Neural Network Learning Algorithms Comparison on Numerical Prediction of Real Data

Kód výsledku v IS VaVaI

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Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Neural Network Learning Algorithms Comparison on Numerical Prediction of Real Data

Popis výsledku v původním jazyce

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

Název v anglickém jazyce

Neural Network Learning Algorithms Comparison on Numerical Prediction of Real Data

Popis výsledku anglicky

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

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

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Návaznosti

Z - Vyzkumny zamer (s odkazem do CEZ)

Rok uplatnění

2010

Kód důvěrnosti údajů

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

Název statě ve sborníku

MENDEL 2010, 16th International Conference on Soft Computing

ISBN

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ISSN

1803-3814

e-ISSN

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Počet stran výsledku

6

Strana od-do

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Název nakladatele

Brno University of Technology

Místo vydání

Brno

Místo konání akce

Brno

Datum konání akce

1. 1. 2010

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

288144100043