has become quite successful in practical applications as a tool for solving several traditional mathematical and data-analysis tasks, such as classification, clustering, approximation and prediction. In this paper, main principles of employing multilayer perceptrons for the approximation of unknown functions are outlined, and another possible use ofmultilayer perceptrons in combinatorial catalysis is indicated - their use for the extraction of knowledge from experimental catalytic input and output data. To counterbalance the abstractness of the subject, the method is illustrated by applying multilayer perceptrons to data on catalyst composition and catalytic results in the oxidative dehydrogenation of propane to propene.

Název v anglickém jazyce

Feedforward Neural Networks in Catalysis: A Tool for the Approximation of the Dependency of Yield on Catalyst Composition, and for Knowledge Extraction

Popis výsledku anglicky

Artificial neural networks are distributed computing systems implementing the functionality characterizing biological neural networks. This way of computing has become quite successful in practical applications as a tool for solving several traditional mathematical and data-analysis tasks, such as classification, clustering, approximation and prediction. In this paper, main principles of employing multilayer perceptrons for the approximation of unknown functions are outlined, and another possible use ofmultilayer perceptrons in combinatorial catalysis is indicated - their use for the extraction of knowledge from experimental catalytic input and output data. To counterbalance the abstractness of the subject, the method is illustrated by applying multilayer perceptrons to data on catalyst composition and catalytic results in the oxidative dehydrogenation of propane to propene.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BA - Obecná matematika

OECD FORD obor

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Projekt

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Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2003

Kód důvěrnosti údajů

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

Název periodika

Catalysis Today

ISSN

0920-5861

e-ISSN

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Svazek periodika

81

Číslo periodika v rámci svazku

-

Stát vydavatele periodika

DE - Spolková republika Německo

Počet stran výsledku

10

Strana od-do

485-494

Kód UT WoS článku

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EID výsledku v databázi Scopus

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