Neural Network Learning Algorithms Comparison on Numerical Prediction of Real Data

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F62156489%3A43110%2F10%3A00169903" target="_blank" >RIV/62156489:43110/10:00169903 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Neural Network Learning Algorithms Comparison on Numerical Prediction of Real Data
Original language description
In this paper we concentrate on prediction of future values based on the past course of a variable, Traditionally this task is solved using statistical analysis - first a time-series model is constructed and then statistical prediction algorithms are applied to it in order to obtain future values. This paper describes two learning algorithms for training Multi-layer perceptron networks, widely known Back propagation learning algorithm and Levenberg-Marquardt algorithm. Both of these methods are appliedto solve prediction of real numerical time series represented by Czech household consumption expenditures. Tested dataset includes twenty-eight observations between the years 2001 and 2007. The observations are represented by quarterly data and the goalis to predict three future values for first three quarters of 2008. Predicted values of both experiments are compared with measured values. In the next step, a comparison of neural network topology efficiency regarding to learning algorit
Czech name
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Czech description
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Publication year
2010
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
MENDEL 2010, 16th International Conference on Soft Computing
ISBN
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ISSN
1803-3814
e-ISSN
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Number of pages
6
Pages from-to
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Publisher name
Brno University of Technology
Place of publication
Brno
Event location
Brno
Event date
Jan 1, 2010
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
288144100043

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