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Neural Network Learning Algorithms Comparison on Numerical Prediction of Real Data

The result's identifiers

  • 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

  • DOI - Digital Object Identifier

Alternative languages

  • 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

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    IN - Informatics

  • OECD FORD branch

Result continuities

  • Project

  • Continuities

    Z - Vyzkumny zamer (s odkazem do CEZ)

Others

  • Publication year

    2010

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    MENDEL 2010, 16th International Conference on Soft Computing

  • ISBN

  • ISSN

    1803-3814

  • e-ISSN

  • Number of pages

    6

  • Pages from-to

  • 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