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Approximation and prediction of wages based on granular neural network

Result description

This article offers a detailed computational algorithm used in that type of neural networks, extends their applications to fit and predict the data of wages time series, conducts experiments and indicates the gain of granular neural networks, specifically conducting experimentation using the classical (statistical) or econometric methods and conventional/soft RBF neural networks. Results are analysed and opportunities for future research are suggested.

Keywords

granual neural networkcomputational algorithm

The result's identifiers

Alternative languages

  • Result language

    angličtina

  • Original language name

    Approximation and prediction of wages based on granular neural network

  • Original language description

    This article offers a detailed computational algorithm used in that type of neural networks, extends their applications to fit and predict the data of wages time series, conducts experiments and indicates the gain of granular neural networks, specifically conducting experimentation using the classical (statistical) or econometric methods and conventional/soft RBF neural networks. Results are analysed and opportunities for future research are suggested.

  • Czech name

    Aproximace a predikce mezd založené na granulární neuronové síti

  • Czech description

    Článek poskytuje detailní výpočetní algoritmus navrhnutý pro aproximaci a predikci časových řad prostřednictvím granulační RBF sítě. Je poskytnuta popsaná aplikace aproximace a predikce sítě na ekonomické časové řadě. Současně je identifikován přínos vyvinuté metody oproti klasickým statistickým, ekonometrickým modelům a modelům založeným a klasických (perceptronových) sítích. V závěru je načrtnut možný směr dalšího výzkumu v této oblasti.

Classification

  • Type

    D - Article in proceedings

  • CEP classification

    IN - Informatics

  • OECD FORD branch

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2008

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    ROUGH SETS AND KNOWLEDGE TECHNOLOGY

  • ISBN

    978-3-540-79720-3

  • ISSN

    0302-9743

  • e-ISSN

  • Number of pages

    8

  • Pages from-to

  • Publisher name

  • Place of publication

    Chengdu

  • Event location

    Chengdu

  • Event date

    Jan 1, 2008

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article