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Extrémní učící se stroje pro předpovídání časových řad / Extreme learning machines for time series prediction

Thesis is aimed at the possibility of utilization of extreme learning machines and echo state networks for time series forecasting with possibility of utilizing GPU acceleration. Such predictions are part of nearly everyone’s daily lives through utilization in weather forecasting, prediction of regular and stock market, power consumption predictions and many more. Thesis is meant to familiarize reader firstly with theoretical basis of extreme learning machines and echo state networks, taking advantage of randomly generating majority of neural networks parameters and avoiding iterative processes. Secondly thesis demonstrates use of programing tools, such as ND4J and CUDA toolkit, to create very own programs. Finally, prediction capability and convenience of GPU acceleration is tested.

Identiferoai:union.ndltd.org:nusl.cz/oai:invenio.nusl.cz:376967
Date January 2018
CreatorsZmeškal, Jiří
ContributorsRajnoha, Martin, Burget, Radim
PublisherVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií
Source SetsCzech ETDs
LanguageCzech
Detected LanguageEnglish
Typeinfo:eu-repo/semantics/masterThesis
Rightsinfo:eu-repo/semantics/restrictedAccess

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