Advanced approach to numerical forecasting using artificial neural networks

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F09%3APU85803" target="_blank" >RIV/00216305:26210/09:PU85803 - isvavai.cz</a>

Alternative codes found

RIV/62156489:43110/09:00142680

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Advanced approach to numerical forecasting using artificial neural networks

Original language description

Current global market is driven by many factors, such as the information age, the time and amount of information distributed by many data channels it is practically impossible analyze all kinds of incoming information flows and transform them to data with classical methods. New requirements could be met by using other methods. Once trained on patterns artificial neural networks can be used for forecasting and they are able to work with extremely big data sets in reasonable time. The patterns used for learning process are samples of past data. This paper uses Radial Basis Functions neural network in comparison with Multi Layer Perceptron network with Back-propagation learning algorithm on prediction task. The task works with simplified numerical time series and includes forty observations with prediction for next five observations. The main topic of the article is the identification of the main differences between used neural networks architectures together with numerical forecasting. D

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

JD - Use of computers, robotics and its application

OECD FORD branch

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Project

<a href="/en/project/GA102%2F07%2F1503" target="_blank" >GA102/07/1503: Advanced Optimizing the Design of Communication Systems via neural networks</a><br>

Continuities

Z - Vyzkumny zamer (s odkazem do CEZ)

Publication year

2009

Confidentiality

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

Name of the periodical

Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis

ISSN

1211-8516

e-ISSN

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Volume of the periodical

2009

Issue of the periodical within the volume

6

Country of publishing house

CZ - CZECH REPUBLIC

Number of pages

8

Pages from-to

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UT code for WoS article

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EID of the result in the Scopus database

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