Knowledge Discovery in Dynamic Data Using Neural Networks

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17310%2F15%3AA1601ESJ" target="_blank" >RIV/61988987:17310/15:A1601ESJ - 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

Knowledge Discovery in Dynamic Data Using Neural Networks

Popis výsledku v původním jazyce

The paper proposes a new approach to implement common neural network algorithms in the network environment. In our experimental study we have used three different types of neural networks based on Hebb, daline and backpropagation training rules. Our goalwas to discover important market (Forex) patterns which repeatedly appear in the market history. Developed classifiers based upon neural networks should effectively look for the key characteristics of the patterns in dynamic data. We focus on reliability of recognition made by the described algorithms with optimized training patterns based on the reduction of the calculation costs. To interpret the data from the analysis we created a basic trading system and trade all recommendations provided by the neural network.

Název v anglickém jazyce

Knowledge Discovery in Dynamic Data Using Neural Networks

Popis výsledku anglicky

The paper proposes a new approach to implement common neural network algorithms in the network environment. In our experimental study we have used three different types of neural networks based on Hebb, daline and backpropagation training rules. Our goalwas to discover important market (Forex) patterns which repeatedly appear in the market history. Developed classifiers based upon neural networks should effectively look for the key characteristics of the patterns in dynamic data. We focus on reliability of recognition made by the described algorithms with optimized training patterns based on the reduction of the calculation costs. To interpret the data from the analysis we created a basic trading system and trade all recommendations provided by the neural network.

Druh

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

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

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Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název periodika

Cluster Computing-The Journal of Networks Software Tools and Applications

ISSN

1386-7857

e-ISSN

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Svazek periodika

18

Číslo periodika v rámci svazku

4

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

11

Strana od-do

1411-1421

Kód UT WoS článku

000365236800008

EID výsledku v databázi Scopus

2-s2.0-84944534720